7.1 Agency and Responsibility

Our argument turns on the concept of responsibility. For a person to launder his or her agency requires that he or she be a moral agent in the first place, and being a moral agent requires that one be in some sense morally responsible. In this section, we first distinguish several facets of responsibility and how they relate to one another, which helps structure our understanding of agency laundering in the following section. Then, we offer a substantive account of responsibility, which will ground our understanding of the moral wrongs associated with agency laundering.

7.2 Agency Laundering

With our discussion of responsibility in mind, we can return to the chapter’s central argument. Using an automated process to make decisions can allow a person to distance themself from morally suspect actions by attributing the decision to the system, thereby laundering their agency. Put slightly differently, invoking the complexity or automated nature of a decision system to explain an outcome allows a party to imply that the action is something for which they are not morally responsible.

Compare money laundering.Footnote ¹⁸ Where one has such large amounts of illicit cash that spending it or placing it into legitimate financial instruments would be suspicious, one can launder it by mingling it with other, legitimate streams of income so that the illicit cash appears legal. For example, one might add the illegal cash to money received in a legal, cash-dependent business.Footnote ¹⁹ The bad thing (income from an illicit source) is hidden by the existence of some other, similar phenomenon. To be clear, we are not making an argument by analogy; decisions are not like cash. Rather, the point is that it is possible to obscure the source of responsibility for actions and make them appear unsuspicious by mingling them with other actions.

Consider a minor example (“Chair”). Suppose that Cheese State University vests department chairs with control over curriculum. A chair and several members of their department would like to get rid of phlogiston studies (“P-studies”) because they think it is unimportant. The chair could do this unilaterally by removing courses, reassigning instructors, and altering degree requirements but wants to avoid the wrath of the department phlogistologists. The chair therefore delegates curriculum decisions to a committee of people who they know want to eliminate P-studies. When P-partisans complain, the chair responds that it was the committee’s decision, though the chair knew from the beginning what that decision would be. By impaneling a committee to ensure the results the chair wanted, the chair obscures their own role in the decision. The committee appears to be the relevant power, though it remained the chair.

There are several features of Chair to address initially. First is that the chair had legitimate institutional authority to make the decision, and if they had moved to eliminate P-studies unilaterally it would have happened. The chair’s institutional authority is a form of role responsibility for their department’s curriculum. The chair has the responsibility to anticipate educational needs, department resources, student demand, scholarly trends, and so forth, and to ensure that the department’s offerings adequately address them. And the chair’s de facto power to alter the curriculum is a form of causal responsibility; when the curriculum changes, the chair’s actions are an essential part of the explanation why.

Second, although the chair has power to make the decision, they draw in a separate body by giving the committee some degree of causal responsibility. Because the curriculum change would not occur without the committee’s work, the committee is an essential part of the explanation for the curriculum change. It is not the only cause, as it is mixed with the chair’s actions. Third, when the chair forms the committee, they imply it is neutral, would weigh evidence fairly, and might act in a way that the chair doesn’t anticipate. But that’s a ruse – ex hypothesi, the chair knows that the committee will act just as the chair wishes.

Fourth, the chair’s actions obscure their causal responsibility with respect to the curriculum. The chair is able to obscure the fact that they orchestrated the result by making the committee partially causally responsible (i.e., a key part of the explanation) for the result. Fifth, although the chair appears to fulfill their responsibility in shepherding the curriculum, appointing the committee obscures the chair’s designs to eliminate P-studies.

The following is a definition of agency laundering that incorporates these features of Chair. An agent (a) launders their agency where

1. (1) a is morally responsible with respect to some domain X, and

2. (2) a ensures that b (some process, person, or entity) has some causal responsibility with respect to X, and

3. (3) a ascribes (implicitly or explicitly) morally relevant qualities to b’s actions (e.g., relevance, neutrality, reliability), and

4. (4) in virtue of (2) and (3), a obscures the scope of their causal responsibility with respect to X, and

5. (5) in virtue of (4), a fails to adequately account for events within X for which they are morally responsible.

This definition only gets us so far. It sets out the structure of agency laundering, which tracks and incorporates the structure of moral responsibility from Section 7.1.1. However, it does not explain the moral problem of agency laundering itself (if there is one). That’s our next task.

There are several ways in which the chair may have acted wrongly. One possibility is that it is unjustifiable to eliminate phlogistology in any case. But let’s leave that aside and assume that it’s permissible to eliminate it based on its substance and the context. More important is that the chair’s ascription of morally relevant qualities to the committee is misleading, and they have therefore deceived people about the process involved. Regardless of whether getting rid of P-studies is justifiable, the chair’s obscuring their reasons and intentions in impaneling the committee do not appear justifiable. Others with whom the chair has a relationship have a claim to understand such an important facet of their professional lives.

A still deeper moral problem is that the chair’s action allows them to avoid the core demand of responsibility, which is to provide an account. Regardless of whether the chair is meeting their role responsibilities with respect to the curriculum, they are forestalling others’ ability to demand an account for the chair’s actions within a domain of their legitimate concern.Footnote ²⁰ This is the defining feature of agency laundering, and it turns on the substantive account of responsibility given in Section 7.1.2. There, we explained that responsibility is first about social relations. We hold others responsible for their actions in part by forming reactive attitudes, and such reactive attitudes are key in understanding responsibility. However, our view is that moral responsibility is also a matter of whether agents are open to demands to justify their actions and whether it is appropriate for others to demand an account of their reasons and intentions.

Now we come full circle. Agency laundering involves a kind of misdirection (as in (2)–(4)). But, crucially, the misdirection undermines others’ ability to demand reasons for an agent’s actions. In other words, the laundering part of agency laundering cuts straight to the heart of what responsibility is by undermining the ability of others to ask the agent to provide an account.

Department members will be unable to ask the chair for their reasons and intentions in eliminating phlogistography, because the chair’s actions look like formation of a committee that (apparently) deliberated about and then eliminated the subfield. Department members would reasonably believe that all the chair has to provide is an account of delegation to the committee. But an account that focused on the committee would not be an account of the chair’s actual reasons and intentions, which are about engineering an outcome, not initiating a process to weigh things.Footnote ²¹

It is worth explaining the role of condition (5) a bit further. What matters about (5) is that it distinguishes cases like Chair from structurally similar cases of delegation. Consider a variation in which the chair thinks P-studies should be eliminated, and they know that there are so few P-sympathists that any full committee will have a majority of P-eliminationists. Nonetheless, the chair delegates the curriculum decision to a committee because of their commitment to inclusive, democratic department governance. As in Chair, (1)–(3) obtain. And (4) plausibly obtains, as the chair’s causal responsibility in forming the committee may obscure their causal role in deciding to review P-studies. But the key difference is that the committee formation in “Democratic Chair” is not a sham, constructed so that the chair can avoid having to account for their actions regarding the curriculum. Just as in the original example, department members will reasonably believe that the action for which the chair should provide an account is the formation of the committee. But in democratic chair, that is the only action for which the chair should provide an account.

