1. Introduction
Computer ethicists have long argued that computer systems are value-laden (Friedman and Nissenbaum Reference Friedman and Nissenbaum1996; Kraemer et al. Reference Kraemer, van Overveld and Peterson2011). Machine learning (ML) is no exception, as highlighted by investigative journalists and academic studies alike (Angwin et al. Reference Angwin, Larson, Surya and Kirchner2016; Obermeyer et al. Reference Obermeyer, Powers, Vogelli and Mullainathan2019). Recently, philosophers of science have begun to address this issue, drawing on parallels with the values in science literature (Biddle Reference Biddle2020; Johnson, Reference Johnsonforthcoming). This article further explores these parallels, focusing on a strategy that has been proposed for managing value-ladenness in both domains: transparency.
Scientific results are often the result of complex chains of justification. This can occlude significant value-laden decisions to nonexperts who rely on these results. Philosophers have argued that to mitigate the impacts of value-ladenness scientists should make transparent either the uncertainties (Betz Reference Betz, Elliott and Steel2017) or the values involved in such decisions (Douglas Reference Douglas2009; Elliott Reference Elliott2017, Reference Elliott2020). Though not uncontested (Nguyen Reference Nguyen2021; Schroeder Reference Schroeder2021), proponents of transparency argue that it helps secure democratic legitimacy and autonomy for nonexperts (Elliott Reference Elliott2010).
Similar concerns drive calls for transparency in ML. As with scientific results, the complexity of modern ML systems, and their ability to discover novel correlations, make them epistemically opaque (Burrell Reference Burrell2016; Sullivan Reference Sullivan2022; Zednik Reference Zednik2021). A growing field of technical research, known as “interpretable” or “explainable” artificial intelligence (XAI), seeks to improve the epistemic transparency of ML systems (Zednik Reference Zednik2021; Zerilli Reference Zerilli2022). However, many law and technology scholars argue that this focus is misplaced, proposing instead governance frameworks based on making transparent the goals and values that ML systems promote (Kroll Reference Kroll2018; Selbst and Barocas Reference Selbst and Barocas2018).
Drawing on the values in science literature, this article evaluates these different approaches to transparency in ML. I start by outlining my preferred construal of value-ladenness and distinguish three types of transparency: epistemic transparency, retrospective value transparency, and prospective value transparency. Next, I apply this distinction to transparency in ML. I highlight limitations of XAI as a means to promoting epistemic transparency. Finally, I consider three sources of value-ladenness in ML: problem formulation, inductive risk, and specification gaming. I argue that retrospective value transparency is only well-suited for dealing with the first, while the third raises serious challenges even for prospective value transparency.
2. Value-ladenness
Scientific inquiry involves choices that could reasonably have been made differently. Scientists make decisions about how to conceptualize and frame research questions, what type of models to develop, which hypotheses to test, how to collect data and design experiments, and what kinds of evidence suffice to accept or reject hypotheses. There are often several available options, each with advantages and drawbacks, but no decisive reason to prefer one over the other. In these cases, the decisions scientists make are contingent (Brown Reference Brown2020, 57–86): someone in the same epistemic situation—with the same evidence, background information, methodologies, available equipment, and other resources for inquiry—could reasonably have chosen differently.
A contingent decision is value-laden when the reasonable alternative options differ in their potential impacts on the things we value and care about. One much-discussed form of value-ladenness is inductive risk, that is, the risk of inferential error. Because different types of errors—most saliently false positives and false negatives—typically differ in their potential impacts, methodological decisions which affect the balance of inductive risk are often value-laden (Douglas Reference Douglas2009). Another example concerns the types of evidence different methodologies provide. For example, randomized controlled trials tend to be good for assessing whether a given intervention produced some population-level outcome but are relatively uninformative about distributive consequences. Thus, if most researchers pursue randomized controlled trials, it becomes difficult for policy makers to find evidence relevant to policies aimed at distributive values, such as equality or priority for the worst-off (Khosrowi Reference Khosrowi2019).
