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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. 

In this paper, we suggest a systematic approach for developing socio-technical assessment for hiring ADS. We suggest using a matrix to expose underlying assumptions rooted in pseudoscientific essentialized understandings of human nature and capability, and to critically investigate emerging auditing standards and practices that fail to address these assumptions. 

Algorithmic impact assessments (AIA) are increasingly being proposed as a mechanism for algorithmic accountability. These assessments are seen as potentially useful for anticipating, avoiding, and mitigating the negative consequences of algorithmic decision-making systems (ADS). At the same time, what an AIA would entail remains under-specified. While promising, AIAs raise as many questions as they answer. Choices about the methods, scope, and purpose of impact assessments structure the possible governance outcomes. Decisions about what type of effects count as an impact, when impacts are assessed, whose interests are considered, who is invited to participate, who conducts the assessment, the public availability of the assessment, and what the outputs of the assessment might be all shape the forms of accountability that AIA proponents seek to encourage. These considerations remain open, and will determine whether and how AIAs can function as a viable governance mechanism in the broader algorithmic accountability toolkit, especially with regard to furthering the public interest. Because AlAs are still an incipient governance strategy, approaching them as social constructions that do not require a single or universal approach offers a chance to produce interventions that emerge from careful deliberation. 

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science. 

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. 

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. 

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.