So that’s the account. Let’s turn to some cases. These will help us understand how predictive, automated decision systems can launder agency.

7.3 Facebook and Anti-Semitic Advertising

In 2017, ProPublica published a report detailing an investigation into Facebook-targeted advertising practices.Footnote ²² Using Facebook’s automated system, the ProPublica team found a user-generated category called “Jew hater” with more than 2,200 members. While two thousand Facebook users choosing to identify as “Jew hater” in their profiles seems like a lot, Facebook’s platform helpfully informed the ProPublica team that it was too small an audience for an effective ad buy. To help ProPublica find a larger audience (and hence have a better ad purchase), Facebook suggested a number of additional categories. For example, it suggested including the category “Second Amendment,” presumably because of some overlap in users’ choices of interests in their profiles. ProPublica used the platform to select other profiles displaying anti-Semitic categories, and Facebook approved ProPublica’s ad with minor changes.

Facebook’s platform also allows clients to target ads by excluding profiles by age, geographic, and race and ethnic categories. For example, advertisers can target users in specific places and income ranges while excluding people with specific “ethnic affinities.” Many of these affiliations are generated automatically, based on content users and their friends have liked or shared. In some cases it is not the category that creates a problem, but the purpose of the ad. Targeting an ad by age makes sense in some contexts (life insurance, toys) but is discriminatory in others (job recruitment).Footnote ²³

When ProPublica revealed the anti-Semitic categories and other news outlets reported similarly odious categories,Footnote ²⁴ Facebook responded by explaining that algorithms had created the categories based on user responses to target fields (e.g., answers to questions about education and hobbies). It also pledged to address the issue. But Facebook was loath to claim it had responsibility. Chief Operating Officer Sheryl Sandberg claimed in a public response that “[w]e never intended or anticipated this functionality being used this way.”Footnote ²⁵ That is no doubt true, though Facebook wishes to both sell advertising and employ as little labor as possible to monitor how that advertising functions.

Is it agency laundering? An agent (Facebook) launders its agency where

1. (1) Facebook has moral responsibility with respect to targeted advertising, and

2. (2) Facebook ensures that its algorithmic advertising process has some causal responsibility with respect to targeted advertising on its platform, and

3. (3) Facebook ascribes morally relevant qualities to its algorithmic advertising process’s actions, and

4. (4) in virtue of (2) and (3), Facebook obscures the scope of its causal responsibility with respect to targeted advertising on its platform, and

5. (5) in virtue of (4) Facebook fails to adequately account for events within a domain for which it is morally responsible: specifically, the way in which its advertising platform helps target advertising to racists.

Each of these conditions appears to obtain. Certainly, Facebook has causal responsibility with respect to targeted advertising on its platform. A more difficult question is whether Facebook has role, or prospective, responsibility. The clearest sense in which they have role responsibility is that they have de jure authority over their platform, and they have a general responsibility to be good members of the broad community of people who use the platform. More specifically, they have (in our view) a specific responsibility to ensure that their platform does not facilitate racists to easily find an audience to whom they can advertise.

The claims that Facebook has such specific moral responsibilities will no doubt be controversial. Others may argue that Facebook has a moral responsibility to be a mere conduit of communication among members.Footnote ²⁶ That is unconvincing for a couple of reasons. For one, this case is about advertising. Any claims about how Facebook should structure information between end users tell us nothing about Facebook’s responsibility vis-à-vis advertisers. Moreover, Facebook already acts as if it has responsibilities with respect to both content and advertising. It has community standards, by which it judges and removes content, and it restricts certain kinds of advertising.Footnote ²⁷ In any case, agency laundering only requires that Facebook have general responsibilities within this domain.

Facebook’s categories are derived in part by automated systems. It takes a hands-off approach, letting users generate profile information, letting an algorithm pick out characteristics from user profiles, letting advertisers peruse those categories, and letting an algorithm suggest compatible categories to build better ad target groups. Thus, Facebook ensures that an algorithmic process has causal responsibility (i.e., is a key part of the explanation) for what ads appear to whom on Facebook’s platform. That’s condition (2).

Facebook’s business model includes allowing advertisers to target groups of people narrowly and effectively. It does this in a way that avoids the labor costs associated with human approval of ad targets or human oversight of ad categories and purchases. In so doing, Facebook implies that its algorithmically generated categories and suggestions are relevant to advertisers (otherwise, advertisers would have no reason to purchase ads). And the fact that one can place ads based on those categories without oversight implies that Facebook believes (at least implicitly) that whatever ads served to whatever audience are appropriate. These are morally relevant qualities, as per our third condition. The algorithms’ causal responsibility and implication that they are appropriate obscure the scope of Facebook’s causal responsibility (condition (4)).

Finally, in automating its advertising process, Facebook is able to claim that it “never intended or anticipated this functionality being used this way.” It effectively distances itself from the fact that a system for which it is (causally and morally) responsible allows noxious (and in the case of discriminatory categories, illegal) advertising. That is, the causal responsibility of the algorithm’s suggestions deflects from Facebook’s causal responsibility in creating a platform that uses the algorithm, minimizes the labor that would be required to better monitor advertising categories, and profits from the automated system. Its attribution of morally salient characteristics (relevance, usefulness) presupposes that its optimization is consistent with Facebook’s responsibilities, though it is not.

Here is where understanding Facebook’s actions as agency laundering is a difference-maker. Conditions (1)–(4) describe several important moral features. But the crux of laundering is condition (5). The fact that Facebook is morally responsible with respect to targeted advertising means that it is appropriate to demand that Facebook provide an account of its intentions and reasons in facilitating racists in easily finding an audience to whom they can advertise. Facebook has inserted an automated procedure into its advertisement purchasing procedure, and it suggests that the algorithms are the natural object to scrutinize rather than Facebook’s reasons and intentions with respect to building a system that deploys them and lets them run with minimal supervision. In doing so, Facebook undermines the central feature of responsibility by deflecting demands for an account of Facebook’s reasons, intentions, and actions in helping racists target advertise. Hence, the automated process is a mechanism by which Facebook launders its agency.

