This article discusses the potential sources and consequences of unfairness in artificial intelligence (AI) predictive tools used for anti-corruption efforts. Using the examples of three AI-based anti-corruption tools from Brazil—risk estimation of corrupt behaviour in public procurement, among public officials, and of female straw candidates in electoral contests—it illustrates how unfairness can emerge at the infrastructural, individual, and institutional levels. The article draws on interviews with law enforcement officials directly involved in the development of anti-corruption tools, as well as academic (...) and grey literature, including official reports and dissertations on the tools used as examples. Potential sources of unfairness include problematic data, statistical learning issues, the personal values and beliefs of developers and users, and the governance and practices within the organisations in which these tools are created and deployed. The findings suggest that the tools analysed were trained using inputs from past anti-corruption procedures and practices and based on common sense assumptions about corruption, which are not necessarily free from unfair disproportionality and discrimination. In designing the ACTs, the developers did not reflect on the risks of unfairness, nor did they prioritise the use of specific technological solutions to identify and mitigate this type of problem. Although the tools analysed do not make automated decisions and only support human action, their algorithms are not open to external scrutiny. (shrink)

I discuss the epistemic status of algorithmic predictions in the legal realm. My main claim is that algorithmic predictions do not give us knowledge, not even probabilistic knowledge. The situation, however, is relevantly different from the one in which we find ourselves at the time of assessing statistical evidence in general, and it is rather related to the fact that algorithmic fairness in legal contexts is essentially undetermined. In the light of this, we have to settle for justified beliefs and (...) justified credences. I end by drawing some morals for the Knowledge Norm of action. (shrink)

The use of algorithms to support prediction-based decision-making is becoming commonplace in a range of domains including health, criminal justice, education, social services, lending, and hiring. An assumption governing such decisions is that there is a property Y such that individual a should be allocated resource R by decision-maker D if a is Y. When there is uncertainty about whether a is Y, algorithms may provide valuable decision support by accurately predicting whether a is Y on the basis of known (...) features of a. Based on recent work on statistical evidence in epistemology this article presents an argument against relying exclusively on algorithmic predictions to allocate resources when they provide purely statistical evidence that a is Y. The article then responds to the objection that any evidence that will increase the proportion of correct decisions should be accepted as the basis for allocations regardless of its epistemic deficiency. Finally, some important practical aspects of the conclusion are considered. (shrink)

The literature on the ethics of machine learning in healthcare contains a great deal of work on algorithmic fairness. But a focus on fairness has not been matched with sufficient attention to the relationship between machine learning and distributive justice in healthcare. A significant number of clinical prediction models have been developed which could be used to inform the allocation of scarce healthcare resources. As such, philosophical theories of distributive justice are relevant when considering the ethics of their design and (...) implementation. This paper considers the relationship between machine learning in healthcare and distributive justice with a focus on four aspects of algorithmic design and deployment: the choice of target variable, the model's socio-technical context, the choice of input variables, and the membership of the datasets that models are trained and validated on. Procedural recommendations for how these considerations should be accounted for in the design and implementation of such models follow. (shrink)

Should the input data of artificial intelligence (AI) systems include factors such as race or sex when these factors may be indicative of morally significant facts? More importantly, is it wrong to rely on the output of AI tools whose input includes factors such as race or sex? And is it wrong to rely on the output of AI systems when it is correlated with factors such as race or sex (whether or not its input includes such factors)? The answers (...) to these questions are controversial. In this paper, I argue for the following claims. First, since factors such as race or sex are not morally significant in themselves, including such factors in the input data, or relying on output that includes such factors or is correlated with them, is neither objectionable (for example, unfair) nor commendable in itself. Second, sometimes (but not always) there are derivative reasons against such actions due to the relationship between factors such as race or sex and facts that are morally significant (ultimately) in themselves. Finally, even if there are such derivative reasons, they are not necessarily decisive since there are sometimes also countervailing reasons. Accordingly, the moral status of the above actions is contingent. (shrink)

An AI-based “patient preference predictor” (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP–that between algorithmic prediction and decision-making–and argue that much of the recent philosophical disagreement stems from this (...) oversight. I show how three prominent objections to the PPP only challenge its use as the sole determinant of a choice, and actually support its use as a source of evidence about patient preferences to inform human decision-making. The upshot is that we should adopt the evidential conception of the PPP and shift our evaluation of this technology towards the ethics of algorithmic prediction, rather than decision-making. (shrink)

Philosophy &Public Affairs, Volume 51, Issue 2, Page 166-190, Spring 2023.

Despite the widespread use of automated decision-making (ADM) systems, they are often developed without involving the public or those directly affected, leading to concerns about systematic biases that may perpetuate structural injustices. Existing formal fairness approaches primarily focus on statistical outcomes across demographic groups or individual fairness, yet these methods reveal ambiguities and limitations in addressing fairness comprehensively. This paper argues for a holistic approach to algorithmic fairness that integrates procedural fairness, considering both decision-making processes and their outcomes. Procedural fairness (...) emphasizes the importance of fair decision-making procedures, which aligns with theories of relational justice that stress the quality of social relations and power dynamics. We highlight the need for substantive procedural fairness to ensure better outcomes and address forward-looking responsibilities. Additionally, we propose leveraging Public Engagement, a core dimension within the well-established Responsible Research and Innovation framework, to enhance procedural fairness in ADM systems. Our contribution underscores the value of Public Engagement in fostering fairer ADM processes, thereby expanding the current focus beyond technical outcome-based approaches to encompass broader procedural considerations. (shrink)