Rulemaking and Inscrutable Automated Decision Tools

" Rulemaking and Inscrutable Automated Decision Tools ," 119 Colum. L. Rev. 1851 (2019)

"Rulemaking and Inscrutable Automated Decision Tools," 119 *Colum. L. Rev.* 1851 (2019)

Katherine J. Strandburg, Rulemaking and Inscrutable Automated Decision Tools, 119 Colum. L. Rev. 1851 (2019)

Machine learning models derived from large troves of personal data are increasingly used in making decisions important to peoples' lives. 1 [Vol. 119:1851 These tools have stirred both hopes of improving decisionmaking by avoiding human shortcomings and concerns about their potential to amplify bias and undermine important social values. 2 It is often hard for humans to grasp or explain how or why machine-learning-based models map input features to output predictions because they often combine large numbers of input features in complicated ways. 3 This inherent inscrutability 4 has drawn the attention of data scientists, 5 legal scholars, 6 policymakers, 7 and others 8 to the explainability problem. [Vol. 119:1851 accountability. 11 Talia Gillis and Josh Simons, for example, contrast "[t]he focus on individual, technical explanation . . . driven by an uncritical bent towards transparency" with their argument that "[i]nstitutions should justify their choices about the design and integration of machine learning models not to individuals, but to empowered regulators or other forms of public oversight bodies." 12 Taken together, these threads suggest the view of explanatory flows in decisionmaking illustrated in Figure 1, in which decisionmakers justify their choices by explaining case-by-case outcomes to decision subjects and separately explaining design choices regarding automated decision tools to the public and oversight bodies. 11. For the most part, this emphasis is recent. See, e.g., Doshi-Velez & Kortz, supra note 3, at 3-9 (describing the explanation system's role in public accountability); Hannah Bloch-Wehba, Access to Algorithms, 88 Fordham L. Rev. (forthcoming 2019) (manuscript at 4-9), https://ssrn.com/abstract=3355776 (on file with the Columbia Law Review) ("These features . . . have prompted calls for new mechanisms of transparency and accountability in the age of algorithms."); Robert Brauneis & Ellen P. Goodman, Algorithmic Transparency for the Smart City, 20 Yale J.L. & Tech. 103, 132 (2018) ("Such accountability requires not perfect transparency . . . but . . . meaningful transparency."); Gillis & Simons, supra note 9 (manuscript at 11-12) ("Explanations of machine learning models are certainly not sufficient for many of the most important forms of justification in modern democracies . . . ."); Selbst & Barocas, supra note 4, at 1087 ("[F]aced with a world increasingly dominated by automated decision-making, advocates, policymakers, and legal scholars would call for machines that can explain themselves."); Jennifer Cobbe, Administrative Law and the Machines of Government: Judicial Review of Automated Public-Sector Decision-Making, Legal Stud. (July 9, 2019), https://www.cambridge.org/core/journals/legal-stud-ies/article/administrative-law-and-the-machines-of-government-judicial-review-of-automatedpublicsector-decisionmaking/09CD6B470DE4ADCE3EE8C94B33F46FCD/core-reader (on file with the Columbia Law Review) ("Legal standards and review mechanisms which are primarily concerned with decision-making processes, which examine how decisions were made, cannot easily be applied to opaque, algorithmically-produced decisions."). But, for a truly pathbreaking consideration of these issues, see Danielle Keats Citron, Technological Due Process, 85 Wash. U. L. Rev. 1249, 1258 (2008) ("This technological due process provides new mechanisms to replace the procedural regimes that automation endangers."). 12. Gillis & Simons, supra note 9 (manuscript at 6-12); see also David Lehr & Paul Ohm, Playing with the Data: What Legal Scholars Should Learn About Machine Learning, 51 U.C. Davis L. Rev. 653, 708-09 (2017) (emphasizing the many choices involved in implementing a machine learning model and the different sorts of explanations that could be made). Many real-world decision systems require significantly more complex explanatory flows, however, because decisionmaking responsibility is delegated and distributed across multiple actors to handle large numbers of cases. Delegated, distributed decision systems commonly include agenda setters, who determine the goals and purposes of the systems; rulemakers tasked with translating agenda setters' goals into decision criteria; and adjudicators, who apply those criteria to particular cases. 13 In democracies, the ultimate agenda setter for government decisionmaking is the public, often represented by legislatures and courts. The public also has a role in agenda setting for many private decision systems, such as those related to employment and credit. 14 Figure 2 illustrates the explanatory flows required by a delegated, distributed decision system. 13. The terms "adjudication" and "rulemaking" are borrowed, loosely, from administrative law. See 5 U.S.C. § 551 (2012); see also, e.g., id. § § 553-557. The general paradigm in Figure 2 Delegation and distribution of decisionmaking authority, while often necessary and effective for dealing with agenda setters' limited time and expertise, proliferate explanatory information flows. Delegation, whether from the public or a private agenda setter, creates the potential for principal-agent problems and hence the need for accountability mechanisms. 15 Explanation requirements, including a duty to inform principals of facts that "the principal would wish to have" or "are material to the agent's duties," are basic mechanisms for ensuring that agents are accountable to principals. 16 Distribution of responsibility multiplies these principal-agent concerns, while adding an underappreciated layer of 15. See Kathleen M. Eisenhardt, Agency Theory: An Assessment and Review, 14 Acad. Mgmt. Rev. 57, 61 (1989) ("The agency problem arises because (a) the principal and the agent have different goals and (b) the principal cannot determine if the agent has behaved appropriately."); see also Gillis & Simons, supra note 9 (manuscript at 6-10) (arguing for a principal-agent framework of accountability in considering government use of machine learning). 16. Restatement (Third) of Agency § 8.11 (Am. Law Inst. 2005). Reviewing Authorities Decision Subjects explanatory flows necessary for coordination among decision-system actors. 17 Automated decision tools are particularly attractive to designers of delegated, distributed decision systems because their deployment promises to improve consistency, decrease bias, and lower costs. 18 For example, such tools are being used or considered for decisions involving pretrial detention, 19 sentencing, 20 child welfare, 21 credit, 22 employment, 23 and tax auditing. 24 Unfortunately, the inscrutability of many machinelearning-based decision tools creates barriers to all of the explanatory flows illustrated in Figure 2. 25 Expanding the focus of the explainability debate to include public accountability is thus only one step toward a more realistic view of the ramifications of decision tool inscrutability. Before incorporating machine-learning-based decision tools into a delegated, distributed decision system, agenda setters should have a cleareyed view of what information is feasibly available to all of the system's actors. This would enable them to assess whether that information, combined with other mechanisms, can provide a sufficient level of accountability 26 and coordination to justify the use of a particular automated decision tool in a particular context. 25. See infra section IV.B. 26. See, e.g., Bloch-Wehba, supra note 11 (manuscript at 27-28) (discussing the challenge of determining adequate public disclosure of algorithm-based government decisionmaking); Brauneis & Goodman, supra note 11, at 166-67 ("Governments should consciously generate-or demand that their vendors generate-records that will further public understanding of algorithmic processes."); Citron, supra note 11, at 1305-06 (arguing that mandatory audit trails "would ensure that agencies uniformly provide detailed notice to individuals"); Gillis & Simons, supra note 9 (manuscript at 2) ("Accountability is achieved when an institution must justify its choices about how it developed and implemented its decision-making procedure, including the use of statistical techniques or machine learning, to an individual or institution with meaningful powers of oversight and Incorporating inscrutable automated decision tools has ramifications for all stages of delegated, distributed decisionmaking. This Essay focuses on the implications for the creation of decision criteria---or rulemaking. 27 As background for the analysis, Part I briefly compares automated, machine-learning-based decision tools to more familiar forms of decisionmaking criteria. Part II uses the explanation requirements embedded in administrative law as a springboard to analyze the functions that explanation has conventionally been expected to perform with regard to rulemaking. Part III considers how incorporating inscrutable machine-learning-based decision tools changes the potential effectiveness of explanations for these functions. Part IV concludes by suggesting approaches that may alleviate these problems in some contexts. The design of a delegated, distributed decision system begins with an agenda setter (or agenda setters) empowered to determine the goals that should guide case-by-case decisions. To align decision outcomes with the system's goals as consistently and efficiently as possible, agenda setters task rulemakers with specifying decision criteria for adjudicators to apply. While legislators specify some decision criteria on behalf of the public, they routinely delegate rulemaking to agencies. 28 The general framework of agenda setting, rulemaking, and adjudication describes many decision systems, including in the private sector. 29 Rulemakers can devise various sorts of decision criteria, depending on the decision context. Criteria can be rule-like-specifying which caseby-case facts are to be taken into account and how-or standard-likegiving adjudicators more flexibility regarding what factual circumstances enforcement."); Selbst & Barocas, supra note 4, at 1138 ("Where intuition fails, the task should be to find new ways to regulate machine learning so that it remains accountable."). 27. Elsewhere, I focus on the implications for adjudication. Katherine J. they deem relevant and how they weigh those facts in coming to a decision. Decision criteria may also combine rule-like and standard-like aspects according to various schemes. For example, DWI laws in many states combine a rule-like blood alcohol threshold, above which a finding of intoxication is required, with a standard-like evaluation of intoxication at lower levels. 30 Some speed limit laws use a somewhat different scheme: Above a rule-like speed limit, there is a presumption of unsafe driving, but adjudicators may make standard-like exceptions for a narrow range of emergency circumstances. 31 Federal sentencing guidelines illustrate another possible approach. In United States v. Booker, the Supreme Court held that it is unconstitutional to treat the guidelines as completely mandatory rules. 32 Judges are now "required to properly calculate and consider the guidelines when sentencing, even in an advisory guideline system." 33 The guidelines thus retain their rule-like character, but the combination scheme now gives judges the flexibility to weigh them in light of other circumstances. Rulemakers' design choices implicate well-known trade-offs between the predictability, consistency, technical expertise, and efficiency of rulelike criteria on the one hand and the flexibility and adaptability of standard-like criteria on the other. Incorporating an automated decision tool has several implications for those design choices. First, rulemakers will need to divide decision criteria explicitly into automated and nonautomated sets, recognizing that automated assessment is utterly rule-like. Conventional narrative descriptions of decision criteria allow a spectrum from rule-like to standard-like that does not always demand such bright line allocation up front. Second, automation, especially using machine learning, distinctively constrains the sorts of rules that can be developed. 