There are several potential rejoinders to our argument here. One might disagree about what Facebook’s responsibilities are. One might argue instead that it is advertisers and users who bear responsibility for populating Facebook’s categories with racist characteristics. Certainly, it is true that users populating categories with anti-Semitic and other racist ads bear responsibility for those actions, and any advertiser targeting ads based on such categories bears responsibility for doing so. But, as in Chair, that others have acted wrongly does not tell us much about Facebook’s responsibility. One might further argue that Facebook has not laundered its agency because it has agreed to address the problem. But the fact that Facebook has indicated an intention to address these problems demonstrates that it is a problem within Facebook’s control.

A related objection concerns the degree, or the severity, of Facebook’s failure to fulfill its responsibilities (assuming that it has some). Perhaps Facebook knew of problems in how its algorithms functioned to allow malignant actions. But perhaps instead it was merely negligent.Footnote ²⁸ This is no doubt an area others will reasonably dispute. It does not matter for our analysis of laundering, though. Facebook laundered whatever degree of agency it had. Moreover, it can launder its agency even if it meets its substantive role responsibilities. That the advertising platform afforded the opportunity to target advertising in a racist way is something for which detailed explanation of intentions, reasons, and actions is warranted.

Another potential objection is that it may well be that no particular Facebook contractor or developer acted with discriminatory intent, alleviating any potential moral responsibility any of them might have for the outcome.Footnote ²⁹ However, Facebook’s role responsibility is not reducible to any particular individual developer within Facebook. Rather, the company’s responsibility is better understood as widely distributed across its contractors, employees, and other stakeholders. Moreover, Facebook’s platform is complex, and the consequences of its operations over time are impossible to predict. Coeckelbergh and Wackers argue that organizations deploying such complex, vulnerable systems have obligations to manage their operations not only legally, but with a certain positive “imagination” regarding systemic crises or other harms.Footnote ³⁰ In other words, it is unjustifiable to simply let such complex systems run their course and cause harm.

There is a further, related question about whether the conception of responsibility we have outlined here is properly attributable to collectives. There is significant philosophical debate about collective responsibility, and we cannot do it justice here. But we can note two things. First, the accounts of responsibility we outline in Section 7.2 need not be limited to individual wills. Certainly, we do have reactive attitudes toward collections of people, and those targets may be apt. Further, it seems plausible to attribute reasons to groups, in which case it seems plausible that such a group may be responsible in the sense that it ought to be the case that the collective be accountable. Second, even if it is the case that a collective’s responsibility is reducible to the responsibility of its individual members, this would imply that those individuals have laundered their agency. In any case, whatever responsibility there is, Facebook’s reliance on algorithms to do work and to explain its failures is (on the conception outlined here) an instance of agency laundering.

7.4 Uber and Driver Management

Another private-sector example shows how our concept of agency laundering can distinguish between structurally similar cases. The ride-hailing company Uber has received substantial social, regulatory, and academic criticism based on its AI-driven, algorithmic systems. Uber uses such systems to map routes, track passengers, monitor drivers, anticipate demand, steer driver behavior, and (at one point) identify and deceive regulators. Many of these uses have been criticized elsewhere on the grounds that they are deceptive, unfair, opaque, or even illegal.Footnote ³¹ Our task here, though, is to consider whether any are instances of agency laundering and, if so, whether analyzing them as agency laundering sheds light on moral concerns with Uber’s practices.

Recall two issues that we touched on in Chapter 5: how Uber uses algorithmic systems to keep its drivers working. One way is by providing reminders of individual drivers’ goals. For example, the Uber app might display a message that the driver is very close to their goal of earning $50 for their shift, which may induce them to take more riders. Similarly, Uber at times sends drivers their next ride requests before they have delivered their current rider. This creates a kind of “queue effect,” much like video platforms that keep people watching by immediately starting the next episode of a series.Footnote ³² A number of critics – including drivers – object to these practices on the grounds that they rely on nonrational mechanisms or are manipulative.Footnote ³³

Another way that Uber gets drivers to keep working involves the prospect of dynamically priced fares, which allows drivers to increase their per-hour earnings by driving during high-demand/low-supply periods. When there are lots of passengers seeking rides and relatively few drivers working, Uber will charge higher (surge) prices and drivers thus earn more. Uber’s driver app will often prompt drivers to work at times that Uber anticipates will be high demand. So, it might say that, for example New Year’s Eve will probably have surge pricing.Footnote ³⁴ However, such prompts do not guarantee surge pricing, and drivers do not know when they accept a ride whether it will be surge-priced. In some cases, the Uber app estimates surge pricing, but fares during that period are normal, either because demand does not materialize or because enough drivers are working to offset the demand.Footnote ³⁵

These two cases are structurally similar: app-based mechanisms that prompt drivers to work somewhat more than they would have otherwise. Recall our argument that only surge pricing interferes with people’s valuable freedoms. Relatedly, only the surge pricing appears to be a case of agency laundering. To see this, begin by running both through our understanding of laundering.

1. (1) Uber has moral responsibility with respect to fielding drivers, and

2. (2) Uber ensures that its algorithm has some causal responsibility with respect to fielding drivers, and

3. (3) Uber ascribes morally relevant qualities to its app-based prompts to drivers, and

4. (4) In virtue of (2) and (3), Uber obscures the scope of its causal responsibility with respect to fielding drivers, and

5. (5) In virtue of (4), Uber fails to adequately account for events within a domain for which it is morally responsible: specifically, the way in which its interface induces driving.

Conditions (1) and (2) are clear enough. Although Uber claims to be a technology company merely connecting riders and drivers through a platform, it nonetheless plays a large role in getting people to both drive and ride. It enters into contractual relationships with drivers and riders, maintains standards for drivers and equipment, subjects drivers to background checks, adjudicates disputes, and so forth. And there is no question that its algorithms are a key part of the explanation of which drivers are driving when. Uber ascribes morally relevant features to the algorithms: that they reflect drivers’ own goals, that they are reliable, that they are based on a neutral assessment of facts on the ground (condition (3)).

The differences in the cases concern conditions (4) and (5).

Begin with the case of goal-reminders and queuing effects. It is difficult to see how Uber obscures its causal responsibility in incentivizing driving when it uses these tactics to spur drivers into taking more rides. Certainly, Uber is drawing on (or even exploiting) behavioral psychology, and behavioral psychology is an essential part of the explanation of drivers’ decisions to drive. But that fact, and the fact that Uber has set up a system in which algorithms instantiate such strategies, is not obscured.

Now consider condition (5). Here, too, it is difficult to see how Uber fails to adequately account for events within a domain for which it is morally responsible. As a provider of ride-hailing services, Uber has an interest in keeping enough drivers on the road, and it is using a straightforward tactic to promote this interest. Further, Uber has been clear about the practice. In a recent New York Times article, a spokesperson for Uber describes goal-reminding and queuing as ways to incentivize driving.Footnote ³⁶ As far as we can tell, Uber does not launder its agency when it uses goal-reminders and queuing.

