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Measuring Fairness of Rankings under Noisy Sensitive Information
16 references·1 citing·35 second hop
Measuring Fairness of Rankings under Noisy Sensitive Information (2022)Measuring Fairness of Rankings under Nois…Algorithmic Bias in Education (2021)Algorithmic Bias in EducationA Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Reducing Disparate Exposure in Ranking: A Learning To Rank Approach (2020)Reducing Disparate Exposure i…Mitigating bias in algorithmic hiring (2020)Mitigating bias in algorithmi…iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making (2019)iFair: Learning Individually …Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search (2019)Fairness-Aware Ranking in Sea…Fairness in Recommendation Ranking through Pairwise Comparisons (2019)Fairness in Recommendation Ra…Fairness of Exposure in Rankings (2018)Fairness of Exposure in Ranki…Equity of Attention (2018)Equity of AttentionMeasuring Fairness in Ranked Outputs (2017)Measuring Fairness in Ranked …FA*IR (2017)FA*IRImproving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records (2016)Improving Ecological Inferenc…Equality of Opportunity in Supervised Learning (2016)Equality of Opportunity in Su…Unequal Representation and Gender Stereotypes in Image Search Results for Occupations (2015)Unequal Representation and Ge…Fairness through awareness (2012)Fairness through awarenessDiscrimination-aware data mining (2008)Discrimination-aware data min…Fairness in Search Systems (2024)Fairness in Search SystemsIncorporating Fairness into Game Theory and Economics (2011)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Princeton University Press (2017)Bias in computer systems (1996)Stereotype Threat and Women's Math Performance (1999)Gender and the Career Choice Process: The Role of Biased Self‐Assessments (2001)Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (2019)Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023)Big Data's Disparate Impact (2014)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2017)Designing Fair Ranking Schemes (2019)Fairwalk: Towards Fair Graph Embedding (2019)Semantics derived automatically from language corpora contain human-like biases (2017)Socially Responsible AI Algorithms: Issues, Purposes, and Challenges (2021)Fairness in recommender systems: research landscape and future directions (2023)Inherent Tradeoffs in the Fair Determination of Risk Scores (2023)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color (1991)Dissecting racial bias in an algorithm used to manage the health of populations (2019)The Ethics of Artificial Intelligence in Education (2022)Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review (2023)8 Decolonizing Methodologies: Research and Indigenous Peoples (2024)The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024)Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings. (1963)Certifying and Removing Disparate Impact (2015)Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development (2020)A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle (2021)Fairness in Machine Learning: A Survey (2023)Fairness-Aware Classifier with Prejudice Remover Regularizer (2012)Fair, Transparent, and Accountable Algorithmic Decision-making Processes (2017)'It's Reducing a Human Being to a Percentage' (2018)
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References

  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 2012Fairness through awareness3,501 cited
  • 2016Equality of Opportunity in Supervised Learning2,535 cited
  • 2021Algorithmic Bias in Education988 cited
  • 2020Mitigating bias in algorithmic hiring830 cited
  • 2008Discrimination-aware data mining676 cited
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  • 2015Unequal Representation and Gender Stereotypes in Image Search Results for Occupations509 cited
  • 2017FA*IR497 cited
  • 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search382 cited
  • 2017Measuring Fairness in Ranked Outputs378 cited
  • 2019Fairness in Recommendation Ranking through Pairwise Comparisons369 cited
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  • 2020Reducing Disparate Exposure in Ranking: A Learning To Rank Approach

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  • 2024Fairness in Search Systems12 cited