skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability
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.  more » « less
Award ID(s):
1704369 1704425
PAR ID:
10463678
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2023 ACM Conference on Fairness, Accountability, and Transparency
Page Range / eLocation ID:
1450 to 1462
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, ACM Conference on Fairness, Accountability,and Transparency (FAccT ’21))
    null (Ed.)
    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. 
    more » « less
  2. (, Syracuse law review)
    Jacobson v. Massachusetts has long stood for the proposition that courts should generally uphold the government’s public health policies even when they incidentally infringe constitutional rights protections. But the COVID-19 pandemic disrupted this traditional understanding, as many federal courts struck down or enjoined state and local pandemic-response policies, downplaying the applicability of Jacobson. Meanwhile, prominent legal scholars argued that judicial deference premised on Jacobson should be completely abandoned. This article argues that Jacobson must be reconsidered in light of COVID-19, but its posture of deference should not be abandoned. Instead, this article proposes a new theory of “Public Health Deference,” which is the deference that courts should afford to the government’s pandemic-response policies. This article argues that Public Health Deference should be premised on the quality of the processes by which the government creates and implements public health policies, even during an emergency. Courts should not blindly defer to the government’s pandemic response; instead, they should evaluate the government’s decision-making processes to ensure that they meet standards of transparency, accountability, public justification, and community engagement. 
    more » « less
  3. ; ; ; ; (, AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES))
    null (Ed.)
    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. 
    more » « less
  4. ; ; ; (, Session 9E: Web Censorship and Auditing)
    Organized surveillance, especially by governments poses a major challenge to individual privacy, due to the resources governments have at their disposal, and the possibility of overreach. Given the impact of invasive monitoring, in most democratic countries, government surveillance is, in theory, monitored and subject to public oversight to guard against violations. In practice, there is a difficult fine balance between safeguarding individual’s privacy rights and not diluting the efficacy of national security investigations, as exemplified by reports on government surveillance programs that have caused public controversy, and have been challenged by civil and privacy rights organizations. Surveillance is generally conducted through a mechanism where federal agencies obtain a warrant from a federal or state judge (e.g., the US FISA court, Supreme Court in Canada) to subpoena a company or service-provider (e.g., Google, Microsoft) for their customers’ data. The courts provide annual statistics on the requests (accepted, rejected), while the companies provide annual transparency reports for public auditing. However, in practice, the statistical information provided by the courts and companies is at a very high level, generic, is released after-the-fact, and is inadequate for auditing the operations. Often this is attributed to the lack of scalable mechanisms for reporting and transparent auditing. In this paper, we present SAMPL, a novel auditing framework which leverages cryptographic mechanisms, such as zero knowledge proofs, Pedersen commitments, Merkle trees, and public ledgers to create a scalable mechanism for auditing electronic surveillance processes involving multiple actors. SAMPL is the first framework that can identify the actors (e.g., agencies and companies) that violate the purview of the court orders. We experimentally demonstrate the scalability for SAMPL for handling concurrent monitoring processes without undermining their secrecy and auditability. 
    more » « less
  5. ; (, FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency)
    This essay joins recent scholarship in arguing that FAccT's fundamental framing of the potential to achieve the normative conditions for justice through bettering the design of algorithmic systems is counterproductive to achieving said justice in practice. Insofar as the FAccT community's research tends to prioritize design-stage interventions, it ignores the fact that the majority of the contextual factors that practically determine FAccT outcomes happen in the implementation and impact stages of AI/ML lifecycles. We analyze an emergent and widely-cited movement within the FAccT community for attempting to honor the centrality of contextual factors in shaping social outcomes, a set of strategies we term ‘metadata maximalism’. Symptomatic of design-centered approaches, metadata maximalism abstracts away its reliance on institutions and structures of justice that are, by every observable metric, already struggling (where not failing) to provide accessible, enforceable rights. These justice infrastructures, moreover, are currently wildly under-equipped to manage the disputes arising from digital transformation and machine learning. The political economy of AI/ML implementation provides further obstructions to realizing rights. Data and software supply chains, in tandem with intellectual property protections, introduce structural sources of opacity. Where duties of care to vulnerable persons should reign, profit incentives are given legal and regulatory primacy. Errors are inevitable and inextricable from the development of machine learning systems. In the face of these realities, FAccT programs, including metadata maximalism, tend to project their efforts in a fundamentally counter-factual universe: one in which functioning institutions and processes for due diligence in implementation and for redress of harms are working and ready to interoperate with. Unfortunately, in our world, these institutions and processes have been captured by the interests they are meant to hold accountable, intentionally hollowed-out, and/or were never designed to function in today's sociotechnical landscape. Continuing to produce (fair! accountable! transparent!) data-enabled systems that operate in high-impact areas, irrespective of this landscape's radically insufficient paths to justice, given the unavoidability of errors and/or intentional misuse in implementation, and the exhaustively-demonstrated disproportionate distribution of resulting harms onto already-marginalized communities, is a choice - a choice to be CounterFAccTual. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email