One plausible counterargument here is that, at least in extreme cases, using such tools undermines drivers’ wills so much that it obscures the scope of Uber’s causal responsibility. Perhaps the interface is sufficiently gamified that users have hallmarks of addiction, or perhaps the quality of drivers’ wills is so degraded that decisions to drive do not count as drivers’ own. In that case, Uber’s causal responsibility would be far greater than it appears and any adequate account of Uber’s responsibility would include an explanation of how it circumvents drivers’ wills. That possibility is worth both empirical and philosophical examination. Nonetheless, at least weaker forms of nudging seem well within the range of responsible employer behavior and not cases of agency laundering.

Contrast the goal-reminders and queuing with the surge-pricing case. Uber uses machine learning techniques to predict high-demand/low-supply times and uses those predictions to prompt drivers to work. This does seem to obscure Uber’s causal responsibility in fielding drivers, per condition (4). The judgment that surge pricing is likely to occur appears to be an inference about how the world outside Uber is operating, and Uber is merely reacting to it. Indeed, in comments pertaining to the phenomenon of surge pricing, Travis Kalanick (Uber’s cofounder and former CEO) said, “We are not setting the price. The market is setting the price.”Footnote ³⁷ In fact, Uber is causally responsible for setting up a system in which there are pay differentials (where driving at surge times is more attractive to drivers) and then using those facts to induce driving. Surge periods are not a natural feature about the world that Uber measures, but a period defined and deployed by Uber.

More important, though, is condition (5). By predicting surge pricing and signaling the likelihood of surge pricing to drivers, Uber simultaneously exploits surge pricing and makes it less likely. In other words, by using surge pricing as an inducement to drivers seeking a better wage, Uber helps ensure that supply matches demand more closely. Thus, it creates for drivers reasonable expectations of better pay and fails to meet them. Then, when the prices are not offered, Uber tells the drivers it is the market that is making the decision, not Uber. But this is a failure to account for the situation a driver finds themself in when, for example, they have driven to Times Square on New Year’s Eve under Uber’s advice that there will be a surge and then find that the surge has disappeared. As Alex Rosenblat observes, “When drivers follow this advice and find that they have been dispatched to pick up a passenger for a nonpremium-priced ride, meaning that surge pricing has disappeared, they feel tricked.”Footnote ³⁸ It is not the market that reached out to the driver to quell the surge. It is not the market that decided how Uber’s payment system works. Rather, it is Uber that sets up a system where a driver who responds to its enticements may not get surge rates if the campaign to get drivers to an area has worked. And this is the action within its domain of moral responsibility for which Uber owes an account. Uber’s claim that it is simply the market’s doing is an inadequate account, satisfying condition (5). Thus, Uber launders its agency.

So our conception of agency laundering is sensitive enough to distinguish between different uses of algorithmic systems to influence driver behavior. The next question is whether analyzing each in terms of agency laundering adds something of value beyond simply analyzing Uber’s responsibilities to its employees. We believe that it does. The laundering analysis emphasizes the fact that use of tools (committees, bureaucracies, technologies) may be a way to simultaneously violate duties and undermine accountability. This is a way to show that laundering adds something. By calling it laundering, we can make clear what is happening. But more importantly, our argument picks out a discrete moral infirmity, viz., eroding others’ ability to demand Uber provide an account of its reasons and intentions. In Uber’s case, use of a tool to both predict surge pricing and induce drivers looks similar to use of other prompts. One might be tempted to think of it as a case of nudging (or perhaps of manipulation). But that would miss the fact that by tying the process to a prediction about facts on the ground, Uber can deflect attention from its own responsibility for creating a situation in which it simultaneously predicts surge pricing and makes it less likely.

7.5 VAMs and Teacher Evaluation

Facebook, Uber, and other large technology firms receive substantial attention. It would be a mistake, however, to think that agency laundering is primarily the province of the private sector. The depth and importance of agency laundering may be even greater in public agencies. To demonstrate, we will reexamine Houston Schools’ use of EVAAS. Recall that EVAAS is in practice not auditable for two interrelated reasons. First, all of its scores are so deeply interconnected with other scores that the only way to recalculate a score is to recalculate them all. Second, recalculating all of the scores is a complex task and thus very costly.

Despite its shortcomings, Houston Schools defended its use of EVAAS on the grounds that it reliably measures student progress.Footnote ³⁹ But this claim, even if true, is largely irrelevant to the question of whether the use of EVAAS is justified. Student progress and the contributions an individual teacher makes to student progress are distinct quantities. To measure one is not to measure the other. Indeed, the American Statistical Association (ASA) issued a statement in 2014 pointing out that most studies conclude that teachers have only a marginal effect on the test scores that VAMs (such as EVAAS) take as inputs.Footnote ⁴⁰ So, even if EVAAS reliably measures student progress, this is a poor proxy for teacher effectiveness.

Has the school district laundered its responsibility for firing teachers? We think so.

1. (1) Houston Schools is morally responsible with respect to hiring, firing, and promoting teachers, and

2. (2) Houston Schools ensures that EVAAS has some causal responsibility in making those determinations, and

3. (3) Houston Schools ascribes morally relevant qualities to EVAAS, and

4. (4) In virtue of (2) and (3), Houston Schools obscures the scope of its causal responsibility with respect to hiring, firing, and promoting teachers, and

5. (5) In virtue of (4), Houston Schools fails to adequately account for events within a domain for which it is morally responsible: specifically, the “exiting” of teachers deemed ineffective through EVAAS.

In virtue of Houston Schools’ role as an employer, the first condition is met. When Houston Schools implements EVAAS to aide in personnel decisions it meets the second condition. Houston Schools meets the third condition implicitly by using EVAAS for high-stakes decisions. It meets the third condition explicitly by invoking EVAAS’s reliability in measuring student progress as a reason in favor of using EVAAS. The fourth condition is met when Houston Schools repeatedly refers to one good thing that EVAAS does (measure student progress) to obscure the fact that Houston Schools is implementing a system in which teachers are fired based on measures for which the teachers are not responsible (recall the statement from the ASA mentioned earlier). Finally, the fifth is met because teachers who are fired on account of their EVAAS scores are given a faulty accounting of why they were fired. They are told they are being fired for being ineffective, when, given EVAAS’s flaws, this is likely not the case. Hence, understanding Houston Schools’ actions as laundering shows us that there is something going on beyond lack of transparency; the mechanism of evaluation positively misdirects those who would seek reasons for how teachers are treated. It forestalls teachers’ ability to demand an account for the school district’s actions, within a domain of their legitimate concern.