There are two things worth noticing about this definition of value-ladenness. First, it makes decisions the primary locus of value-ladenness. However, products of inquiry, such as theories, models, or algorithms, can be called value-laden in a derived sense if they are shaped by and mediate the potential impacts of value-laden decisions. That is, if some contingent decision had been made differently, the product would been different in a way that changes its potential impacts on the things we value.
Second, decisions are defined as value-laden regardless of whether value judgments played any role in reaching or justifying them. As Ward (Reference Ward2021) argues, philosophers have discussed (and often conflated) four distinct kinds of value-ladenness: (i) decisions consciously motivated by values, (ii) decisions justified by values, (iii) decisions causally influenced by values (e.g., through implicit biases or institutional structures), and (iv) decisions that impact the things we value. While there are often complex interactions between these, my use of the term in this article is restricted to (iv).
Few philosophers would deny that many scientific decisions are value-laden in this sense (though spelling out exactly how is often not trivial; Ward [Reference Ward2021, 57]). Rather, framed this way, the main disagreements concern what can and should be done to manage value-ladenness. I will focus on issues relating to transparency. These arise when scientific products have been shaped by value-laden decisions in ways that are unclear to those relying on them. Take randomized controlled trials again: It would be problematic if contingent decisions about what kinds of evidence to prioritize systematically inhibited policy makers from pursuing otherwise legitimate values, especially if this happened without the awareness of policy makers or the public. More generally, if nonexperts are unaware of the value-ladenness of the scientific products they rely on, it risks undermining their ability to determine which values are prioritized in their own decision making (Betz Reference Betz, Elliott and Steel2017; Elliott Reference Elliott2010; Schroeder Reference Schroeder2021). Concerns of this kind motivate calls for transparency.
3. Epistemic transparency and value transparency
The concept of transparency is complex (Biddle Reference Biddle2020; Elliott Reference Elliott2020), but generally involves providing relevant kinds of information to nonexperts. I take this to involve at least two things:
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(a) Openness: communicating (or making accessible) the relevant information to the right audience.
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(b) Comprehensibility: ensuring the audience can understand and use the information.
These in turn entail a further precondition:
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(c) Explicitness: ensuring the communicator is aware of and able to articulate the information in the right way, i.e., so that it achieves (a) and (b).
Different kinds of transparency can be distinguished based on the type of information involved. Philosophers have promoted two general kinds of transparency in response to value-ladenness: epistemic transparency and value transparency.
Epistemic transparency focuses on information about the uncertainties or justifications involved in contingent decisions. For instance, Betz (Reference Betz, Elliott and Steel2017) proposes full uncertainty disclosure as a strategy for managing inductive risk. Briefly, he argues that scientists should only endorse claims that are beyond reasonable doubt. Instead of asserting uncertain conclusions, these should be reframed as “hedged hypotheses,” where the type and degree of uncertainty are made explicit. When faced with contingent choices, rather than picking one option, scientists should analyze as many as possible, report the consequences of each option, and specify any remaining uncertainties.
The motivation for epistemic transparency is to leave as many value-laden decisions as possible to those who rely on scientific products. Ideally, scientists should merely lay out the options and explain uncertainties, leaving it to nonexperts to decide what risks they are willing to take, given the values they want to prioritize. However, this strategy faces limitations in relation to each of (a)–(c). First, due to the number of contingent choices scientists face, it easily becomes intractable to analyze and communicate all but a small fraction. Second, the technical vocabulary necessary to accurately specify the relevant uncertainties may not be comprehensible to nonexperts. (Betz for instance mentions “imprecise probabilities, fuzzy logic, or degrees of possibility” [2017, 104].) Third, parts of scientists’ knowledge are arguably tacit, situated in know-how, habits, or social structures. Scientists may not themselves be able to make these fully explicit without distortion (Nguyen Reference Nguyen2021). To be sure, Betz emphasizes that his view is only meant as an ideal norm. But this leaves open how to manage value-ladenness under nonideal circumstances.
