Measuring justice in machine learning

Lundgard, Alan

doi:10.1145/3351095.3372838

Cited by 14 publications

(5 citation statements)

References 76 publications

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“…It is also crucial to gauge if reducing bias across one dimension could affect biases in the other dimensions. Most fairness measures do not account for the intersectionality of identities and standards of justice outside the predominantly Western sphere of distributive justice (Sambasivan et al, 2021;Lundgard, 2020).…”

Section: Fairness From the Lens Of Multiple Social Dimensionsmentioning

confidence: 99%

Fairness in Language Models Beyond English: Gaps and Challenges

Ramesh¹,

Sitaram²,

Choudhury³

2023

Preprint

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With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on English, while multilingual models and non-English languages have received comparatively little attention. This paper presents a survey of fairness in multilingual and non-English contexts, highlighting the shortcomings of current research and the difficulties faced by methods designed for English. We contend that the multitude of diverse cultures and languages across the world makes it infeasible to achieve comprehensive coverage in terms of constructing fairness datasets. Thus, the measurement and mitigation of biases must evolve beyond the current datasetdriven practices that are narrowly focused on specific dimensions and types of biases and, therefore, impossible to scale across languages and cultures.

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Section: Fairness From the Lens Of Multiple Social Dimensionsmentioning

confidence: 99%

Fairness in Language Models Beyond English: Gaps and Challenges

Ramesh¹,

Sitaram²,

Choudhury³

2023

Preprint

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“…ADS is often evaluated according to to specific normative criteria, including fairness [14,71], accountability [147], explainability [8,103], and contestability [67,140]. Such inquiry is motivated in part by evidence that ADS can be biased and can cause various types of harms [13,15,31,109], and in part by application of theories of justice [18,75,98]. Despite major theoretical and practical issues commonly known in academia, prior work shows that perception of the trustworthiness of algorithms relative to humans can be favorable, though it is highly dependent on context, subjectivity of the domain, and performance [11,89,95].…”

Section: Automated Decision Makingmentioning

confidence: 99%

Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

Pan,

Yakhmi,

Iyer

et al. 2022

Preprint

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While research continues to investigate and improve the accuracy, fairness, and normative appropriateness of content moderation processes on large social media platforms, even the best process cannot be effective if users reject its authority as illegitimate. We present a survey experiment comparing the perceived institutional legitimacy of four popular content moderation processes. We conducted a within-subjects experiment in which we showed US Facebook users moderation decisions and randomized the description of whether those decisions were made by paid contractors, algorithms, expert panels, or juries of users. Prior work suggests that juries will have the highest perceived legitimacy due to the benefits of judicial independence and democratic representation. However, expert panels had greater perceived legitimacy than algorithms or juries. Moreover, outcome alignment-agreement with the decision-played a larger role than process in determining perceived legitimacy. These results suggest benefits to incorporating expert oversight in content moderation and underscore that any process will face legitimacy challenges derived from disagreement about outcomes.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing.

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“…In recent years, Rawls' work has become influential in the algorithmic fairness literature (Lundgard 2020;Barocas, Hardt, and Narayanan 2019;Mehrabi et al 2019). Some of this work focuses on using the other aspects of Rawls' theory, such as the original position (Rawls 1971), to develop novel principles of governance to ensure appropriate transparency, explainability, and fairness (Lee et al 2019;Wong 2020;Grace 2020).…”

Section: Related Workmentioning

confidence: 99%

RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

et al. 2021

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We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspirational data distributions: data generated to reflect an ideally fair, FEO-satisfying society. FEO states that everyone with the same talent and willingness to use it should have the same chance of achieving advantageous social positions (e.g., employment), regardless of their background circumstances (e.g., socioeconomic status). Satisfying FEO requires alterations to social structures such as school assignments. Our paper describes RAWLSNET, a method which takes as input a BN representation of an FEO application and alters the BN's parameters so as to satisfy FEO when possible, and minimize deviation from FEO otherwise. We also offer guidance for applying RAWLSNET, including on recognizing proper applications of FEO. We demonstrate the use of our system with publicly available data sets. RAWLSNET's altered BNs offer the novel capability of generating aspirational data for FEO-relevant tasks. Aspirational data are free from the biases of real-world data, and thus are useful for recognizing and detecting sources of unfairness in machine learning algorithms besides biased data.

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Measuring justice in machine learning

Cited by 14 publications

References 76 publications

Fairness in Language Models Beyond English: Gaps and Challenges

Fairness in Language Models Beyond English: Gaps and Challenges

Comparing the Perceived Legitimacy of Content Moderation Processes: Contractors, Algorithms, Expert Panels, and Digital Juries

RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

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