34 Third, the use of inscrutable automated decision tools limits the schemes that adjudicators can feasibly use to combine automated assessments with their assessments of nonautomated factors. 35 Complete automation of consequential decisions is uncommon, and likely to remain so, for normative and legal reasons. 36 Human adjudicators will often be tasked with evaluating some aspects of decision criteria and combining those evaluations with automated tool outputs to make final decisions. Because different combination schemes can produce very [Vol. 119:1851 different outcomes, rulemakers should specify a combination scheme for adjudicators to apply. The rigidity of automated assessment rules limits the feasible combination schemes, especially when the automated tool is inscrutable to adjudicators, and often to rulemakers as well. 37 Developments in machine learning are driving the recent upsurge of interest in automated decision tools. Machine learning is designed to fit "big" training data to complex, nonlinear models that map large sets of input features to outcome variables, 38 which serve as proxies for a relevant decision criterion of interest. 39 By using large numbers of features and training data for many individual cases, machine learning can automatically "learn" nuanced distinctions between cases from the training data, thereby producing models that are both "personalized" and more "evidence based" than may be possible using more conventional rulemaking approaches. 40 The hope is that machine-learning-based decision tools can extend automated, rule-like assessment to some decision criteria that adjudicators would conventionally have been required to evaluate in a more standard-like manner. 41 The choice to incorporate a machine-learning-based decision tool constrains rulemakers' design choices in several important ways, however. The idea that the law should be tailored to better fit the relevant context to which it applies is obvious and has been around as long as the idea of law itself."); see also P'ship for Pub. Serv., Seize the Data: Using Evidence to Transform How Public Agencies Do Business 3 (2019), https://ourpublicservice.org/wp-content/uploads/2019/06/Seize-the-Data.pdf [https://perma.cc/2UD3-D2QA] (discussing the ways in which federal agencies can utilize data to inform their decisionmaking). 41. Casey & Niblett, supra note 40, at 335 ("As technologies associated with big data, prediction algorithms, and instantaneous communication reduce the costs of discovering and communicating the relevant personal context for a law to achieve its purpose, the goal of a well-tailored, accurate, and highly contextualized law is becoming more achievable."). But see, e.g., Solon Barocas, danah boyd, Sorelle Friedler & Hanna Wallach, Editorial, Social and Technical Trade-Offs in Data Science, 5 Big Data 71 (2017) (providing an overview of several critiques of machine learning models). 1. Data-Driven Constraints on Rule Design. -Machine learning has the potential to create nuanced models of how outcome variables depend on many feature variables, but collecting the sort of "big data" needed to take advantage of machine learning's strengths is difficult and expensive. As a result, machine learning processes often rely on "found data," 42 collected for some other purpose, to train the models. 43 Unfortunately, reliance on found data leaves rulemakers at the mercy of whatever feature sets and outcome variables happen to have been collected. 44 Having "big data" for an outcome variable makes it possible to train a model that effectively predicts that outcome variable, but that sort of data is often not available for the decision criteria that are truly of interest. Treating a loose or inaccurate proxy as if it were a true assessment is likely to lead to inaccurate, biased, and otherwise problematic decisions. 45 For example, a judge might like to know the likelihood that the defendant would commit a serious crime if released pending trial, but the available data might instead record arrests for any crime, which is a loose and biased proxy for the factor of interest. 46 There is thus often a trade-off between using an outcome variable for which "bigger" data is available and using a better proxy for the true criteria of interest. The need for "big" training data similarly limits the available feature sets to data types that have been recorded for large numbers of individuals. 47 Those limits constrain the sorts of factual "evidence" that can be considered by a machine-learning-based decision tool. As a result, opting to use a machine-learning-based decision tool places restrictions on decisioncriteria design that may or may not be worth the trade-offs. The limitations imposed by training data availability are related to a machine learning model's "generalizability," or ability to perform well in handling cases that were not included in the data used to train it. 48 Generalizability is also related to issues of over-or under-fitting that are associated with the extent to which a model can pick up normatively 2016) (discussing bias and other problems with using "substantiation, meaning a decision that abuse has been investigated and found to have occurred," as an outcome variable for predicting risk of child abuse). 46. See, e.g., Eaglin, supra note 19, at 75-77 (2017) ("[D]efining recidivism is less intuitive and more subjective than it may appear."). 47. See Nay & Strandburg, supra note 38 (manuscript at 14) ("Relevant information may be left out of the feature set simply because it was not prevalent enough in the training data, because it is idiosyncratic, unquantifiable or otherwise not collectible en masse or because it is newly available and/or newly relevant due to societal or technological changes."). 48. Id. (manuscript at 7) ("A model is generalizable to the extent it applies, and performs similarly well, beyond the particular dataset from which it was derived."). relevant distinctions between cases. 49 A model that accurately fits its training data can fail to generalize well if new factual scenarios crop up over time, if its outcome variable is a bad proxy for some subgroups of the population, if its feature variables do not capture all normatively relevant distinctions, or if it is simply over-fitted to the training data because of the way that developers have tuned the machine learning parameters. By limiting the outcome variables and features that a machine-learning-based model can consider, data availability constraints are likely to limit the model's generalizability. There is no computational metric for generalizability because it depends on how well the model will perform on as-yet-unknown cases. 2. Inscrutability and Decision-Criteria Design. -Machine learning's inscrutability stems from the fact that the computational mapping from feature inputs to outcome prediction is often hard to explain in terms that are intuitively comprehensible to humans. 50 Part of what makes these mappings difficult to explain is their reliance on large numbers of features, which can make the behavior of even simple functions difficult to intuit. 51 "Deep learning" models lack explainability at a more fundamental level, in that the ways they map input features to outcome variables cannot be represented in standard forms, such as closed equations, decision trees, or graphs. 52 Even developers and subject matter experts find it difficult or impossible to interpret such models, though there is ongoing research into technical methods for producing approximate interpretations of inscrutable machine-learning models and for training sufficiently accurate explainable models. 53 Developers employ inscrutable machine learning models, despite their explainability issues, because they are often more accurate in fitting the training data. 54 Essentially, this is because a more complicated, and thus less explainable, computational mapping can always be fit more closely to the training data. 55 Discussions of this trade-off between "accuracy" and explainability have focused rather myopically on explanation's value to decision 49. Id. For the balance of this Essay, I will refer to both sorts of concerns as "generalizability." 50. 55. See id. ("[U]nderstanding and measuring AI systems in terms of their optimizations gives us a way to benefit from them even though they are imperfect and even when we cannot explain their particular outcomes."). subjects. 56 This section briefly explores how inscrutability constrains decision-criteria design, focusing on its implications for a decision system's ability to cope with generalizability concerns. Explanations of decision criteria also have important functions associated with accountability and coordination, which are analyzed in Part II, below. Generalizability is essentially the technical version of the long-standing concern that rule-like decision criteria will be insufficiently flexible and forward-thinking to produce good outcomes in real-world decisions. While machine-learning-based models can be more nuanced than conventional rules in taking account of many known features, they cannot avoid the limitations of their training data. 57 Conventional decision systems cope with generalizability concerns in two ways. First, rulemakers can scrutinize the rules in advance and try to imagine how things might go wrong, so that the rules can be redesigned to avoid problems that would otherwise crop up in real-world cases. This option is not available for inscrutable machine-learning-based models. While rulemakers can and should scrutinize the training data, features, outcome variables, and validation metrics, those methods are not equivalent to scrutinizing the logic of the rule. Second, conventional rulemakers often provide adjudicators with some standard-like flexibility to use analogy, common sense, normative judgment, and so forth to cope with case-by-case circumstances that are not adequately treated by rule-like criteria. Human adjudicators' ability to generalize in this way is limited when they are faced with the output of an inscrutable automated decision tool because they cannot discern whether and how the tool has failed to consider relevant factual circumstances. These limitations on adjudicators' capacity to generalize constrain the sorts of schemes that rulemakers can design for combining automated and nonautomated factors. For example, while adjudicators can apply the per se blood alcohol limit discussed earlier without understanding its basis, they cannot sensibly consider whether to deviate from the sentencing guidelines in a particular case without understanding the basis for the suggested sentence. 59 When critics talk about the inscrutability of machine-learning-based decision tools, a common rejoinder is that human decisionmakers are 56. See Selbst & Barocas, supra note 4, at 1111. 57. See Nay & Strandburg, supra note 38 (manuscript at 6). 58. Id. (manuscript at 14-15). 59. For a more extensive discussion of these issues, see Strandburg, supra note (manuscript at 15-17). also "black boxes," 60 in the sense that it is impossible to know what went on in a human decisionmaker's mind before coming to a decision. 61 This rejoinder misses the mark. Reason giving is a core requirement in conventional decision systems precisely because human decisionmakers are inscrutable and prone to bias and error, not because of any expectation that they will, or even can, provide accurate and detailed descriptions of their thought processes. This point sharpens when one shifts from Figure 1's decisionmaking paradigm to the more realistic paradigm of Figure 2. When the decisionmaker is a distributed, multi-actor institution, explanation requirements cannot be aimed at uncovering what went on in "the" decisionmaker's mind. Generations of legal scholars have considered the functions that explanation and reason giving can perform in delegated, distributed decision systems. Machine-learning-based decision tools ease some of the familiar challenges posed by human black boxes and create some new ones. 62 Before focusing on what is distinctive about these tools, it makes sense to learn from our experience with legal explanation requirements for human decision systems. 