Note that Houston Schools uses EVAAS while fulfilling its public function of managing an education system. That means that the public has a collective stake in how the system functions and has an interest in the actions Houston Schools undertakes. Hence, the fact that Houston Schools’ laundering makes accountability all the more difficult matters in a way that accountability of private firms does not; it suggests that use of EVAAS must conform to standards of public reason, rather than aligning only to the isolated wishes of the district.Footnote ⁴¹

7.6 COMPAS and Criminal Sentencing

So far, we have described agency laundering in both private-sector and public-sector cases. And in the Uber case we saw how use of algorithmic decision systems will not be agency laundering where the agent does not obscure their causal responsibility for outcomes. We will return to Loomis to demonstrate how a public entity can avoid agency laundering by making clear their moral responsibility for an outcome.

Loomis and COMPAS have been the subjects of significant criticism. However, we think that the Wisconsin Supreme Court’s opinion in the case shows how actors can avoid agency laundering in deploying algorithmic systems. Hence, the case shows that our understanding of agency laundering is not so broad as to be meaningless.

A trial court launders its agency where

1. (1) the trial court has moral responsibility with respect to sentencing, and

2. (2) the trial court ensures that COMPAS has some causal responsibility with respect to sentencing, and

3. (3) the trial court ascribes morally relevant qualities to COMPAS, and

4. (4) in virtue of (2) and (3), the trial court obscures the scope of its causal responsibility with respect to sentencing, and

5. (5) in virtue of (4), the trial court fails to adequately account for decisions pertaining to sentencing, specifically the decision to sentence Loomis in the maximum range.

The trial court certainly has moral responsibility with respect to sentencing. But did the trial court ensure that COMPAS had some causal responsibility with respect to sentencing? The judge referenced Loomis’s risk scores and they plausibly had an effect on sentencing. This, though, was only one of the factors the judge described. He also considered important the conduct Loomis admitted as part of the read-in charges and Loomis’s conduct while under prior supervision. Let’s interpret this as giving some degree of causal responsibility to COMPAS. It is, after all, at least plausible that the COMPAS score is a key part of the explanation for Loomis’s sentence. The court’s use of COMPAS implies that it is useful, reliable, and fair, which are morally relevant qualities, per condition (3).

The question of agency laundering in Loomis turns on conditions (4) and (5). Although the judge in the case referenced the COMPAS assessment in his decision, he also indicated that his own judgment (based on Loomis’s conduct and history) led him to a similar conclusion. There is some possibility that the judge was confabulating by ascribing his own reasons to the outcome COMPAS reached. If that’s true, it would not be that the use of COMPAS obscures the trial court’s causal responsibility. Rather, it would be that the court’s description of its reasons obscures the scope of COMPAS’s causal responsibility.

The key issue, though, is whether the trial court fails to adequately account for decisions pertaining to sentencing. Consider the following from the Wisconsin Supreme Court’s decision.

The passage makes clear that tools like COMPAS cannot be used alone, and use of such scores has to be supported by other factors that are independent of the tool. Similarly, the court required that courts weigh all relevant factors in order to sentence an individual defendant,Footnote ⁴³ and it prohibited trial courts from using scores to determine whether to incarcerate a person or not, to determine the length and severity of sentence, and to determine aggravating or mitigating factors in sentencing.Footnote ⁴⁴ And the court required that any PSI that uses a COMPAS report carry a number of warnings about the limitations of such reports.

The supreme court’s Loomis opinion places responsibility squarely on the trial court in using tools like COMPAS. It prohibits trial courts from relying completely on the COMPAS algorithm, and it requires trial courts to use other factors to support any use of risk assessment algorithms. Hence, the court forecloses the ability of trial courts to use algorithms as a way to distance themselves from responsibility. Thus, Loomis addresses condition (5), and it is not a case of agency laundering.Footnote ⁴⁵

Deploying tools like COMPAS could certainly be a means by which courts (and others in the criminal justice system) can launder their agency. However, the Loomis decision is tailored precisely to avoid that. Hence, it appears to be a good test case for our view. It is the use of an algorithmic system where one does not launder their agency. As a result, it can demonstrate how other actors may fail in their moral responsibilities, even where their actions superficially resemble the Loomis court’s. The court did not forestall others’ ability to demand an account for its actions within a domain of their legitimate concern, but a different court (or different actor within a criminal justice system) might do so by failing to provide its own reasons for decisions.

7.7 Related Concepts and Concerns

7.8 Conclusion

Our goals in this chapter were to, first, explain a type of wrong that arises when agents obscure responsibility for their actions. We have outlined this type of wrong and called it “agency laundering.” Second was to draw on several cases to help specify our account of agency laundering. We have argued that some of these (Facebook advertising, Uber’s surge-pricing prompts, Houston Schools’ use of EVAAS) involve laundering and two (other Uber prompts, use of COMPAS in the Loomis case) do not. Third, we have argued that analyzing these cases in terms of agency laundering both helps understand the cases and adds something morally. Lastly, we have distinguished agency laundering from other relevant concepts.

We have not given the final word on agency laundering here. One further question concerns the degree to which laundering must be intentional. In other words, can a person who uses a tool to make decisions launder their agency inadvertently? This appears compatible with our definition of agency, though the moral importance of such laundering warrants further consideration. Another question concerns how widely the concept of agency laundering applies. A few people with whom we’ve discussed this project have asked whether large-scale social processes (e.g., political events and movements) can serve either as mechanisms or as sites of laundering. Perhaps so, though that would involve sorting through complex issues of causal responsibility and conceptual questions of capacity responsibility.

8.1 Two New Technologies

In 2011, UCLA anthropology professor Jeff Brantingham launched PredPol, which is an algorithmic system for predicting and preventing crime. The idea at the heart of PredPol is that crimes follow predictable, geographic patterns.Footnote ¹ Thus, it may be possible to reduce crime by deploying police resources to places where predictive analytics suggests crime will occur. To facilitate this, PredPol has a graphical interface that overlays a red box (indicating a predicted crime “hotspot”) on top of a city map. This allows police to concentrate on those areas in the hopes of deterring crime.

