“☑ Fairness Toolkits, A Checkbox Culture?” On the Factors that Fragment Developer Practices in Handling Algorithmic Harms

Balayn, Agathe; Yurrita, Mireia; Yang, Jie; Gadiraju, Ujwal

doi:10.1145/3600211.3604674

Cited by 8 publications

(3 citation statements)

References 76 publications

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“…Similarly, negotiating benchmarks for quality assessment internally can itself prove a laborious and demanding process as siloed teams can pose significant communication problems and inefficiencies [66,82,89,116]. Unsurprisingly, there has been a plethora of approaches and toolkits for assessing algorithmic systems, with varied yardsticks for measurement and evaluation depending on the nature of the algorithmic system in question [21,40,73,86,91,124,129].…”

Section: Whose Benchmarks? Whose Methods?mentioning

confidence: 99%

“…Although engaging in a legal-theoretical debate remains well out of the scope of this paper, we believe it is important to highlight that our legal enquiry departs from the understanding of law as the form-giving institution that stabilises expectations and exchanges by weaving together the economy with the political system in its state form through formal, often -and importantly for our caseprofession-based, organisational arrangements that produce collectively binding decisions within their respective functional areas [72,[19][20][21][22]. Oftentimes, and increasingly in the field of technology, in doing so it offloads normative work and routines of such practice to governance regimes that feed on the coordinated interactions of their subjects and imperatives of economic efficiency to (re)produce order that seems and operates beyond law [64,128].…”

Section: Elements Of the Emerging Political Economy Of Algorithmic Au...mentioning

confidence: 99%

“…Several studies have discussed the necessity yet complexity of incorporating societal considerations in the assessment of algorithmic systems [20,21,88]. The processes for doing so and the effect that organisational structures and practises can have on them are equally well researched and documented empirically [81].…”

Section: Societal Implications and The Required Expertisementioning

confidence: 99%

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Law and the Emerging Political Economy of Algorithmic Audits

Terzis,

Veale,

Gaumann

2024

Preprint

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Accepted in the Proceedings of the 2024 ACM Conference on Fairness, Accountability and Transparency. For almost a decade now, scholarship in and beyond the ACM FAccT community has been focusing on novel and innovative ways and methodologies to audit the functioning of algorithmic systems. Over the years, this research idea and technical project has matured enough to become a regulatory mandate. Today, the Digital Services Act (DSA) and the Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the ‘practice’ of algorithmic auditing thereby presaging how this ‘ecosystem’ will develop. In this paper, we systematically review the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. Who is likely to occupy this space? What are some political and ethical tensions that are likely to arise? How are the mandates of ‘independent auditing’ or ‘the evaluation of the societal context of an algorithmic function’ likely to play out in practice? By shaping the picture of the emerging political economy of algorithmic auditing, we draw attention to strategies and cultures of traditional auditors that risk eroding important regulatory pillars of the DSA and the OSA. Importantly, we warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors and/or diluted within a complex web of (sub-)contractual arrangements, diverse portfolios, and tight timelines.

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Section: Whose Benchmarks? Whose Methods?mentioning

confidence: 99%

Section: Elements Of the Emerging Political Economy Of Algorithmic Au...mentioning

confidence: 99%

Section: Societal Implications and The Required Expertisementioning

confidence: 99%

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Law and the Emerging Political Economy of Algorithmic Audits

Terzis,

Veale,

Gaumann

2024

Preprint

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Policy advice and best practices on bias and fairness in AI

Alvarez,

Colmenarejo,

Elobaid

et al. 2024

Ethics Inf Technol

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The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.

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