63 Section II.A therefore provides a brief overview of some of the primary sources for legal explanation requirements. Section II.B then discusses the primary theoretical rationales behind these requirements, as relevant to rulemakers. The principle that government decisions should be justified by reasons is well enshrined in the law, not only in the United States but also in 60. See other democracies. 64 Under U.S. law, reason giving is a key component of the constitutional requirement that no one be deprived by government of life, liberty, or property without "due process of law." 65 Though government decisionmakers are not always required to give reasons for their decisions, reason giving is the least common denominator of due process requirements. 66 Administrative law is especially concerned with delegated, distributed decision systems and has been described as "the progressive submission of power to reason." 67 Where agencies engage in rulemaking, explanations address not only the individual right to due process but also concerns about separation of powers and delegation of legislative power. 68 The Administrative Procedure Act (APA), along with the Constitution's due process requirement, imposes general procedural structures and constraints that apply to most federal agencies. 69 Its purposes include informing the public about the agency's activities and providing for public participation in the rulemaking process. 70 Its provisions thus exemplify the sort of explanation requirements that law imposes on rulemakers. Explanation and justification are at the heart of notice and comment rulemaking, the most common process by which administrative agencies promulgate regulations. 71 This dialogue with the public, who are the ultimate agenda setters for government decision systems, illustrates one aspect of explanation's function within a distributed decision system. After designing a set of regulations, an agency ordinarily must publish them in the Federal Register, along with a section that "discusses the merits of the proposed solution, cites important data and other information used to develop the action, and details its choices and reasoning. The agency must also identify the legal authority for issuing the rule." 72 After publication, the public is given an opportunity to comment on the proposal. 73 The agency must then consider the comments when it finalizes the rule. 74 Final rules must be published along with a statement that "sets out the goals or problems the rule addresses, describes the facts and data the agency relies on, responds to major criticisms in the proposed rule comments, and explains why the agency did not choose other alternatives." 75 Rulemakers are also required to explain any later changes to existing regulations, 76 which helps ensure that reforms are made carefully and for appropriate reasons. Judicial oversight is another mechanism for ensuring that rules are in accord with the agenda setter's goals. 77 The record of the rulemaking process is an important basis for judicial review. Courts generally must defer to agency legal interpretation and expertise wherever the governing statute is silent or ambiguous. 78 Nonetheless, a regulation may be overturned if a reviewing court determines that it is unconstitutional; inconsistent with the governing statutory authority; or arbitrary, capricious, or an abuse of discretion. 79 Because judges often lack the substantive expertise that would be required for effective substantive review of agency rulemaking, courts perform a so-called "hard look" review of the rulemaking record to test whether an agency approached a given rulemaking task diligently, rationally, and without pursuing conflicting agendas. Under the hard look approach to the arbitrary and capricious standard, an agency must "demonstrate that it engaged in reasoned decisionmaking by providing an adequate explanation for its decision," 80 "provide the 'essential facts upon which the administrative decision was based' and explain what justifies the determination with actual evidence beyond a 'conclusory statement.'" 81 A rule will also fail the test if it "is the product of 'illogical' or inconsistent reasoning; . . . fails to consider an important factor relevant to its action, such as the policy effects of its decision or vital aspects of the problem in the issue before it; or . . . fails to consider 'less restrictive, yet easily administered' regulatory alternatives." 82 The prospect of hard look review "on the record" gives agencies incentives to create detailed records justifying the rules they promulgate, thereby also providing incentives for agencies to make rules that can be justified by such records. These accountability mechanisms are far from perfect and are regularly critiqued 83 but nonetheless endure as core means for addressing the unavoidable accountability problems faced by delegated, distributed decision systems. Legal scholars have identified many normative rationales for reasongiving requirements. While some of these rationales pertain primarily to explanations aimed at decision subjects, 84 many are relevant to this Essay's focus on rulemaking. One important category of rationales focuses on improving the quality of the rules, in the sense of how effectively they further the agenda setter's goals. While scholars have mostly viewed quality control through an accountability lens, the law's reason-giving requirements also facilitate coordination, as this section explains. Another category of rationales is founded in the special relationship citizens have with a democratic government, in that they are both decision subjects and agenda setters. 1. Reason Giving to Improve Quality. -Reason giving "promotes accountability by limiting the scope of available discretion and ensuring that public officials provide public-regarding justifications for their decisions" and "facilitates transparency, which, in turn, enables citizens and other public officials to evaluate, discuss, and criticize governmental action, as well as potentially to seek legal or political reform." 85 It is also a bulwark against arbitrariness. 86 For administrative agencies, "legitimacy flows primarily from a belief in the specialized knowledge that administrative decisionmakers can bring to bear on critical policy choices. And the only evidence that this specialized knowledge has in fact been deployed lies in administrators' explanations or reasons for their actions." 87 In addition to promoting quality through accountability, reason giving might be expected to improve rule quality through the disciplining effect of "showing your work" and by facilitating communication and coordination among rulemakers. Reason giving also assists in the evaluation and reform of rules. 88 a. The "Show Your Work" Phenomenon. -The "show your work" phenomenon is familiar: The very process of explaining one's reasoning is likely to improve it by highlighting loopholes, inconsistencies, and weaknesses. 89 For groups, the "show your work" phenomenon includes the benefits of deliberating to jointly produce an explanation. If rulemakers anticipate that outsiders will see, and potentially critique, their explanations, the effect is heightened, since the prospect of being exposed as sloppy, ill informed, biased, or captured should provide incentives for rulemakers to devise rules that can be explained and justified. By forcing rulemakers to justify their work product in terms of appropriate goals and relevant facts, the "show your work" phenomenon may also deter bias and arbitrariness. This phenomenon will presumably 2012) (discussing the "gathering movement to reconceptualize the legitimacy of administrative agencies in terms of their political-and specifically, their presidential-accountability as opposed to their expertise, their fidelity to statutory commands, or their role as fora for robust citizen participation and deliberation" (footnotes omitted)). 88. See Strandburg, supra note 27 (manuscript at 12-13). 89. See In Re Expulsion of N.Y.B., 750 N.W.2d 318, 326 (Minn. Ct. App. 2008) (drawing an analogy between procedural requirements in administrative law and the "show your work" method of teaching mathematics). be most effective when it creates self-awareness of unintentional bias and arbitrariness. But, as one commentator colorfully put it, "hypocrisy has a civilising force" in human decisionmaking. 90 Explanations facilitate scrutiny, making it more difficult to mask intentional bias. b. Explaining to Agenda Setters. -Notice and comment, review by the Office of Information and Regulatory Affairs (OIRA), and judicial review exemplify the interplay of explanation and feedback between agenda setters and rulemakers. Explanations to agenda setters perform two main functions related directly to the principal-agent problems mentioned earlier. 91 The first function is accountability, which entails keeping an eye out for ways in which a rulemaking entity's bias, conflicts of interest, sloppiness, or lack of zeal might have infected the rule it devised. The second function is to catch misalignments between the agenda setter's goals and the rule's potential application to real-world case types that rulemakers may not have considered adequately (or at all). This second function relates to the generalizability concerns discussed in Part I. 92 For government decision systems, the general public is the ultimate agenda setter. Public feedback may also be vital to some private decision systems because of the value of engaging diverse perspectives in ferreting out problems of accountability and misalignment. The benefits of a public explanation obviously depend on whether the public is willing and able to engage with it-a perennial problem. Notice and comment has been criticized because well-funded, concentrated interests are better equipped to understand the proposed rules and to use their influence to bend them to their own benefit. 93 The empirical picture is mixed. While many studies find little participation by individuals in notice and comment rulemaking, 94 some have found substantial participation in commenting on particular sorts of regulations by citizen groups or individuals submitting form letters. 95 While citizen groups are usually not heavily resourced, they can build up significant subject matter expertise, allowing them to submit meaningful feedback and criticism. 96 This is obviously not a complete answer to the power imbalance, but it counsels against underestimating the societal benefit of public explanations. Moreover, the power imbalances in the case-by-case decision systems of interest to us here are somewhat different from those in the standard interest group story, in which powerful regulated entities use notice and comment to influence agencies propounding environmental or consumer protection regulations. 97 Here, the affected parties are individuals, who do not have outsize power to influence the design of decision systems that are critical to their opportunities in vital arenas such as employment, credit, public benefits, criminal justice, and family life. 98 Moreover, these individuals are members of the public and thus agenda setters in their own right. c. Communication and Coordination Among Rulemakers. -It almost goes without saying that the quality of outcomes from a delegated, distributed decision system depends on coordination and communication between the players, including within rulemaking entities and between rulemakers and adjudicators. 