Search for: All records

In widely used sociological descriptions of how accountability is structured through institutions, an “actor” (e.g., the developer) is accountable to a “forum” (e.g., regulatory agencies) empowered to pass judgements on and demand changes from the actor or enforce sanctions. However, questions about structuring accountability persist: why and how is a forum compelled to keep making demands of the actor when such demands are called for? To whom is a forum accountable in the performance of its responsibilities, and how can its practices and decisions be contested? In the context of algorithmic accountability, we contend that a robust accountability regime requires a triadic relationship, wherein the forum is also accountable to another entity: the public(s). Typically, as is the case with environmental impact assessments, public(s) make demands upon the forum's judgements and procedures through the courts, thereby establishing a minimum standard of due diligence. However, core challenges relating to: (1) lack of documentation, (2) difficulties in claiming standing, and (3) struggles around admissibility of expert evidence on and achieving consensus over the workings of algorithmic systems in adversarial proceedings prevent the public from approaching the courts when faced with algorithmic harms. In this paper, we demonstrate that the courts are the primary route—and the primary roadblock—in the pursuit of redress for algorithmic harms. Courts often find algorithmic harms non-cognizable and rarely require developers to address material claims of harm. To address the core challenges of taking algorithms to court, we develop a relational approach to algorithmic accountability that emphasizes not what the actors do nor the results of their actions, but rather how interlocking relationships of accountability are constituted in a triadic relationship between actors, forums, and public(s). As is the case in other regulatory domains, we believe that impact assessments (and similar accountability documentation) can provide the grounds for contestation between these parties, but only when that triad is structured such that the public(s) are able to cohere around shared experiences and interests, contest the outcomes of algorithmic systems that affect their lives, and make demands upon the other parties. Where courts now find algorithmic harms non-cognizable, an impact assessment regime can potentially create procedural rights to protect substantive rights of the public(s). This would require algorithmic accountability policies currently under consideration to provide the public(s) with adequate standing in courts, and opportunities to access and contest the actor's documentation and the forum's judgments. 

Excerpt in lieu of abstract: Ethics is arguably the hottest product in Silicon Valley's hype cycle today, even as headlines decrying a lack of ethics in technology companies accumulate. After years of largely fruitless outside pressure to consider the consequences of digital technology products, the very recent past has seen a spike in the assignment of corporate resources in Silicon Valley to ethics, including hiring staff for roles we identify here as "ethics owners." In corporate parlance, "owning" a portfolio or project means holding responsibility for it, often across multiple divisions or hierarchies within the organization. Typically, the "owner" of a project does not bear sole responsibility for it, but rather oversees integration of that project across the organization. A remarkable range of internal and external challenges and responses tends to fall under a single analytic framework called "ethics." This strains an already broad term that in some contexts means an open-ended philosophical investigation into moral conditions of human experience and, in other contexts, means the bureaucratized expectations of professional behavior. Likewise, it places strain on corporate structures because it is bureaucratically challenging to disambiguate whether these problems belong in the domain of legal review, human resources, engineering practices, and/or business models and strategy. 

While computer scientists working on questions of fairness have diligently produced algorithmic approaches that seek to minimize disparate impacts across racial categories, the concept of race itself remains either unexamined, or constrained by definitions arising in legal and policy domains. While this may be appropriate for some applications, it is not altogether obvious that the FAT community benefits from refraining from developing a theory of race to guide its own practices. This tutorial will translate concepts from critical race theory and social scientific discourses into concepts legible to a community of machine learning practitioners through a dis- cussion of these theories and small-group activities that illustrate the salience of these theories for problems of fairness in machine learning. 

This translation tutorial will demonstrate that making fairness a tractable engineering goal requires demarcating a clear conceptual difference between bias and unfairness. Making this distinction demonstrates that bias is a property of technical judgments, whereas fairness is a property of value judgments. With that distinction in place, it is easier to articulate how engineering for fairness is an organizational or social function not reducible to a mathematical description. Using hypothetical and real-life examples I will show how product design practices would benefit from an explicit focus on value-driven decision processes. The upshot of these claims is that designing for fairness (and other ethical commitments) is more tractable if organizations build out capacity for the “soft” aspects of engineering practice. See: https://www.youtube.com/watch?v=y-Dj0cw5OtE&t=7s for the presentation.