There is evidence that PredPol is effective in some respects, especially in addressing property crime. And it has a degree of support. Time magazine called predictive policingFootnote ² one of the “50 Best Inventions” of 2011.Footnote ³ Today, PredPol is a national leader in predictive policing technology and dozens of cities across the United States use it.Footnote ⁴

There are, however, several important criticisms of the technology. One is that its ability to predict property crimes may not translate well to predicting (much less preventing) other crimes. Another concern is that predictive policing in general simply recreates biases in policing practices and that predictive tools will be deployed in ways that harm communities of color, much in the same way that stop-and-frisk policies, pretrial detention, and sentencing do.Footnote ⁵

Not long after PredPol was getting off the ground, a different set of algorithmic systems was developing in a way that would have profound implications for electoral politics. The political data analytics firm Cambridge Analytica began building specialized personal profiles and using large datasets collected from social media to stage political influence operations at scale. While use of profiling and data to influence politics is nothing new, Cambridge Analytica’s actions are novel in several ways. The data on which their operation was built was massive and obtained in intrusive ways (including by gaining access via friends’ permissions in Facebook). In addition, the data was collected under the imprimatur of a prestigious academic institution (Cambridge University). Finally, the kinds of information pushed to users were difficult for others to see, as it was targeted on social media. This left fewer obvious trails as it was happening than, say, television or mail ads. In parallel with the Cambridge Analytica efforts was a Russia-sponsored disinformation campaign, also making use of social media and recommendation systems. This was carried out by an organization called the Internet Research Agency (IRA).

Although PredPol and Cambridge Analytica/IRA address fundamentally different parts of modern life, they both have important implications for democratic governance. PredPol is a tool that helps structure how governments exercise their enormous power to investigate and sanction crime. The ability of the state to visit harms upon its constituents is at the heart of liberal theorists’ commitments to limit state power.Footnote ⁶ This concern about state-sanctioned harm is sharpened by concerns about unsanctioned state violence that is often unaccountable.Footnote ⁷ Cambridge Analytica, in contrast, is not primarily about how a state deploys its power. Rather, it is about the connection (or disconnection) between democratic processes and governance. Put differently, it is about the necessary conditions for democratic processes to provide grounds for governments to hold power and implement policy decisions.Footnote ⁸

Questions about justifying exercise of power via policing and questions about democratic processes and justifying power are, at root, about political legitimacy. So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (Chapters 3 and 4), in terms of the conditions required for people to act autonomously (Chapters 5 and 6), and in terms of the responsibilities of agents (Chapter 7). Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows: In Section 8.2 we will outline a conception of political legitimacy. In Section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems – among them PredPol and the Cambridge Analytica-Facebook amalgam – can hinder that legitimation process and conflict with democratic legitimacy, as we argue in Section 8.4. We will conclude by returning to our old friends, Loomis, Wagner, and Houston Schools.

8.2 POLITICAL LEGITIMACY: THREE CONCEPTIONS AND A HYBRID VIEW

Governments exercise enormous power over their denizens and expend vast resources promoting a range of policies (education, transportation infrastructure, defense, public health, commercial regulation, scientific research, information collection, parklands, safety and policing, and on and on). Those powers can be exerted in ways that are justifiable or unjustifiable, that are good or bad, and that are useful and not useful. The exercise of political authority can, in other words, be legitimate or not. In its broadest sense, political legitimacy refers to the justification of political authority, where “authority” just means having a certain political right to act coercively. But what constitutes such a right, and how could anyone ever acquire it? In this section, we discuss three conceptions of legitimacy – descriptive, democratic (or “will” based), and epistemic (or “normative authority” based) – and then offer our own, hybrid view based on a recent account from Fabienne Peter.

8.3 Legitimating Processes

As we explained in the previous section, there are two facets to normative legitimacy. One, characterized by “will” theories generally, is vulnerable to arbitrariness. The other is characterized by normative authority, which is in turn a function of justified belief in structures, policies, and actions of states that are in fact good. This second facet is not vulnerable to arbitrariness in the way that will theories are. That is because justification is baked into the definition. Any policy, structure, or action that is both correct and justifiably believed to be correct is, ipso facto, legitimate. The primary limitation of the normative authority facet of legitimacy is epistemic; determining what is in fact the right policy or action is difficult, to say the least (cf., Section 1.5).

When we consider technological threats to legitimacy – gaming of algorithms in social media, use of algorithms in predictive policing, for example – it is tempting to move straight from a conception of legitimacy to a claim that some set of institutions, policies, or structures are themselves illegitimate, all things considered. It will no doubt be true that some political actions will fail to meet both the will conditions and the normative authority conditions for legitimacy and those actions will thus be unjustifiable exercises of authority. However, for any particular policy taking place within a complex set of social and political structures against background facts that are themselves challenging to interpret, all-things-considered judgments about legitimacy will be difficult. Hence, it is a far larger task than we can address here. Moreover, making that kind of final judgment about legitimacy itself would take us too far afield from the subject of this book, which is about autonomy. Finally, legitimacy (like autonomy) is not a binary, success concept. It is not something that an institution, polity, law, or action simply has or lacks. There is a spectrum of legitimacy, and limitations based on will or based on normative authority will often diminish legitimacy without warranting a conclusion that a policy decision crosses some floor below which it is illegitimate.

For these reasons, our focus here is on legitimation.Footnote ²⁸ Will theories, normative authority theories, Peter’s disjunctive theory, and our hybrid interpretation of Peter’s view all rely on exercises of persons’ autonomy as a foundation for legitimacy of governments, laws, policies, and the like. That process of exercising autonomy, either through will or through justified beliefs about normative claims (or both), is the mechanism by which use of political authority is justified. And in turn, members of a polity have the responsibility to use their autonomy to understand and support (or not support) actions by their government.Footnote ²⁹ It is that process of legitimation that algorithmic systems can disrupt. In other words, algorithmic systems may in some cases hinder people from fulfilling their responsibilities of legitimating government policies, actions, laws, and the like.

Our argument for the centrality of autonomy to legitimacy begins with the conception of legitimacy outlined earlier. For political authority to be justified, it must be based on either democratic will or normative authority (i.e., where the exercise of political authority is good and there is justified belief that it is good), or a degree of both. As normative authority increases – that is, where policy is good and there is excellent epistemic justification for it – the degree to which it must be supported by mere will decreases.

Both paths to legitimate authority are based on a process of legitimation, each of which is itself grounded in autonomy. We explained in Section 8.2.2 that will-based theories are based on some version of agreement, public reason, or hypothetical consent and that Christman and Oshana explicitly link legitimacy to their conceptions of autonomy. The idea is straightforward. For one’s agreement to a system of governance and to the actions, policies, and decisions of a government to carry force, the agreement must be based on conditions that meet the criteria of autonomy and freedom. One must be epistemically competent, the reasoning upon which one’s agreement is based must satisfy authenticity conditions (as Christman puts it, one must not be understandably alienated from them). One should have the room to determine whether to support political decisions by having a degree of substantive independence. Moreover, people performing their legitimating responsibilities should be free of substantial impediments to the quality of their agency. As we explained in Chapter 5, one’s agency may be impaired by affective, deliberative, and social challenges. Where the quality of their agency is sufficiently challenged in those ways, their ability to fulfill the responsibility of legitimating government policies is limited.