99 For conventional decision systems, explanation's coordinating function has received considerably less scholarly attention than its accountability function. This is not terribly surprising for two reasons. First, the narrative form of conventional rules makes their content somewhat self-explanatory to agenda setters and adjudicators, shifting the focus toward explaining why that content is justified. Second, explanation's coordinating function piggybacks on its accountability function. Requiring rulemakers to create explanations aimed at the public, courts, or other agenda setters indirectly provides incentives for the coordinated effort necessary to create those explanations, which 95. See Mariano-Florentino Cuéllar, Rethinking Regulatory Democracy, 57 Admin. L. Rev. 411, 462 (2005) (studying three regulatory proceedings in which 72.1%, 98.6%, and 98.3% of comments, respectively, came from individual members of the public; in two of the proceedings, individual comments were almost exclusively form letters); Golden, supra note 94, at 253-55 (finding contributions by citizens' groups ranging from 0% to 16.7% of comments depending on the agency and regulation). 96. See Cuéllar, supra note 95, at 450-51, 458-59 (finding, in a study of two regulatory proceedings, considerably higher values for "comment sophistication" in comments from public membership or public interest organizations than from individuals); Yackee, supra note 93, at 105 ("[I]nterest group comments provide a new source of information and expertise to the bureaucracy during the rulemaking process."). 97. See Golden, supra note 95, at 255 (contrasting EPA and NHTSA rulemakings with "extremely limited participation by public interest or citizen advocacy groups" with HUD rulemakings where "commenters include citizen advocacy groups, individual citizens, and a wide range of government agencies"). 98. See supra notes 19-23 and accompanying text. 99. For more on explanations between rulemakers and adjudicators, see Strandburg, supra note 27 (manuscript at 10-13). in turn activates the "show your work" phenomenon. 100 Similarly, the record creation incentivized by hard look review 101 requires internal coordination, while the resulting record can facilitate further communication and coordination. In addition, and partly to ensure that the required explanations and record will pass muster, rulemaking bodies often impose procedures that amount to internal explanation requirements. 102 2. Reason Giving, Democracy, and Respect. -Reason giving legitimates governmental decisionmaking in a democracy because, as one scholar puts it, "[a]uthority without reason is literally dehumanizing. It is, therefore, fundamentally at war with the promise of democracy, which is, after all, self-government." 103 Particularly in the context of rulemaking by unelected administrative agencies, reason-giving requirements ensure that members of the public are treated as citizens, rather than subjects: "[T]o be subject to administrative authority that is unreasoned is to be treated as a mere object of the law or political power, not a subject with independent rational capacities." 104 Explanations also empower citizens in their agenda-setting role, by helping them to understand what the rules require, providing bases for individual and group opinion formation and advocacy, and helping minorities to identify rules that ignore or undermine their interests. 105 Reason giving thus "embodies, and provides the preconditions for, a deliberative democracy that seeks to achieve consensus on ways of promoting the public good that take the views of political minorities into account." 106 These rationales do not have the same force for private-sector decisions, where decision subjects ordinarily do not have similar agenda-setting rights. But explaining the rationale behind decisionmaking criteria also comports with more general societal norms of fair and nonarbitrary treatment. Moreover, the public has an interest as citizens and individuals, both legally and ethically, in the fairness and reasonableness of private decision systems that fundamentally affect people's lives. 107 Indeed, private decision systems do not operate in a legal vacuum but are subject to legal protections including, for example, antidiscrimination laws and protections against fraud. In addition, as a practical matter, some subjects of private-sector decision systems are also users or customers, whose market relationships to decisionmakers give them some leverage to demand explanations of the rules that govern those relationships. This Part builds on Part II's brief sketch of the purposes of reasongiving requirements by considering how the limited explainability of machine-learning-based decision tools affects the functions that explanations have conventionally been expected to perform in connection with rulemaking. Section III.A begins by taking a more precise look at which aspects of a machine-learning-based decision tool are unexplainable. Section III.B then reflects on how each of the explanation functions described in Part II is affected by the incorporation of an inscrutable machine-learning-based decision tool. Machine learning's explainability problems reside in the inscrutability of a machine learning model's computational mapping of input features to outcome variables. 108 There are, however, many aspects of the development of machine-learning-based decision tools, and of the decision rules embedded in those tools, that are just as explainable as a rule in conventional narrative form. To assess the impact of inscrutable machine-learning-based decision tools, it is important to be precise about what can and cannot be explained. 1. Explainable Components of a Machine-Learning-Based Decision Tool. -In some respects, the touted "black box" nature of machine learning models 109 is not nearly all that it is cracked up to be. Many choices made in the process of creating an automated decision tool are not so different from choices made in more traditional rulemaking processes. Moreover, some of those choices are embedded as components of the rules of the ultimate decision system, just as similar choices are reflected in narrative rules, and can be explained in conventional fashion.  Definitions of decision criteria to be assessed by the automated tool;  Definitions of outcome variables to be used as proxies for decision criteria;  Definitions of feature variables to be used as factual evidence in automated decision criteria assessments; and  Combination schemes governing how adjudicators should combine automated assessments with other relevant information to make decisions. Whether, under what circumstances, and to whom the law requires rulemakers to explain these components is outside the scope of this analysis, but there are no technical barriers to requiring such explanations. 2. Explainable Rulemaking Record. -Other important choices involved in creating a machine learning model are not reflected on the face of the ultimate automated decision rule but can be described and explained in a record of the rulemaking process. 110 Such choices include selecting training data, determining machine learning algorithms and technical parameters, devising validation protocols, and evaluating whether a model has been adequately validated to justify using it in a decision rule. All of these choices, and the reasons for them, could be included in a record of the development of a machine-learning-based decision tool. Most importantly, such a record could include information about the sources, demographics, and other characteristics of the training data sample; definitions of validation metrics; and results of validations and performance tests. This information plays much the same role as information about statistical and more specialized technical bases for rules that are routinely included in agency rulemaking records and facilitate hard look review by courts and the cost-benefit analysis required for some rules by OIRA. 111 In light of the previous section's parsing of explainable and unexplainable aspects of machine-learning-based decision tools, this section explores how and why incorporating such tools into a decision system is likely to affect the functions of explanation, 112 identifying where the inscrutability of a machine learning model's computational mapping from input features to outcome variables is likely to create serious problems. 1. The "Show Your Work" Phenomenon. -The "show your work" phenomenon carries over straightforwardly to an automated decision tool's 110. See explainable components and recordable information. 113 In essence, developers' design choices are all explainable, and the benefits of the "show your work" phenomenon will apply to those choices. 114 The full benefits of the "show your work" phenomenon may not be retained, however, for two reasons. First, the "show your work" phenomenon is effectuated primarily through self-awareness and thus depends on developers having sufficient incentives to create detailed and persuasive explanations. Unfortunately, common practices for developing automated decision tools undermine those incentives. Because many rulemaking entities do not have data scientists on staff, they outsource development or purchase off-the-shelf products. 115 Many of these outsourced machine-learning-based decision tools are burdened with confidentiality agreements that severely limit the explanations and records of development that are provided to rulemaking entities and may block public disclosure almost entirely. 116 Such secrecy undermines the "show your work" phenomenon. Second, the "show your work" phenomenon will not aid in resolving problems that developers cannot avoid through careful design choices and validation, as discussed further in section III.B.2.c, below. 2. Explaining to Agenda Setters. -This section considers how the functions of explanation to agenda setters depend on access to (i) the explainable components; 117 (ii) information about data selection, sources, and validation that could be available in a rulemaking record; 118 and (iii) a conventional narrative explanation of the way that the rule maps input features to outcome variables. Explanations to agenda setters serve accountability functions but can also be important for generalizability. 119 As noted earlier, the benefits of public explanation are often effectuated through advocacy groups. 120 To isolate the unique issues stemming from machine-learning-based decision tools, it is thus helpful to consider whether such tools can be satisfactorily explained to advocacy groups with significant substantive expertise and moderate resources, assuming that most other agenda setters, such as legislatures, courts, OIRA, or private businesses, will have at least the capacity of such groups. a. Explainable Components. -The explainable components identified in section III.A.1 will be understandable to a public advocacy group with sufficient expertise and resources and can facilitate extremely valuable checks on the decision system's accountability and generalizability. For example, such a group might assess whether the proxy outcome variable is biased or unlikely to generalize to some sorts of cases; consider whether the use of some feature variables is normatively unacceptable or whether important features are missing from the list; or evaluate whether the amount of flexibility given to adjudicators in combining the automated tool output with other information is appropriate. These agenda setters can help to evaluate whether it is normatively appropriate to use a rule-like automated tool to evaluate certain decision criteria or whether a more flexible, standard-like approach should be required. 121 Though rulemakers presumably will also have considered this question, they may be prone to view automation's potential through rose-colored glasses for various reasons, such as a bias toward cost-cutting measures. 122 b. Data Sources and Validation. -Explanations of data sources and validation in a rulemaking record are potentially useful for uncovering bias or sloppiness, detecting holes in the coverage of the sample set, and ensuring that all normatively relevant performance metrics have been examined. For example, unrepresentative training data is one important source of generalizability problems. 