Actionable Auditing

Raji, Inioluwa Deborah; Buolamwini, Joy

doi:10.1145/3306618.3314244

Cited by 337 publications

(69 citation statements)

References 15 publications

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“…744 Algorithmic audits in 2019 uncovered disproportionate performance or biases within AI systems ranging from self-driving-car software that performed differently for darker-and lighter-skinned pedestrians, 745 gender bias in online biographies, 746 skewed representations in object recognition from lower-income environments, 747 racial differences in algorithmic pricing, 748 and differential prioritization in healthcare, 749 as well as performance disparities in facial recognition. 750 In several cases, these audits had a tangible impact on improving the lives of people unfairly affected. 751 They also had a substantial impact on policy discussions.…”

Section: Ai and Interdisciplinary Analysis For Problem Solvingmentioning

confidence: 99%

A Unified Framework of Five Principles for AI in Society

Floridi¹,

Cowls²

2019

Harvard Data Science Review

386

214

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Section: Ai and Interdisciplinary Analysis For Problem Solvingmentioning

confidence: 99%

A Unified Framework of Five Principles for AI in Society

Floridi¹,

Cowls²

2019

Harvard Data Science Review

386

214

View full text Add to dashboard Cite

show abstract

“…For algorithms that track online behavior in order to personalize (limit) ads and products, audits have focused primarily on issues of transparency or autonomy. Audits of some algorithms, such as facial recognition or affect recognition (Buolamwini and Gebru, 2018; Raji and Buolamwini, 2019), have focused not only on bias, but also on the potential for abuse. The audit instrument we sketch here is meant to be more comprehensive and broadly applicable.…”

Section: What Is An Algorithm Audit?mentioning

confidence: 99%

The algorithm audit: Scoring the algorithms that score us

2021

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In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed.

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“…For instance, discarding data due to a subpopulation having missing values more commonly (useless answers or not provided) may result in underrepresentation. Decision makers may also favour a privileged group or reinforce stereotypes influenced by cultural norms or one's own beliefs by, for instance, using underrepresentative data to train machine‐learning models (e.g., gender and racial bias found in AI recognition technology 70,71 ). They can also provide different subsets of questions to different groups, thus hindering objectivity and leading to a selective and possibly misleading use of data to support decisions that have already been made.…”

Section: Mapping Missingness and Unfairnessmentioning

confidence: 99%

Missing the missing values: The ugly duckling of fairness in machine learning

et al. 2021

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Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are defined over protected attributes, such as gender, race and nationality. Missing values are one frequent manifestation of all these latent causes: protected groups are more reluctant to give information that could be used against them, sensitive information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we present the first comprehensive analysis of the relation between missing values and algorithmic fairness for machine learning: (1) we analyse the sources of missing data and bias, mapping the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should discourage the consideration of missing values as the uncomfortable ugly data that different techniques and libraries for handling algorithmic bias get rid of at the first occasion, (3) we studyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Actionable Auditing

Cited by 337 publications

References 15 publications

A Unified Framework of Five Principles for AI in Society

A Unified Framework of Five Principles for AI in Society

The algorithm audit: Scoring the algorithms that score us

Missing the missing values: The ugly duckling of fairness in machine learning

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