Publications
"Against the Causal Account of Algorithmic Fairness" (with Duncan Purves and Schuyler Sturm), Synthese, forthcoming [abstract] [penultimate]
- According to the causal account of algorithmic fairness, disparities in error rates across socially salient groups are unfair only if they are causally explained by group membership. This paper argues that the causal account of algorithmic fairness fails to correctly label cases of algorithmic redlining as instances of algorithmic unfairness. Because these are canonical cases of algorithmic unfairness, the causal account should be rejected. We suggest that the fundamental error made by proponents of the causal account is to conflate algorithmic unfairness and algorithmic discrimination. Algorithmic unfairness can manifest as direct discrimination, indirect discrimination, or evidentiary unfairness. The causal accounts errs in recognizing only the first category.
"Transparency in Automated Decision Systems" (with John Basl), A Companion to Applied Philosophy of AI, eds. Martin Hähnel and Regina Müller (Blackwell), 2025 [abstract] [published]
- Much has been made about the opacity of certain AI-based decision systems. Many have argued that in high-stakes decision contexts, a failure to be able to interpret, explain, or justify the outputs of such systems results in a failure of our obligations to those over whom we deploy these decision systems. These obligations are typically understood as obligations to provide information to decision subjects (or their proxies) so they may assess whether they have been treated appropriately. Concerns about black box systems have motivated work on so-called "explainable AI" ("XAI") tools and techniques to render black boxes transparent. At the same time, these concerns have been met with skepticism about both the meaning and value of explainability, especially given the opaque nature of much human decision making. In this chapter, we summarize the current state of the debate between explainability proponents and skeptics. We then go on to articulate an alternative basis for grounding explainability requirements that appeals to duties of consideration - duties decision makers have to ensure that they are reasoning about decision subjects appropriately. We explain how this alternative approach helps address explainability skepticism and orient our thinking about how decision makers ought to integrate AI-based tools into their decision-making processes.
"What We Owe to Decision Subjects: Beyond Transparency and Explanation in Automated Decision-Making" (with Jeff Behrends and John Basl), Philosophical Studies, 2023 [abstract] [published]
- The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making.
"Equalized Odds is a Requirement of Algorithmic Fairness," Synthese, 2023 [abstract] [penultimate]
- Statistical criteria of fairness are formal measures of how an algorithm performs that aim to help us determine whether an algorithm would be fair to use in decision-making. In this paper, I introduce a new version of the criterion known as “Equalized Odds,” argue that it is a requirement of procedural fairness, and show that it is immune to a number of objections to the standard version.
"Embedded EthiCS: Integrating Ethics Broadly Across
Computer Science Education" (with Barbara Grosz et al.),
Communications of the Association for Computing
Machinery, 2019
[final]
"Ethics and Artificial Intelligence in Public Health
Social Work," in Artificial Intelligence and Social
Work, ed. Milind Tambe and Eric Rice (Cambridge
University Press), 2018 [penultimate]
Papers in Progress
A paper on algorithms and pure statistical evidence (under review)
A paper defending a novel statistical criterion of fairness (under review)
A paper defending calibration within groups (under review)
A paper on race-adjusted clinical algorithms (with Noah Hammarlund and Duncan Purves) (under review)
A paper on moral deference to AI systems (with Jeff Behrends and John Basl) (under review)
A paper on causal-explanatory accounts of proxy discrimination (with Duncan Purves) (draft available)
A paper on the reference class problem and algorithmic fairness (in preparation)
A paper on whether AI systems can act for the right reasons (with Lyndal Grant) (in preparation)
A paper on procedural fairness and the risk of error (in preparation)
"Ethics for Artificial Agents" [abstract] [draft]
- Machine ethics is a relatively new subfield of
computer ethics that focuses on the ethical issues
involved in the design of autonomous software agents
("artificial agents"). According to what I call the
"agential theory" of machine ethics, it is morally
permissible to design an artificial agent to perform
a given action in a given situation if and only if it
would be morally permissible for a human agent to
perform the same action in the same situation. The
agential theory has been highly influential in recent
work on machine ethics. This paper argues that the
agential theory is false by developing a series of
counterexamples, and uses those counterexamples to
illustrate more general lessons about how artificial
agents ought to be designed to act.