123 The public's diverse perspectives may give it an edge over rulemakers in identifying forms of representativeness that might matter for the decision criteria in question. 124 The technical knowledge about data science that is required to understand this information may currently be beyond the capacity of many advocacy groups and other agenda setters. 125 Over time, however, advocacy groups, particularly the larger and better resourced among them, will undoubtedly upgrade their technical expertise by involving data scientists in their work, as advocacy groups have done in other 126 One concern is that there are so many decision systems-national, state, local, and private--incorporating machinelearning-based decision tools that it may be difficult for advocacy groups, many of which might be small and otherwise nontechnical in nature, to keep up with all of them. For the most part, though, if characteristics about the training data, results from performance tests, and other information discussed in section III.A.2 are included in the rulemaking record, they can be expected to perform the same explanation functions as the information in a more conventional rulemaking record. c. Inscrutability of the Computational Mapping from Input Features to Outcome Variable. -Information about the explainable components, data sources, and validation studies may be sufficient for the accountability function of explanation to agenda setters, in part because those information sources provide access to the most important information available to the rulemaking entity itself. The inscrutability of machine learning models creates more fundamental problems, however, regarding the extent to which explanation can help detect generalizability problems and other unintentional misalignments between the decision system's purposes and the automated criteria. 127 In some respects, the generalizability of a rule is always a guessing game-nobody can be certain how any rule will perform "out in the wild" because there may be cases that neither agenda setters nor rulemakers could have anticipated. 128 Conventional rules, with their narrative format, nonetheless allow human readers to anticipate and identify some generalizability issues using logical inference, analogy, and common sense. These reasoning methods are not applicable to inscrutable machine learning models, however. Moreover, computational validation tools and other statistical and mathematical analyses cannot provide the same sorts of insights about generalizability, which depend on a grasp of the logic of the rule. Researchers have invented various approaches for creating approximate explanations for a machine learning model's opaque mapping. 129 While many of these methods are designed to explain the specific 126. See, e.g., Shobita Parthasarathy, Breaking the Expertise Barrier: Understanding Activist Strategies in Science and Technology Policy Domains, 37 Sci. & Pub. Pol'y 355, 358-60 (2010) (describing how breast cancer patient advocates found sympathetic experts to educate them about the technical complexities of their causes in order to advance their advocacy). Indeed, some advocacy groups are already beginning to do this. See, e.g., AI Now Inst., Litigating Algorithms: Challenging Government Use of Algorithmic Decision Systems 4-5 (2018), https://ainowinstitute.org/litigatingalgorithms.pdf [https://perma.cc/M82T-LR9H] (noting that organizers of a recent workshop examining litigation involving the government's use of algorithmic systems featured participation by relevant legal and scientific experts). 127. See supra section II.B.1.b. 128. Indeed, this is a primary justification for using standards rather than rules. See Strandburg, supra note 27 (manuscript at 13). 129. See generally Lipton, supra note 5 (surveying the academic literature of techniques designed to render machine learning models interpretable). results of individual cases, 130 some attempt to create more general approximate explanations, which might be useful for probing generalizability issues. 131 For example, a model trained to distinguish wolves from dogs in photographs worked well on its training data but failed on a larger set of photos. 132 The problem was that the training data was skewed-nearly all of the wolves were in snowy landscapes, so the model used the presence of snow to distinguish wolves from dogs. Techniques for creating approximate explanations of the machine logic helped to identify that generalizability problem because, after receiving the explanations, nearly all human observers were able to recognize that "snow" played a key role in that logic. 134 On the whole, though, it remains uncertain whether any of these technical approaches can replace human analysis of narrative rules. Though machine learning models are trained to reproduce the outputs that human beings assigned to the training data, the mappings they create are not likely to be similar to human mental models. 135 While the association of wolves with snow ran throughout the training data, tougher generalizability issues may arise from unanticipated or uncommon "edge" cases. Humans are reasonably good at reading rules and thinking about whether they are mistaken or have blind spots but are not similarly good at predicting an inscrutable machine learning model's blind spots. For example, a deep learning model trained to triage pneumonia patients performed very well on validation tests. Researchers also created a less accurate, but explainable, model based on the same data. 137 Scrutiny of the explainable model identified a problem in the data: Pneumonia patients with asthma are high risk, but because they had routinely been treated in the ICU, their outcomes were good, fooling the model into treating them as low risk. 138 The data scientists and medical experts working on the project could, in principle, have foreseen that the data for asthma sufferers might be misleading, but they didn't. They identified the asthma problem only after scrutinizing the explainable model. 139 The problem with the asthma data presumably also affected the inscrutable machine learning model, but researchers would not have been able to detect it. Moreover, as the study authors noted, because the inscrutable machine-learning-based model was fit more tightly to the training data than the explainable model, "it was possible that the neural nets had learned other patterns that could put some kinds of patients at risk" that did not show up in the explainable version. 140 Without an intuitive window into the logic of the machine learning model, there was simply no way to tell. In sum, while the explainable components and rulemaking record can give agenda setters a good grasp on accountability and some handle on potential generalizability problems, there is no doubt that both rulemakers and agenda setters will more effectively anticipate generalizability problems if they can simply read the rule. Whether such lingering generalizability concerns outweigh the benefits of using an inscrutable machine-learning-based tool for particular decision criteria in a particular context can only be a normative judgment. Agenda setters-including, where appropriate, the public-should have the final say on that trade-off. 3. Communication and Coordination Among Rulemakers. -In conventional rulemaking, explanations created for agenda setters may be sufficient to facilitate communication and coordination among rulemakers. Incorporating a machine-learning-based tool into a decision system increases the challenges of communication and coordination, however, because of the disciplinary barriers between substantive experts and data scientists. Those barriers both heighten the importance of explanation and increase its difficulty. Data scientists differ from traditional rulemakers in three respects: (i) they are tool-building specialists, rather than subject matter specialists; (ii) they often do not work for the rulemaking entity; 141 and (iii) trade secrecy claims and confidentiality agreements can constrain their interactions with substantive rulemakers. 142 Because data scientists are information technologists, rulemakers may be tempted to view their work as a technical task akin to those as-signed to an IT department. 143 Machine learning model development is deeply entangled with subject matter expertise and normative choices, however. 144 Data scientists' role is thus more like that of empirical economists, who are also technical specialists whose methods have broad application. Good economic modeling requires considerable substantive knowledge, however, which economists must access by collaborating with substantive experts or acquiring substantive expertise. Because they are highly contextual, economic models cannot simply be used off the shelf. Before porting them over to new situations, their underpinnings must be scrutinized to determine whether they can be appropriately adapted for use in those situations. Data scientists' design decisions are even more substantively fraught because the inscrutable models they create are used directly for assessing decision criteria. As a result, the substantive, normative, and policy assumptions underlying these choices have a direct impact on decision outcomes. Though close communication and coordination between data scientists and substantive experts is critical, each group's unfamiliarity with the other's disciplinary knowledge will tend to impede it. When the development of automated decision tools is outsourced, those difficulties inevitably mount. Confidentiality agreements and trade secrecy claims keep information from rulemakers and discourage open communication, which only makes matters worse. 145 Documentation, user manuals, and training are traditional forms of explanation between software engineers and their clients. 146 While they may be sufficient for users, those explanatory forms are unlikely to facilitate the close communication and coordination required for codevelopment of decision criteria that incorporate machine-learning-based decision tools. Some view the use of automated decision tools as inherently dehumanizing or disrespectful, at least in some contexts. 147 Here I do not adopt that view and hence consider whether the inscrutability of machine-learning-based decision tools creates problems for democratic and human values even when conventional rule-like decision criteria would have been acceptable. Though democratic legitimacy and dignitary concerns are part of the standard reasons for requiring government decisionmakers to provide explanations, 148 complete explanations of all government decisions have never been required. Machine-learning-based decision tools can be explained in a limited sense, as just discussed. The question, then, is when those limited explanations are sufficient in light of these sorts of concerns. The answer to this question is likely to depend on many contextual factors, including the nature of the decision and what is at stake, the justification for automating a particular aspect of the decision criteria, the way in which adjudicators are expected to use the automated output in coming to final decisions, and, crucially, the extent to which explainable aspects of the automated tool are, in fact, explained. Citizens will likely have or develop a sense of whether the limited explanations available in a particular context are sufficient in light of these legitimacy and dignitary values. This sort of evaluation is likely to incite controversy but is not terribly different from the normative assessments that currently go into determining whether rules are appropriately employed in various contexts or what level of "due process" is appropriate for a particular decision. Rulemakers should, however, be prepared for the possibility that using inscrutable machine learning models for some sorts of decision criteria will be normatively unacceptable to the citizenry, regardless of how well-validated the machine learning model might be. This concluding Part considers how to obtain as many of the traditional benefits of explanation as possible for decision systems that incorporate machine-learning-based decision tools. As noted earlier, machine learning's now-canonical "explainability" problem pertains only to the model's computational mapping between features and outcome variables. 