Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

Moss, Emanuel; Watkins, Elizabeth Anne; Singh, Ranjit; Elish, Madeleine Clare; Metcalf, Jacob

doi:10.2139/ssrn.3877437

Cited by 45 publications

(40 citation statements)

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“…organizations to articulate any risks, and then to generate documentation of any mitigation activities in the event that any harms-and associated oversight-do arise 17 [226][227][228][229][230]. A misstep with impact assessments is to only apply them once at the beginning of a long and iterative process in which goals and outcomes can change over time.…”

Section: /77mentioning

confidence: 99%

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz¹,

Vassilev²,

Greene³

et al. 2022

216

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Bias is neither new nor unique to AI and it is not possible to achieve zero risk of bias in an AI system. NIST intends to develop methods for increasing assurance, GOVERNANCE and practice improvements for identifying, understanding, measuring, managing, and reducing bias. To reach this goal, techniques are needed that are flexible, can be applied across contexts regardless of industry, and are easily communicated to different stakeholder groups. To contribute to the growth of this burgeoning topic area, NIST will continue its work in measuring and evaluating computational biases, and seeks to create a hub for evaluating socio-technical factors. This will include development of formal guidance and standards, supporting standards development activities such as workshops and public comment periods for draft documents, and ongoing discussion of these topics with the stakeholder community.

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Section: /77mentioning

confidence: 99%

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz¹,

Vassilev²,

Greene³

et al. 2022

216

View full text Add to dashboard Cite

show abstract

“…However, these auditors are not often granted unconstrained access to data, models, IP, or employees, and, in the current regulatory vacuum, companies have a heavy hand in creating these constraints. External audit bodies, however, are better able to critically analyze fundamentals such as assumptions and epistemologies as well as cross-model contamination (see Moss et al 2021).…”

Section: Cues For Regulatory Audit Mandatesmentioning

confidence: 99%

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

2022

Self Cite

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“…This work -particularly that which focuses on transparency [39] and audits [135] -makes clear that standards of care and frameworks, while important for developing actionable notions of accountability, do not guarantee accountability on their own. Algorithmic impact assessments (AIAs) attempt to fill this gap [96]. They task practitioners with assessing new technologies [115] with respect to the impacts they produce [93].…”

Section: Contemporary Interventions In Accountability and Data-driven...mentioning

confidence: 99%

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

Cooper,

Moss,

Laufer

et al. 2022

Preprint

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In 1996, philosopher Helen Nissenbaum issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Using the conceptual framing of moral blame, Nissenbaum described four types of barriers to accountability that computerization presented: 1) "many hands," the problem of attributing moral responsibility for outcomes caused by many moral actors; 2) "bugs," a way software developers might shrug off responsibility by suggesting software errors are unavoidable; 3) "computer as scapegoat," shifting blame to computer systems as if they were moral actors; and 4) "ownership without liability," a free pass to the tech industry to deny responsibility for the software they produce. We revisit these four barriers in relation to the recent ascendance of data-driven algorithmic systemstechnology often folded under the heading of machine learning (ML) or artificial intelligence (AI) -to uncover the new challenges for accountability that these systems present. We then look ahead to how one might construct and justify a moral, relational framework for holding responsible parties accountable, and argue that the FAccT community is uniquely well-positioned to develop such a framework to weaken the four barriers.

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Assembling Accountability: Algorithmic Impact Assessment for the Public Interest

Cited by 45 publications

References 0 publications

Towards a standard for identifying and managing bias in artificial intelligence

Towards a standard for identifying and managing bias in artificial intelligence

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

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