The link between autonomy and legitimation by normative authority is not as intuitively clear as between autonomy and will theories. Normative authority requires, first, that a policy, law, or action by a government be a good one. But the mere fact that a policy is good cannot confer legitimacy because of what Peter calls the “access constraint.” Legitimacy demands that people believe that the policy is good and that they do so for sound reasons. Put another way, the legitimation process requires that people have sufficient information and understanding to assess a policy and believe that it is justified on its merits. The ability to do that is based on autonomy. To form justified beliefs about whether a policy is good, one must be epistemically competent, they must have some sense of value from which they are not alienated, and their reasoning must be substantively independent enough that their ability to reason is not compromised. Finally, the quality of their agency should not be so diminished that it undermines the degree to which their beliefs and actions are indeed their own.

8.4 Technological Threats to Legitimacy

Before we apply our arguments about legitimacy, legitimation, and autonomy to algorithmic systems, let’s summarize the conception of legitimacy that we have developed.

1. 1. For a government policy, law, or action to be normatively politically legitimate, requires either (a) normative authority, (b) democratic will (within certain bounds, dictated by (a)), or (c) some combination of both (a) and (b).

2. 2. For a policy, law, or action to be legitimate via normative authority requires that the policy, law, or action in fact be good, and meet the access constraint.

3. 3. The access constraint functions to ensure that autonomy enters the process by having belief-forming mechanisms in place under sufficient quality of agency.

4. 4. For a policy, law, or action to be legitimate via democratic will requires some process of voting or public reason.

5. 5. For a process of voting or public reason to confer legitimacy requires that persons engaged in that process do so autonomously.

With that in mind, let’s return to PredPol, Cambridge Analytica, and the IRA.

8.5 Once More Past the Pole

The issue of legitimation in the cases of PredPol, Cambridge Analytica, and the IRA is momentous. How police exercise their power to put others in harm’s way, efforts to affect an election via social profiling, and interference of a hostile nation in a US presidential election cut to the quick of democratic legitimacy. But issues of political authority and legitimation are not limited to matters at the heart of democratic procedures and at the far reaches of state-sanctioned exercise of power. They extend also to ordinary administrative tasks as well, among them risk assessment and teacher evaluation.

COMPAS raises similar issues to PredPol on the normative authority arm of political legitimacy. It is no doubt true that efficient allocation of scarce resources for supervising people in the criminal justice system is good policy. There are substantial open questions about that system overall, of course. But if we accept the premises that the state should sanction some kinds of actions with a range of penalties and that supervision and resources (e.g., drug and alcohol treatment, job programs, counseling, housing) are appropriate responses, assessing persons for placement within those programs would appear to be a reasonable approach. But, as we explained in Section 3.4.3, the use for which COMPAS is designed and suited is different than the use to which it is put in Loomis. Hence, even if there is evidence that COMPAS is well calibrated in assessing risk, that same evidence does not underwrite use for sentencing purposes. As with PredPol, there is a disconnect between evidence and tool use, and that creates a drag on the epistemic facet of legitimation via normative authority.

The VAM cases are a bit simpler in that the tool itself has substantial flaws, as we discussed in Sections 1.1 and 3.4. Evaluating teachers is a reasonable policy goal. However, the fact that the tools to do so have such big flaws precludes use of EVAAS and TVAAS from legitimation via normative authority. There is not a similar impediment to legitimation via democratic will. Note two things, though. First, that some institution, government, law, or policy is legitimate – which is to say based on a justified exercise of political power – is not the same as it being morally justified. Political legitimacy is in that sense a lower bar. Second, the fact that the algorithmic systems in these cases do not place a drag on the legitimation process is not a sufficient condition to ensure legitimacy. As we made clear in Section 8.4, our concern is that some kinds of algorithmic systems hinder citizens’ abilities to exercise their responsibility to legitimate policies. VAMs do not obviously hinder fulfilling that responsibility.

8.6 Conclusion

Autonomy is foundational for political legitimacy. It grounds each of the accounts we canvassed in Section 8.2, including the hybrid account we endorse. We argued that one component part of legitimacy is the process of legitimation. That is, citizens have a responsibility to exercise their autonomy to legitimate governments, policies, laws, and actions. Algorithmic systems can impede citizens from fulfilling those responsibilities. PredPol (and to an extent COMPAS) is an example of an impediment to legitimation via normative authority. Whatever evidence there is of PredPol advancing a good policy goal (and there is some), that deflects from assessing how PredPol is actually used. That conflicts with the access condition, whereby belief in a policy’s value has to be linked to evidence for its actual value.

The cases of Cambridge Analytica and the IRA undermine the second arm of political legitimacy. They create conditions where citizens would be alienated from the source of their beliefs and attitudes; they substitute ersatz involvement for genuine involvement and generate false beliefs.

But we should reiterate here that political legitimacy goes much further than the process of legitimation. For one, there are questions about different levels or targets of legitimacy. The fact, if it is, that policies using particular technologies create a drag on legitimation or, worse, are not legitimate all-things-considered does not tell us whether the agencies using the technologies, the municipalities, states, or nations using those technologies are legitimate or much else. It is important not to overinterpret drags on legitimation to make broader conclusions and legitimacy per se. Related is that legitimate authority can get things wrong and still retain legitimacy to exercise authority. That breadth of legitimacy is why our conclusions here are limited to the ability of citizens to exercise their responsibilities.

9.1 Further Work

In one sense, that is a lot of terrain to have covered. And yet even within the scope of autonomy and algorithmic systems, there is much more work to do. Throughout the book, we pause to point out how various topics bear upon one another. There are, however, connections across the chapters that warrant more attention.

In Chapter 4 we address informational components to autonomy, and we argue that people have claims to information about algorithmic systems based on practical agency, cognitive agency, and democratic agency. There is a question, though, about whether such information is a condition for people to be able to reasonably endorse systems. That is, the precise relationship between what we owe people materially (per Chapter 3 and the Reasonable Endorsement Test) and what we owe people informationally (per the principles of informed practical and cognitive agency) is worth examining. Similar concerns arise in understanding the relationship between practical, cognitive, and democratic agency and political legitimacy. We note in Chapters 4 and 8 that the ability to exercise democratic agency is a component of the legitimating process. We explain how that relationship functions within the normative authority and democratic will “arms” of legitimacy. But a number of questions remain. Just what kinds of processes are subject to legitimation at all? Certainly, direct actions of government agents can be legitimate or not, but what about private actions? Or what about private actors whose influence on state affairs is enormous? Moreover, what is the extent of information necessary for citizens to fulfill their legitimating responsibilities?