149 While this inscrutability is significant, many societally significant aspects of the development of a machine-learning-based decision tool, its final form, and its integration into a decision system are just as explainable as conventional narrative rules and their underpinnings. In particular, the choice to employ a machine-learning-based decision tool to evaluate particular decision criteria is fully explainable and has significant normative and policy implications that should be open to scrutiny. Section IV.A thus argues for applying traditional explanation requirements to the explainable aspects of such systems. Section IV.B focuses on the less-discussed issues of communication and coordination within the rulemaking entity, pointing out that these issues require more attention when automated decision tools are introduced because of the disciplinary barriers between subject matter experts and data scientists within the rulemaking entity. Section IV.C suggests mechanisms for improving the capacity for rulemaking entities and advocacy groups to make full use of the explanations that would be made available to them under the explanation requirements proposed in section IV.A. 150 While large rulemaking entities and advocacy groups may have sufficient resources to obtain the fairly minimal data science expertise necessary for this purpose, smaller rulemaking entities and advocacy groups might consider pooling resources-though perhaps not with one another-to gain access to it. What sort of explanation should be required when a machine-learning-based decision tool is incorporated into a decision system? In particular, how should this question be answered when the tool is incorporated into decision criteria that operate as a "rule" under the APA? When a conventional narrative rule is published in the Federal Register for comment, the public receives full notice of its terms. 151 For rule-like criteria, publication allows the public to determine-and critique-how cases of any imaginable sort would be handled. 152 Because inscrutable machine-learning-based decision tools cannot be summarized in narrative form (or even in understandable mathematical or graphical form), there is no way to provide an equivalently detailed mapping from cases to outcomes. If notice and comment demands this sort of detailed mapping, inscrutable decision tools simply cannot be incorporated into APA rules. While "just say no" to inscrutable decision tools is certainly an appropriate approach in some decision contexts, we should be wary of adopting it as a general response to notice and comment requirements or other explanation mandates. Because machine-learning-based decision tools are attractive to policymakers, 153 an overly expansive interpretation of what explanation requires might backfire by motivating rulemakers and courts to adopt narrower interpretations of whether such requirements apply at all. Moreover, preemptively depriving society of all such tools for all purposes in all significant decision contexts seems questionable as a policy matter, given the advantages of machine-learning-based decision tools in some contexts. [Vol. 119:1851 An opposite approach, which may be closer to what is happening on the ground, 154 is to pretend that machine-learning-based decision tools are not really rules at all but something else that does not have to be explained. 155 This approach is, if anything, worse because it deprives society of the benefits of explanation for the aspects that can be explained and ignores the true rule-like nature of the tools. The analysis here suggests an intermediate approach: Define what constitutes an adequate explanation of a machine-learning-based decision tool and require such an explanation, thus subjecting the incorporation of inscrutable machine learning models to scrutiny while not barring it entirely. This section proposes a framework for adequate explanation composed of two parts: (1) information required to describe the rule and (2) information treated as part of the record for backing up the rule, as in hard look review. 156 Standard administrative law requirements of notice and recordkeeping could be interpreted in these terms. 1. Describing the Rule. -An adequate description of machine-learning-based decision criteria-as would be published in the Federal Register for notice and comment-would include all of the "explainable components" of the rule. 157 Those components are part and parcel of the decisionmaking rule and should be treated as such. They are no more difficult to explain or to understand than conventional narrative rules, and their disclosure fulfills the intended functions of explanation requirements. 158 When disclosure and explanation of a rule is legally re-quired, trade secrecy should not excuse explanation of these aspects, which reflect critical, policy-relevant rulemaking choices. 2. The Rulemaking Record. -Selecting training data and validating the tool's performance bear the same relationship to developing a machine-learning-based decision tool that more familiar sorts of factual inquiry and statistical analysis bear to the development and justification of a conventional rule. Summary information about the training data, explanations of how it was sourced, descriptions of the validation process, and validation results should thus be part of the rulemaking record and made available on the same terms as other parts of the record. Rulemaking entities should not sign confidentiality agreements regarding this information. However, the training data set itself should ordinarily be kept confidential for privacy reasons. Confidentiality agreements regarding certain technical parameters and details of the machine learning process might also be appropriate. To promote informed, effective overall decision-criteria design, data scientists need a deep understanding of both the overall goals of the decision system and how the criteria evaluated by the automated tools will be incorporated into the ultimate decision. Concomitantly, substantive rulemakers need to acquire a basic understanding of the machine learning process so that they can make appropriate choices about whether to automate particular decision criteria, interact meaningfully with data scientists throughout the development process, and design appropriate combination schemes for adjudicators to follow when using the outputs of machine-learning-based tools. The incentives provided by the proposed explanation requirements will go some way toward facilitating the requisite communication and coordination between data scientists and substantive experts. But because these interactions are of such vital importance to decision quality and face significant barriers, more may be necessary. Rulemakers who are considering incorporating a machine-learning-based tool into a decision system would therefore be well advised to adopt a prospective "by design" plan aimed at ensuring the necessary level of cooperation and communication between data scientists and substantive rulemakers. Ideally, the development of machine-learning-based decision tools would be brought in-house, so that dedicated data scientists could develop substantive expertise to support their work. That approach is probably overly ambitious for most rulemaking entities, who would only be undertaking such development on a sporadic basis. Larger entities should still consider hiring an in-house data scientist whose role would be not only to advise the rulemaking entity in its interactions with outside contractors but also to initiate and facilitate the necessary close interactions between rulemakers and outside data scientists. At a minimum, where an automated decision tool is procured from outside data scientists, substantive rulemakers must demand clear and thorough explanations of the aspects described in section III.A so that they can understand the outputs of the automated decision tool and create appropriate combination schemes for adjudicators to use. The depth of information that is available about the automated tool constrains the sorts of combination schemes that adjudicators can implement. Clear communication and coordination between data scientists and substantive rulemakers are critical to assessing the severity of those constraints. As discussed above, inscrutability is especially likely to limit the extent to which adjudicators can serve the role in addressing generalizability issues that is commonly assigned to them in conventional decision systems. Rulemakers must understand and confront these and other trade-offs involved in using inscrutable decision tools. While larger rulemaking entities may be able to develop data science expertise to help them communicate and coordinate with the data science contractors who will probably continue to do most development of machine-learning-based decision tools, smaller rulemaking entities will likely be strapped to find the necessary resources. This is a problem because smaller entities are perhaps most likely to be attracted to the potential cost-savings of automation, while also being least able to afford to acquire data science expertise, increasing the temptation to use off-theshelf solutions. It would be wise for smaller rulemaking entities to develop mechanisms for pooling resources with similarly situated entities to provide access to data science expertise. Ideally, such pooling would bring rulemaking entities in similar substantive arenas together so that the data scientists they work with could also build up substantive expertise. This proposal is tentative; its feasibility would depend on working out the details. If this sort of resource pooling is feasible, it should receive public support, and participation might even be mandated. Advocacy groups and other agenda setters in a given arena may also benefit from creating similar centers of data science expertise to assist them in understanding the explanations provided by rulemakers and ensuring accountability. CONCLUSION Delegated, distributed decision systems-which are responsible for many highly consequential decisions affecting individuals-confront issues of cost, efficiency, and consistency that make automated decision tools particularly attractive. Though scholars and policymakers have fo-cused on explanations to decision subjects and accountability to the public, the inscrutability of automated decision tools has significant, and underappreciated, implications for the explanatory flows required to develop and implement such systems. While sharing the explainable aspects of these tools can replicate some of explanation's traditional functions, using inscrutable automated decision tools inevitably degrades decisioncriteria development in some respects. Thus, in weighing the advantages and disadvantages of such tools for a given decision context, policymakers and system designers should consider how inscrutability affects rulemakers and, as I discuss elsewhere, adjudicators, along with its direct impact on decision subjects. 19. See, e.g., Jessica M. Eaglin, Constructing Recidivism Risk, 67 Emory L.J. 59, 61 (2017). 20. See, e.g., State v. Loomis, 881 N.W.2d 749, 753 (Wis. 2016). 21. See, e.g., Dan Hurley, Can an Algorithm Tell When Kids Are in Danger?, N.Y. Times Mag. (Jan. 2, 2018), https://www.nytimes.com/2018/01/02/magazine/can-an-algorithm-tell-when-kids-are-in-danger.html (on file with the Columbia Law Review). 22. See, e.g., Matthew Adam Bruckner, The Promise and Perils of Algorithmic Lenders' Use of Big Data, 93 Chi.-Kent L. Rev. 3, 12-13 (2018). 23. See, e.g., Pauline T. Kim, Data-Driven Discrimination at Work, 58 Wm. & Mary L. adjudicating-algorithm-regulating-robot/ [https://perma.cc/7R9L-AGDR].

