Public Accountability: Understanding Sentiments towards Artificial Intelligence across Dispositional Identities

Richardson, Brianna; Prioleau, Diandra; Alikhademi, Kiana; Gilbert, Juan E.

doi:10.1109/istas50296.2020.9462184

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Hype also drives and reinforces the idea of perceived objectivity that underpins AI technologies. Datasets on which AI systems are trained, and subsequently analysed, often reflect inequities that occur in the world at large [109,110]. However, the highly technical nature of data-driven AI systems often provides a rhetoric of objectivity which veils the complicated and much more fallible systems underneath [111].…”

Section: Elements Of Ai Hypementioning

confidence: 99%

How AI hype impacts the LGBTQ + community

McAra-Hunter

2024

AI Ethics

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Hype around Artificial Intelligence (AI) has been a feature of this technology since its inception. However, the most recent wave of AI hype has been leveraged to encourage adoption of AI technologies that cause issues for marginalised communities. Hype is also a means to obfuscate real issues of bias, harm, and exploitation felt most sharply by marginalised communities when AI is implemented. This therefore raises the question of power imbalances as a feature of AI technologies as we currently know them. This paper will study the relationship of AI hype and marginalised communities, with particular emphasis on the LGBTQ + community, and look at the way that AI impacts on this community. This paper will pose two key questions: does hype affect marginalised communities, particularly hype around new technologies such as AI; and what impact does the LGBTQ + community experience as a result of hype. This paper will then move on to discuss areas that provide a focus for discourse of AI hype and the impact on the LGBTQ + community: policy and decision-making, the maintenance of the cisgender heteronormative (cishet) baseline, the ubiquity of a mythology of AI, and the role of market expansion.

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Section: Elements Of Ai Hypementioning

confidence: 99%

How AI hype impacts the LGBTQ + community

McAra-Hunter

2024

AI Ethics

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“…They suggest that the public be given notice of any technology that might impact their lives (Reisman et al, 2018). Fairness research also emphasizes the importance of public transparency from institutions when it comes to the existence and the auditing of their AI (Richardson et al, 2020).…”

Section: Prioritize Fairnessmentioning

confidence: 99%

A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions

Richardson¹,

Gilbert²

2021

Preprint

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In a world of daily emerging scientific inquisition and discovery, the prolific launch of machine learning across industries comes to little surprise for those familiar with the potential of ML. Neither so should the congruent expansion of ethics-focused research that emerged as a response to issues of bias and unfairness that stemmed from those very same applications. Fairness research, which focuses on techniques to combat algorithmic bias, is now more supported than ever before. A large portion of fairness research has gone to producing tools that machine learning practitioners can use to audit for bias while designing their algorithms. Nonetheless, there is a lack of application of these fairness solutions in practice. This systematic review provides an in-depth summary of the algorithmic bias issues that have been defined and the fairness solution space that has been proposed. Moreover, this review provides an in-depth breakdown of the caveats to the solution space that have arisen since their release and a taxonomy of needs that have been proposed by machine learning practitioners, fairness researchers, and institutional stakeholders. These needs have been organized and addressed to the parties most influential to their implementation, which includes fairness researchers, organizations that produce ML algorithms, and the machine learning practitioners themselves. These findings can be used in the future to bridge the gap between practitioners and fairness experts and inform the creation of usable fair ML toolkits.

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Public Accountability: Understanding Sentiments towards Artificial Intelligence across Dispositional Identities

Cited by 2 publications

References 17 publications

How AI hype impacts the LGBTQ + community

How AI hype impacts the LGBTQ + community

A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions

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