Value transparency focuses on informing nonexperts about the values involved in value-laden decisions or products. Rather than leaving it to nonexperts to make all value-laden decisions for themselves, this strategy permits scientists to make such decisions, provided they inform the nonexperts about the relevant values. Elliott (Reference Elliott2020, 3) outlines several motivations for this kind of transparency. First, it would warn nonexperts that relying on a given scientific product risks promoting values they disagree with. Second, it may allow them to reanalyze or reframe results in ways that better accord with their priorities. Third, it can enable nonexperts to influence what decisions are made in the first place (Douglas Reference Douglas2009, 153). Finally, it can play an important role in supporting other strategies for managing value-ladenness, such as promoting engagement with different publics who might be impacted (Elliott Reference Elliott2017, 137–62).
The preceding characterization is meant to be consistent with all four of Ward’s senses of value-ladenness. Nonetheless, it is useful to distinguish two subtypes: retrospective value transparency, that is, what values motivated and influenced a given decision (Ward’s first and third senses), and prospective value transparency, that is, what are the potential impacts on things we value of relying on a given decision/product and, relatedly, what values would justify doing so (Ward’s second and fourth senses).
Some discussions of value transparency articulate it in mainly retrospective terms (e.g., Douglas Reference Douglas2009, 153; Elliott Reference Elliott2020, 3; Schroeder Reference Schroeder2021, 550). However, the two can come apart. In many cases, what is directly relevant to nonexperts is what values they risk promoting prospectively. Knowing what motivated or influenced decisions (e.g., through conflict-of-interest statements) at most provides an indirect way to gauge this. Prospective value transparency does not require detailed explanations of how decisions produce their potential impacts or why certain values would justify decisions. Rather, the point is to explain the conclusions of such analyses to nonexperts, such that they can understand what the potential implications are for the values they care about.
This is not to say that prospective value transparency is without its challenges. Explicitness can be especially hard to achieve, as it requires the communicator to predict the likely consequences of scientists’ decisions. Moreover, values can be difficult to express precisely. If scientists and nonexperts have different conceptions of core value concepts (e.g., justice, freedom, health), miscommunication could easily ensue.
Ultimately, none of the three types of transparency distinguished in this section is a panacea. Rather, they are more plausibly seen as complimentary strategies that can be used within broader efforts to manage value-ladenness. Thus, as Elliott (Reference Elliott2020) argues, evaluating the merits of transparency-based strategies will involve more detailed questions concerning the benefits of different types of transparency. In the rest of this article, I will look at some such questions for the case of ML.
4. Explainable AI as epistemic transparency
A prominent objection to using ML in high-stakes decision making, the so-called black box problem, concerns epistemic opacity.Footnote 1 In addition to intentional secrecy by the organizations who control algorithms and lacking technical understanding among users, many have also argued that there are features specific to advanced ML systems that make them epistemically opaque.
ML systems are defined as computer programs capable of using data to improve their performance on some task (Mitchell Reference Mitchell1997, 2). This is typically approached as an optimization problem. For example, many applications of supervised ML are based on search algorithms (e.g., gradient descent) that iteratively adjust the free parameters of a model to optimize its performance on a given dataset according to some performance measure.
Two things distinguish advanced forms of ML, as far as transparency is concerned, from this basic picture. First, the complexity of models. This is not just size—though some ML models are huge, sporting millions or even billions of parameters—but also the highly nonlinear relations that they are able to represent. When models involve many parameters that interact nonlinearly, it becomes difficult to disentangle and make sense of the dependencies between inputs and outputs.
Second, and partly because of this expressive power, ML systems are often able to discover correlations in datasets that go beyond existing human knowledge and understanding. While the power of advanced ML systems to a large extent rests on their ability to find and exploit such correlations, it can also make it difficult to explain their performance (Sullivan Reference Sullivan2022; Zednik Reference Zednik2021). Specifically, it inhibits our ability to explain what features of the target domain the model is tracking (because we lack knowledge of what its features are) and why tracking these features results in the observed model performance (because we lack understanding of the dependencies between them).