FIGURE 1: SCHEMATIC OF EXPLANATORY FLOWS IN A SIMPLE DECISION SYSTEM

16. Restatement (Third) of Agency § 8.11 (Am. Law Inst. 2005).

Rulemaking Entity for Specifying Decision Criteria Adjudicative Entity

I. INCORPORATING MACHINE-LEARNING-BASED TOOLS INTO DELEGATED, DISTRIBUTED DECISION SYSTEMS

A. Rules, Standards, and Automated Decision Tools

B. Machine Learning Models as Decision Tools

46. See, e.g., Eaglin, supra note 19, at 75-77 (2017) ("[D]efining recidivism is less intuitive and more subjective than it may appear.").

II. CONVENTIONAL REASONS FOR EXPLAINING RULEMAKING

88. See Strandburg, supra note 27 (manuscript at 12-13). 89. See In Re Expulsion of N.Y.B., 750 N.W.2d 318, 326 (Minn. Ct. App. 2008) (drawing an analogy between procedural requirements in administrative law and the "show your work" method of teaching mathematics).

98. See supra notes 19-23 and accompanying text. 99. For more on explanations between rulemakers and adjudicators, see Strandburg, supra note 27 (manuscript at 10-13).

III. EXPLAINING MACHINE-LEARNING-BASED DECISION TOOLS

A. Cabining Machine Learning's Explainability Problems

B. Explanation, Decision System Quality, and Machine-Learning-Based Tools

127. See supra section II.B.1.b. 128. Indeed, this is a primary justification for using standards rather than rules. See Strandburg, supra note 27 (manuscript at 13).

129. See generally Lipton, supra note 5 (surveying the academic literature of techniques designed to render machine learning models interpretable).

C. Reason Giving, Democracy, Respect, and Machine Learning

A. Explaining the Incorporation of Machine-Learning-Based Decision Tools

B. Coordination and Communication Between Data Scientists and Substantive Rulemakers

C. Centers of Data Science Expertise for Rulemaking Entities and Advocacy Groups

adjudicating-algorithm-regulating-robot/ [https://perma.cc/7R9L-AGDR].