There are further connections to be drawn between Chapter 5’s discussion of quality of agency and other facets of autonomy. To the extent that the challenges to freedom limit people’s quality of agency (and hence, positive freedom), are they also limitations on people’s ability to reasonably endorse states of affairs? It also seems plausible that such challenges are an impediment to exercising practical, cognitive, and democratic agency. It is therefore worth exploring whether even greater epistemically paternalistic actions are justifiable (or even obligatory) than those we outline in Chapter 6.

We should also point out that the relevance of agency laundering may be even broader than we outline in Chapter 7. Laundering may be applicable in other cases we discuss throughout the book. For example, it would be worth considering it in background checks (Chapter 4) and predictive policing (Chapter 8). When presenting on the topic of agency laundering to academic audiences, we have often received questions about whether it could be applied to political actions (e.g., Brexit). While we cannot do justice to that question here, we can say that the use of sophisticated profiling and influence operations is a plausible mechanism for laundering. Hence, examining influence campaigns as potential sites of laundering is worthwhile.

And moving beyond the topics we’ve covered, the range of open questions is vast. Driverless vehicles, for example, raise numerous issues with respect to responsibility, human control, and worker displacement. Robots, including those that interact with humans, provide care and companionship, and displace labor are a topic of growing philosophical and moral concern. Weapons of war raise numerous issues relevant to human control and responsibility.

9.2 Caveats: Baseline Issues

An important question that one might raise about this project concerns baseline comparisons. So, while COMPAS, EVAAS, TVAAS, PredPol, and other systems may have important problems with respect to autonomy, one might argue that despite those issues, the technologies are better than the relevant alternatives. What matters is not that algorithmic systems have flaws compared to some ideal, but whether they are meaningfully better than relevant alternatives. Having a system like COMPAS that assesses risk may be better than humans, who have well-known biases, who act arbitrarily, and who are harder to audit.

That’s a reasonable criticism. However, it does not undercut the project for a number of reasons. First, even if it is the case that algorithmic systems are better than systems that precede them, it does not follow that they are justifiable. So, even if it is the case that using COMPAS is better than judges at determining likelihood of reoffense, it does not follow that use of COMPAS is itself justifiable. The space of reasonable alternatives need not be some antecedent system and some novel algorithmic system. There could be better algorithms; lower-stakes algorithms; algorithms that do not impose disparate relative burdens; that respect practical, cognitive, and democratic agency; that preserve quality of agency; that do not serve to launder agency; that allow citizens to fulfill their responsibilities of legitimation; and so forth.

Second, even where technologies are better than some alternatives, they may reveal underlying moral concerns. Consider a study from October 2019.Footnote ¹ Obermeyer et al. studied a machine learning system that examined health records in order to predict which patients were in “high risk” categories and either defaulted them into a care program or referred them for screening into a care program. The study determined that Black patients identified as high risk were significantly less healthy than White patients so-identified; that is, Black patients had higher numbers of chronic conditions than similarly categorized White patients. This entailed Black patients were less likely to receive appropriate care than White patients. The reason for the difference, according to the researchers, is that the machine learning algorithm reflected health-care expenditures. That is, risk levels were correlated with the amount of care and treatment that patients received in prior years. As the authors put it, “Black patients generate lesser medical expenses, conditional on health, even when we account for specific comorbidities. As a result, accurate prediction of costs necessarily means being racially biased on health.”Footnote ² That disparity may arise for a couple of reasons. One is that poor patients (who are disproportionately Black) face substantial barriers to receiving healthcare, even if they are insured. For example, transportation time to care facilities may be greater and they may face greater difficulty in getting time off of work. There are also social barriers, including worse treatment of Black patients by care providers and distrust of health-care providers.

Notice, though, that the algorithm at work in the Obermeyer study is likely better than a system that did not use sophisticated technologies to identify high-risk patients and nudge them toward care programs. That is, it was likely better for Black patients as well as White patients and worse for no one. It was just more advantageous for White patients. But there is nonetheless a moral problem with the algorithm. Hence, “better than a prior baseline” should not end our inquiries. In the health system case, the study’s authors developed a different analysis that sorted patients into high-risk groups based on underlying health conditions, and it performed similarly well for Black and for White patients. Our conclusions in this book, we hope, can provide grounds for analyzing what better algorithmic systems would look like.

9.3 Bigger Pictures

A further objection to many of our arguments in the book will have to do with background conditions and structures and whether we have properly identified the most morally salient features of cases. In the case of COMPAS and Loomis, one might argue that the US criminal justice system has so many infirmities that focusing on use of a single tool is beside the point. In Loomis, one could argue that plea deals generally are unjust, in virtue of the fact that they take place in a system where alternatives to plea deals are trials with either expensive counsel or overstretched public defenders, high conviction rates for cases reaching jury verdicts, and long sentences for guilty verdicts. Plea deals to long sentences are common, in other words, because of other kinds of injustices and statutory penalties that outpace what is necessary for either deterrence or desert. Likewise, one might argue that the appropriate focus in analyzing use of VAMs to evaluate K-12 teachers is on issues such as school funding, the vast differences in resources available in wealthy and poor school districts, how those differences track race and ethnicity, the need to ensure excellent teaching, and administrative pressures on teachers generally. One might argue that background checks are a symptom of broader problems of wealth and income inequality, the austere state of healthcare for many Americans, and the fact that landlords have much greater legal power than tenants.

It is certainly the case that one can focus on different facets of problems, be they in criminal justice, education, rental markets, social media, disinformation campaigns, or anything else. But note that how criminal justice systems assess risk, allocate supervisory resources, and sentence people convicted of crimes are constitutive parts of the criminal justice system, not discrete actions. And different, constitutive parts of the criminal justice system may warrant different analyses, and it is not clear that overall systems are best analyzed in a univocal way. And in any case, our work here should be seen as sitting alongside other work on criminal justice, education, social media, disinformation, and so on. Systemic arguments and narrower arguments may be complementary rather than conflicting.

Finally, perhaps the biggest limitation to the project is that the sand is shifting beneath our feet. New ways of using technology continue apace, and some of the systems we discuss over the course of the book will soon have their problems resolved, change, or be replaced by new ones. But those newer algorithmic systems could also be used in ways that fail to respect autonomy, inhibit practical and cognitive agency, limit freedom, launder agency, and create drags on legitimation. That brings us full circle. What matters is not the particular technologies or the specific ways that those technologies are used. Rather, the underlying moral questions are a better anchor. Autonomy is certainly not the only moral value, and the nature, scope, and value of autonomy are contested. Moreover, the ways autonomy interacts with other values require continual reassessment. But, as we stated in Chapter 1, a rock-bottom assumption of this book is that autonomy matters – hopefully, considering autonomy helps us sort through a number of questions about technologies on the ground.