A growing body of technical research, called “explainable AI” (XAI), seeks to develop tools for mitigating these challenges. These include methods for generating partial or idealized representations of ML models, as well as more localised representations of how different inputs relate to a given prediction. The latter are particularly relevant here. Many of these are based on sensitivity analyses: varying one or more input variables to estimate which inputs made the largest difference to a given prediction. These estimates can be presented as heatmaps for visual data, rankings of input features, or counterfactuals.Footnote 2 Another popular approach is to provide uncertainty estimates for predictions, for example, by approximating Bayesian posteriors (Gal and Ghahramani Reference Gal and Ghahramani2016) or using ensemble techniques to estimate (non-Bayesian) confidence intervals (Lakshminarayanan et al. Reference Lakshminarayanan, Pritzel and Blundell2017).
While of necessity brief, this overview suffices to illustrate the point I want to make here, namely that most current XAI tools aim at epistemic transparency: They seek to illuminate the justifications and uncertainties that underpin the predictions of ML systems. In fact, many XAI techniques aim at something close to what Betz recommends, by testing the sensitivity of decisions and explicitly reporting uncertainties. Moreover, although different motivations are cited in the XAI literature for using these techniques, the most important one is arguably to enable those relying on ML systems to assess whether their decisions are justified: that is, to enable people to assess whether decisions are made on a basis they consider normatively acceptable (Selbst and Barocas Reference Selbst and Barocas2018, 1122–26; Zerilli Reference Zerilli2022). Again, this resembles Betz’s motivation for epistemic transparency, namely to enable nonexperts to make the relevant value judgments for themselves.
It should therefore not come as a surprise that these techniques, considered as ways of managing value-ladenness, suffer from many of the same limitations as epistemic transparency in science. First, the sensitivity analyses underlying, for example, heatmaps and counterfactuals only analyze a small subset of the potential alternatives and may misleadingly suggest that the same factors would also be important in other, similar cases (Selbst and Barocas Reference Selbst and Barocas2018, 1113–15). Second, not all audiences will have the expertise to comprehend probabilistic uncertainty representations or technical definitions of counterfactuals. Finally, information about the features that influenced a given output will only allow the audience to evaluate its justification if paired with additional knowledge of how the features that input and output variables represent depend on each other, as well as how different kinds of predictions might impact things we value (Selbst and Barocas Reference Selbst and Barocas2018, 1126–29). But, as mentioned, it is exactly the ability to discover correlations beyond our existing knowledge and understanding that underpins the reliability and opacity of many ML systems.
To be clear, this is not to say that XAI tools cannot be helpful for managing value-ladenness in ML. Rather, my point is that they cannot be the whole of the story.
5. The limits of value transparency
Because of these limitations, some law and technology scholars are skeptical of XAI as a governance tool for ML. Instead, they advocate frameworks based either on providing information about value-laden decisions in the design of ML systems or assessments of their impacts.
For instance, Selbst and Barocas (Reference Selbst and Barocas2018, 1130) argue that designers should document information that can help others understand “(1) the values and constraints that shape the conceptualization of the problem, (2) how these values and constraints inform the design of machine learning models and are ultimately reflected in them, and (3) how the outputs of models inform final decisions.” In other words, this strategy relies on value transparency rather than epistemic transparency. More specifically, as they highlight values that motivate design choices, it is a version of retrospective value transparency.
Others have proposed governance schemes that include elements of prospective value transparency. For instance, Kroll (Reference Kroll2018, 1) rejects the idea that ML systems are uniquely mysterious or unaccountable, arguing instead: “Software systems are designed to interact with the world in a controlled way and built or operated for a specific purpose, subject to choices and assumptions.… Technologies can always be understood at a higher level, intensionally in terms of their designs and operational goals and extensionally in terms of their inputs, outputs and outcomes.” For Kroll, assessing and monitoring the potential impacts of ML systems against well-defined criteria provides exactly the kind of transparency we should demand of any technology.