Scales of Justice

" Scales of Justice ," Slate , May 12, 2014 (with Kal Raustiala)

"Scales of Justice," *Slate*, May 12, 2014 (with Kal Raustiala)

Kal Raustiala & Christopher Jon Sprigman, Scales of Justice, Slate (May 12, 2014), https://slate.com/technology/2014/05/white-smith-music-case-a-terrible-1908-supreme-court-decision-on-player-pianos.html

Way before Spotify, stereos, or even radio, Americans who wanted to listen to music at home had one choice: They could play it themselves. This changed by the 1890s, when a hot new technology arrived: the player piano. Player pianos were miraculous in their ability to play popular songs and old standards alike while people sang along, danced, or just enjoyed the sounds. But as with a lot of newfangled machines, not everyone loved the player piano. In particular, the technology deeply troubled composers, such as the famed John Philip Sousa. Sousa worried that the pianos would kill the public’s demand for sheet music, and copyright royalties from the sale of sheet music were what paid composers’ bills. To make matters worse, the player piano companies refused to pay royalties for the songs they put on piano rolls—scrolls of paper with holes punched out in patterns. People couldn’t read the rolls; they spoke only to machines. And on that ground, the player piano companies argued that the rolls did not “copy” songs, and so could be manufactured and distributed without the need to pay royalties. This was an exceptionally stupid argument. Sheet music and player piano rolls both copy songs; they just use different languages. What matters is what comes out when the language is translated. And for both, the answer is the same: music. So the issue wasn’t terribly difficult, but apparently it was complicated enough to fool the Supreme Court. In a 1908 case called White-Smith Music Publishing Co. v. Apollo Co., the Supreme Court sided with the player piano companies. The court said that because humans couldn’t read player piano rolls, they were not in fact copies, and as a result, composers like Sousa couldn’t demand any royalties. The conventional wisdom is that no one will innovate if others can copy freely. Why invest the time to invent a better mousetrap, or write a great song, if someone can just take the fruit of your hard work and sell it themselves? This basic logic—that copying is bad for creativity—is the fundamental reason we have patent and copyright law. These rules exist to make sure that copying is only legal when the creator agrees to it. Unauthorized copying—as anyone who has watched a DVD with an FBI warning at the start knows—is against the law. So when, more than a century ago, the Supreme Court declared that copying by player piano companies was just fine, we saw the beginning of a process that culminated in the death of American songwriting. Songwriters stopped writing, fearful that the piano roll companies would just steal their new songs and feed the expanding market for player pianos at home. Thanks to the Supreme Court, the golden age of American song essentially ended in 1908. If something seems wrong about this story, it’s because the last part is completely false. As we all know, the golden age of American songwriting did not end in 1908. In fact, geniuses such as George Gershwin and Cole Porter were still kids when White-Smith Publishing was decided. Nearly all the “great American standards” were written in the decades after White-Smith Publishing. The reason American songwriting not only survived the onslaught of the player piano, but thrived, tells us a lot about how creativity really works. The first important thing to know is that the actual result in White-Smith Publishing lasted but a year before it was overturned by Congress. The Copyright Act of 1909 extended the law to cover all “mechanical” reproductions of songs, whether read by people or machines. At the same time, however, Congress did a very important thing: It mandated that all songs would be subject to what lawyers called a “compulsory license.” This meant that any musician could copy another’s song without asking permission, so long as they paid a low standardized fee to the songwriter ($0.02 per copy, originally, and quite a bit lower, adjusted for inflation, today). So on the one hand, Congress overthrew the argument that player piano rolls were not songs. But on the other, Congress set everyone free to copy, just so long as a small fee was paid. So why did Congress create this unusual system of legal copying? Because it was afraid of one company—Aeolian. That firm is long gone. But in the early part of the 20th century, Congress viewed Aeolian as something like the Microsoft or Google of the nascent player piano market. Anticipating that Congress would overturn White-Smith Publishing, Aeolian swiftly bought up song rights from musicians and publishing companies so it could copy them onto player piano rolls. Aeolian’s weaker competitors complained to Congress about this attempt to corner the music market. So to keep the Aeolian Co. from having a monopoly on the then-crucial player piano roll market, Congress allowed anyone to make a mechanical reproduction of someone else’s song. And that doesn’t just mean that Aeolian’s competitors could make their own piano rolls of popular tunes. Congress’s compulsory licensing scheme legalized the cover song—anyone can make their own recording of someone else’s song, just so long as a recording of that song has previously been released, and the cover artist pays the required fee. (In practice, most cover artists pay privately negotiated rates that are even lower than the fee set by Congress). And the freedom to cover others’ songs gave birth to a vibrant culture of continuous musical revival, remaking and reinterpretation, all of which allows good songs to become great and, sometimes, we must admit, classics to be butchered. It’s all in the eye (or ear) of the beholder. But the important point is that because of Aeolian’s dominance of a now-defunct technology, we have a musical culture in America in which musicians are free to tweak songs they like—and they do so with great enthusiasm. Bob Dylan wrote “All Along the Watchtower”; Jimi Hendrix turned it into something quite different and, arguably, made a great song even greater. Another 1960s classic, Van Morrison’s “Gloria,” has been covered by performers including Jimi Hendrix, the Doors, David Bowie, Tom Petty, Bruce Springsteen, Rickie Lee Jones, AC/DC, and (in perhaps the song’s most memorable and inventive reinterpretation) Patti Smith. “Gloria” is an enduring song in part because so many legendary musicians have tweaked it. Cat Power, John Lennon, Willie Nelson, Paul Anka, and many other famous artists have issued albums of nothing but cover songs. All this legal copying has made our musical culture immeasurably richer. Has the freedom to copy others’ songs, in exchange for a very low fee that the original songwriter has no power to override, suppressed the incentive to write new songs? There’s no evidence of that. Indeed, the opposite is true. Every day we see a continual outpouring of new songs. And many of the copied songs—the covers—are themselves incredibly creative. Think about jazz greats like Charlie Parker and John Coltrane. On one level, they are copying—Coltrane’s version of the Rodgers and Hammerstein standard “My Favorite Things” relies on that song’s famous melody. But if Coltrane starts there, it’s certainly not where he ends up. By the song’s end, more than 13 minutes later, Coltrane has altered the original melody and taken it in a much darker, more contemplative direction. At some point in the song, Coltrane crosses the uncertain border that separates copyists from creators. This kind of creative copying—what we will call “tweaking”—is not just a musical thing. It is present in all inventive fields. Perhaps the most important point about tweaking is this: Tweaking does not appear to lead to fewer original creations. If anything, it may often encourage them. Many of the most significant and enduring innovations rest on tweaking. As Malcolm Gladwell has argued, the late Steve Jobs of Apple—an icon of our innovation economy if there ever was one, and the man behind the iPhone and iPad—“was repeatedly referred to as a large-scale visionary and inventor.” But in fact, “he was much more a tweaker.” Jobs, Gladwell goes on to argue, was “the greatest tweaker of his generation.” Even the iPad, Jobs’ last great success, was a tweak of an idea out of Microsoft. And Gladwell rightly points out that the significance of tweaking to technological innovation is by no means a new thing: Economists debating the origins of the industrial revolution have claimed that the key reason Britain, and not France or Germany, was the first home of the industrial age was tweaking. As Gladwell describes their argument, Britain was not necessarily the home of path-breaking pioneers who created the foundational building blocks of the Industrial Revolution. Rather: Britain dominated the industrial revolution because it had a far larger population of skilled engineers and artisans than its competitors: resourceful and creative men who took the signature inventions of the industrial age and tweaked them—refined and perfected them, and made them work. In creating the cover song, the Congress of a century ago had no idea it would let loose one of the great innovative forces in music. It was just trying to repair a blunder by the Supreme Court and ensure that the giant Aeolian Co. didn’t swallow the entire player piano market. Today, the player piano market is minuscule, and Aeolian has been dead for almost three decades. But the lesson of Aeolian lives on. Copying and creativity are not opposites—they are often complements. Since there is nothing new under the sun, every creator relies, sometimes to a large degree, on what came before. This should be obvious, but too often it isn’t. Copying is viewed as a terrible thing, a crime and a killer of creativity. To be sure, copying can have bad effects. But as the Aeolian story shows, it can also be the key to new forms of creativity we never could have imagined.