Convex Fairness Constrained Model Using Causal Effect Estimators

Ogura, Hideki

doi:10.1145/3366424.3383556

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…-Tasks in fairness literature: fair classification (Yang et al, 2020a;Sharifi-Malvajerdi et al, 2019;Heidari et al, 2018;Lohaus et al, 2020;Cotter et al, 2019;Creager et al, 2019;Cotter et al, 2018), fair regression evaluation (Heidari et al, 2019a), fair few-shot learning (Slack et al, 2020(Slack et al, , 2019a, rich-subgroup fairness evaluation (Kearns et al, 2019), rich-subgroup fair classification (Kearns et al, 2018), fair regression (Chzhen et al, 2020a,b;Romano et al, 2020;Agarwal et al, 2019;Mary et al, 2019;Komiyama et al, 2018;Ogura and Takeda, 2020;Berk et al, 2017), fair representation learning (Ruoss et al, 2020), robust fair classification (Mandal et al, 2020) -Tasks in fairness literature: fair classification (He et al, 2020b;Sharma et al, 2020b;Goel et al, 2018;Oneto et al, 2019a;Celis et al, 2019b;Canetti et al, 2019;Cho et al, 2020;Savani et al, 2020;Donini et al, 2018;Heidari et al, 2018;Russell et al, 2017;Quadrianto and Sharmanska, 2017;Calmon et al, 2017;DiCiccio et al, 2020;Xu et al, 2020;Vargo et al, 2021;Roh et al, 2021;Maity et al, 2021;…”

Section: A36 Communities and Crimementioning

confidence: 99%

Algorithmic Fairness Datasets: the Story so Far

Fabris,

Messina,

Silvello

et al. 2022

Preprint

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Data-driven algorithms are being studied and deployed in diverse domains to support critical decisions, directly impacting on people's wellbeing. As a result, a growing community of algorithmic fairness researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of the risks and opportunities of automated decision-making for different populations. Algorithmic fairness progress hinges on data, which can be used appropriately only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we survey over two hundred datasets employed in algorithmic fairness research, producing standardized and searchable documentation for each of them, along with in-depth documentation for the three most popular fairness datasets, namely Adult, COMPAS and German Credit. These documentation efforts support multiple contributions. Firstly, we summarize the merits and limitations of popular algorithmic fairness datasets, questioning their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, to assist dataset users in task-oriented and domain-oriented search. Finally, we analyze these resources from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel datasets.

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Section: A36 Communities and Crimementioning

confidence: 99%

Algorithmic Fairness Datasets: the Story so Far

Fabris,

Messina,

Silvello

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Fair Machine Learning Through the Lens of Causality

Wu,

Zhang,

2023

Machine Learning for Causal Inference

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Algorithmic fairness datasets: the story so far

Fabris

Messina

Silvello

et al. 2022

Data Min Knowl Disc

View full text Add to dashboard Cite

Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.

show abstract

Convex Fairness Constrained Model Using Causal Effect Estimators

Cited by 4 publications

References 24 publications

Algorithmic Fairness Datasets: the Story so Far

Algorithmic Fairness Datasets: the Story so Far

Fair Machine Learning Through the Lens of Causality

Algorithmic fairness datasets: the story so far

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