In the following, I discuss the prospects of both types of value transparency for managing three sources of value-ladenness in ML systems: problem formulation, inductive risk, and specification gaming.
5.1 Problem formulation
An important type of contingent decision in designing an ML system concerns what Passi and Barocas (Reference Passi and Barocas2019, 39) call problem formulation, that is, “turning amorphous goals into well-specified problems” that can be solved using ML. While goals and problems expressed in natural language can often be vague or ambiguous, leaving many implicitly understood things unsaid, ML problems have to be fully and explicitly defined. This requires several decisions. For example: What type of ML problem should the goals be modeled as (e.g., regression problem, clustering problem, matching problem)? What target function should the system aim to capture? How are target variables operationalized as measurable data points? How should model performance be defined and measured? What cost function should optimization algorithms try to minimize?
Most such decisions are value-laden. Different reasonable implementations of a given informal problem formulation will prioritize different desiderata and constraints. To use Passi and Barocas’s example, to design a system for identifying good job applicants, designers will need to find a way of specifying what “good” means. Some aspects of being a good employee (e.g., being personable and good at teamwork) are difficult to define and measure directly. Designers could ask managers to hand-label CVs with their judgments of teamwork ability, but those judgments are rarely completely reliable and could easily reproduce social biases. Instead, designers might rely on readily measurable things like sales figures, even if this only captures one aspect of what the company is looking for. These decisions are clearly value-laden: They will impact whether the ML system is more likely to make reliable predictions but of a variable that is less faithful to the original problem, or vice versa.
Retrospective value transparency seems promising for managing this type of value-ladenness. Even if designers are not directly aware of how their decisions are value-laden, it is relatively close to the surface. Getting them to explain what their goals are for the system, why they translated these goals into a particular problem specification, and what the pros and cons of alternative decisions were, should provide good evidence of what the operative values are. While it may still take some analytical work to make these values explicit, in many cases—such as deciding to operationalize “good employee” in terms of sales figures—it will be relatively straightforward.
5.2 Inductive risk
Inductive risk is a perennial feature of ML (and indeed any algorithm; Kraemer et al. Reference Kraemer, van Overveld and Peterson2011). All real-life applications will involve some risk of error, and different design choices will impact what kinds of errors are more likely to occur and who they are more likely to affect.
For instance, certain variables might be more likely to encode preexisting biases, as Obermeyer et al. (Reference Obermeyer, Powers, Vogelli and Mullainathan2019) show for a system deployed to prioritize patients with higher needs for various public health programs. Here, the designers used healthcare spending as a proxy for healthcare needs, perhaps because it was a plentiful and readily available dataset. This turned out to systematically disadvantage Black patients, as they tend to have less money to spend on their healthcare compared to other patients with the same disease burden. Using more direct measures of healthcare needs, such as disease burden, might mitigate this (although there could still be disparities in how they are reported). However, if this data is sparser it could also lead to higher overall error rates.
Inductive risk can to some extent be addressed through retrospective value transparency. For instance, designers can assign differential weights to false positives and false negatives in performance measures and objective functions. Similarly, ML researchers have proposed various statistical measures of “fairness” that can be used to detect and mitigate disparities, say, in false positive rates between subpopulations (see Biddle Reference Biddle2020 for discussion). However, these techniques treat labels in the data set as ground truth; if the data is biased, this will not be reflected in performance measures (Passi and Barocas Reference Passi and Barocas2019, 40). Detecting this kind of bias thus requires an independent dataset, ideally one reflecting the real-world impacts directly. This is for instance what Angwin et al. (Reference Angwin, Larson, Surya and Kirchner2016) tried to do when they looked at whether defendants categorized as high or low risk of recidivism by the COMPAS system in fact went on to commit new crime.
