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. 

We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is situated at the intersection of two pivotal shifts in workplace dynamics: (a) the increase in online workplace communications due to remote work, and (b) the resurgence of the labor movement and an increase in collective action in workplaces-especially in the tech industry, where this phenomenon has been dubbed the tech worker movement. The shift of work-related communications to online digital platforms in response to an increase in remote work is creating new opportunities for and risks to the privacy of workers. These risks are especially significant for organizers of collective action, with several well-publicized instances of retaliation against labor organizers by companies. Through a series of qualitative interviews with 29 tech workers involved in collective action, we investigate how labor organizers assess and mitigate risks to privacy while engaging in these actions. Among the most common risks that organizers experienced are retaliation from their employer, lateral worker conflict, emotional burnout, and the possibility of information about the collective effort leaking to management. Depending on the nature and source of the risk, organizers use a blend of digital security practices and community-based mechanisms. We find that digital security practices are more relevant when the threat comes from management, while community management and moderation are central to protecting organizers from lateral worker conflict. Since labor organizing is a collective rather than individual project, individual privacy and collective privacy are intertwined, sometimes in conflict and often mutually constitutive. Notions of privacy that solely center individuals are often incompatible with the needs of organizers, who noted that safety in numbers could only be achieved when workers presented a united front to management. Based on our interviews, we identify key topics for future research, such as the growing prevalence of surveillance software and the needs of international and gig worker organizers.We conclude with design recommendations that can help create safer, more secure and more private tools to better address the risks that organizers face. 

Algorithmic impact assessments (AIAs) are an emergent form of accountability for entities that build and deploy automated decision-support systems. These are modeled after impact assessments in other domains. Our study of the history of impact assessments shows that "impacts" are an evaluative construct that enable institutions to identify and ameliorate harms experienced because of a policy decision or system. Every domain has different expectations and norms about what constitutes impacts and harms, how potential harms are rendered as the impacts of a particular undertaking, who is responsible for conducting that assessment, and who has the authority to act on the impact assessment to demand changes to that undertaking. By examining proposals for AIAs in relation to other domains, we find that there is a distinct risk of constructing algorithmic impacts as organizationally understandable metrics that are nonetheless inappropriately distant from the harms experienced by people, and which fall short of building the relationships required for effective accountability. To address this challenge of algorithmic accountability, and as impact assessments become a commonplace process for evaluating harms, the FAccT community should A) understand impacts as objects constructed for evaluative purposes, B) attempt to construct impacts as close as possible to actual harms, and C) recognize that accountability governance requires the input of various types of expertise and affected communities. We conclude with lessons for assembling cross-expertise consensus for the co-construction of impacts and to build robust accountability relationships.