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Overlearning speaker race in sociolinguistic auto-coding

Villarreal, Daniel (2021) Overlearning speaker race in sociolinguistic auto-coding. In: Pitt Momentum Fund 2021.

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Concerns about AI fairness have been raised in domains like the American criminal justice system, where algorithms assessing the risk of a pretrial defendant may inadvertently use defendants’ race as a decision criterion. Similar risks apply to the domain of sociolinguistic auto-coding, in which machine learning classifiers assign categories to variable data based on acoustic features (e.g., car vs “cah”). The proposed project addresses this possibility by using sociolinguistic data by assessing the extent to which auto-coders fail to perform equally well on Black vs White speakers of New England English. We will first assess how an auto-coding classifier that does not take any fairness conditions into account performs with respect to fairness as we have defined it. We will then assess remedies that have been suggested in recent work on AI fairness. Contrary to the approach where AI fairness is an afterthought, the proposed project introduces the notion of AI fairness to a new algorithm in the algorithm’s infancy, rather than after it has been widely adopted.


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Item Type: Conference or Workshop Item (Poster)
CreatorsEmailPitt UsernameORCID
Villarreal, DanielDAV49@pitt.eduDAV490000-0002-6070-1138
Centers: Other Centers, Institutes, Offices, or Units > Office of Sponsored Research > Pitt Momentum Fund
Date: 2021
Event Title: Pitt Momentum Fund 2021
Event Type: Other
DOI or Unique Handle: 10.18117/jdr4-3q69
Schools and Programs: Dietrich School of Arts and Sciences > Linguistics
Refereed: No
Other ID: 4139
Date Deposited: 26 Mar 2021 19:10
Last Modified: 17 Feb 2023 20:16


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