Fully managing inductive risk and bias thus requires us to look beyond the choices of designers and evaluate the impacts of ML systems directly. In other words, it also requires prospective value transparency. To be sure, once a given value-laden impact has been detected, it will often be possible to backtrack and attribute this to one or more decisions within the design process. But if those impacts were not predictable at the time, merely requiring transparency about designers’ choices and motivations are unlikely to reveal them.
5.3 Unintended solutions and specification gaming
This challenge becomes even more pronounced in the final source of value-ladenness I want to discuss. As mentioned, the power of advanced forms of ML lies in their ability to discover and exploit surprising correlations. Such surprises are often exactly what designers are looking for: They deploy ML because they lack an explicitly programmable procedure for solving a problem. Yet the ability to surprise simultaneously gives rise to the phenomenon known as specification gaming (Krakovna et al. Reference Krakovna, Jonathan Uesato, Matthew Rahtz, Ramana Kumar, Leike and Legg2020): When an ML system discovers a “solution” that satisfies the formal specifications of the problem formulation, but which is undesirable given the designers’ intended goals.
ML researchers have demonstrated many cases of specification gaming.Footnote 3 Some involve ML agents designed to play computer games. For instance, one such agent learned to exploit bugs in the software, which enabled it to crash the game just before losing a match. But examples of specification gaming have also been documented in real-world applications. To take a simple example, a system designed to classify skin lesions learned that malignant lesions in the training data were more likely to have been photographed next to a ruler (Patel Reference Patel2017). Obviously, this correlation would not have been a robust basis for making predictions.
The illustrative examples of specification gaming involve outright failures (games crashing, unreliable prediction rules). However, it could equally lead to subtler forms of value-ladenness, for example, by finding a solution that implicitly prioritizes one plausible interpretation of the motivating goals.
Specification gaming arises when designers fail to consider the relevance of certain possibilities (e.g., that there are bugs in the game software) and therefore end up giving a formal problem specification that implicitly allows the undesirable solutions. But, as Krakovna et al. point out, the more complex the tasks become that ML systems are applied to, the harder it becomes for designers to consider every possible implication of their design choices. Though it might be clear in retrospect how the unintended solutions could have been prevented, they can be highly surprising and difficult to predict in advance. Therefore, value transparency strategies focused on designers’ motivations and choices would be unlikely to detect this type of value-ladenness.
These difficulties should of course not be used as an excuse for complacency. Rather, it puts even more onus on designers to carefully test and evaluate the impacts of their systems. Yet prospective value transparency still depends on our ability to recognize when something has (or preferably, will) go wrong. Again, the ability of advanced ML systems to find surprising solutions could potentially produce subtler impacts, or produce them through subtler causal chains, thereby making them more difficult to discover (explicitness) or explain (comprehensibility).
6. Conclusions
In this article I have distinguished three strategies for managing value-ladenness in science—epistemic transparency, retrospective, and prospective value transparency—and argued that these map onto different approaches within the literature on transparency in ML. I have argued that epistemic transparency faces limitations. However, so do both forms of value transparency.
Some constructive lessons can also be drawn. First, my discussion of transparency strategies in ML highlights the importance of distinguishing between retrospective and prospective value transparency strategies. More generally, it illustrates that the merits of transparency as a strategy for managing value-ladenness cannot be evaluated in the abstract. The advantages and drawbacks of different strategies need to be investigated in specific contexts.
Acknowledgments
Many thanks to William Peden, Ali Boyle, and Milena Ivanova for providing very helpful comments on a draft of this paper at short notice. In addition to the PSA2020/21 symposium “Philosophy of Science Meets AI Ethics,” previous versions were presented at the “Philosophie und Wissenschaftsreflexion Colloquium,” Lebniz Universität Hannover, “Virtual Workshop on the Philosophy of Medical AI,” University of Tübingen, and the 2018 BSPS in Oxford. I’m grateful to the audiences at these events for their valuable feedback. This research was funded in whole, or in part, by the Wellcome Trust (Grant number 213660/Z/18/Z) and the Leverhulme Trust, through the Leverhulme Centre for the Future of Intelligence. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
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