Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society最新文献

FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport. FairPOT：平衡AUC性能和公平与比例最优传输。

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2025-10-01 Epub Date: 2025-10-15 DOI: 10.1609/aies.v8i2.36660

Pengxi Liu, Yi Shen, Matthew M Engelhard, Benjamin A Goldstein, Michael J Pencina, Nicoleta J Economou-Zavlanos, Michael M Zavlanos

{"title":"FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport.","authors":"Pengxi Liu, Yi Shen, Matthew M Engelhard, Benjamin A Goldstein, Michael J Pencina, Nicoleta J Economou-Zavlanos, Michael M Zavlanos","doi":"10.1609/aies.v8i2.36660","DOIUrl":"10.1609/aies.v8i2.36660","url":null,"abstract":"Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and criminal justice. In these domains, fairness is often evaluated over risk scores rather than binary outcomes, and a common challenge is that enforcing strict fairness can significantly degrade AUC performance. To address this challenge, we propose Fair Proportional Optimal Transport (FairPOT), a novel, model-agnostic post-processing framework that strategically aligns risk score distributions across different groups using optimal transport, but does so selectively by transforming a controllable proportion, i.e., the top- <math><mi>λ</mi></math> quantile, of scores within the disadvantaged group. By varying <math><mi>λ</mi></math> , our method allows for a tunable trade-off between reducing AUC disparities and maintaining overall AUC performance. Furthermore, we extend FairPOT to the partial AUC setting, enabling fairness interventions to concentrate on the highest-risk regions. Extensive experiments on synthetic, public, and clinical datasets show that FairPOT consistently outperforms existing post-processing techniques in both global and partial AUC scenarios, often achieving improved fairness with slight AUC degradation or even positive gains in utility. The computational efficiency and practical adaptability of FairPOT make it a promising solution for real-world deployment.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"8 2","pages":"1611-1622"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Principles and Policy Recommendations for Comprehensive Genetic Data Governance. 遗传数据综合治理的原则和政策建议。

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2025-01-01 Epub Date: 2025-10-15 DOI: 10.1609/aies.v8i3.36701

Vivek Ramanan, Ria Vinod, Cole Williams, Sohini Ramachandran, Suresh Venkatasubramanian

{"title":"Principles and Policy Recommendations for Comprehensive Genetic Data Governance.","authors":"Vivek Ramanan, Ria Vinod, Cole Williams, Sohini Ramachandran, Suresh Venkatasubramanian","doi":"10.1609/aies.v8i3.36701","DOIUrl":"10.1609/aies.v8i3.36701","url":null,"abstract":"Genetic data collection has become ubiquitous, producing genetic information about health, ancestry, and social traits. However, unregulated use-especially amid evolving scientific understanding-poses serious privacy and discrimination risks. These risks are intensified by advancing AI, particularly multi-modal systems integrating genetic, clinical, behavioral, and environmental data. In this work, we organize the uses of genetic data along four distinct 'pillars', and develop a risk assessment framework that identifies key values any governance system must preserve. In doing so, we draw on current privacy scholarship concerning contextual integrity, data relationality, and the Belmont principle. We apply the framework to four real-world case studies and identify critical gaps in existing regulatory frameworks and specific threats to privacy and personal liberties, particularly through genetic discrimination. Finally, we offer three policy recommendations for genetic data governance that safeguard individual rights in today's under-regulated ecosystem of large-scale genetic data collection and usage.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"8 3","pages":"2136-2149"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13077651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy Preserving Machine Learning Systems 保护隐私的机器学习系统

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2022-01-01 DOI: 10.1145/3514094.3539530

Soumia Zohra El Mestari

引用次数: 0

AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19 - 21, 2021 AAAI/ACM人工智能、伦理与社会会议，牛津，英国，2021年5月19 - 21日

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2022-01-01 DOI: 10.1145/3514094

引用次数: 0

Bias in Artificial Intelligence Models in Financial Services 金融服务中人工智能模型的偏差

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2022-01-01 DOI: 10.1145/3514094.3539561

Ángel Pavón Pérez

引用次数: 0

To Scale: The Universalist and Imperialist Narrative of Big Tech 规模:大科技的普遍主义和帝国主义叙事

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-01-01 DOI: 10.1145/3461702.3462474

Jessica de Jesus de Pinho Pinhal

引用次数: 0

AIES '21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021 AAAI/ACM人工智能、伦理与社会会议，虚拟事件，美国，2021年5月19-21日

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-01-01 DOI: 10.1145/3461702

引用次数: 2

Toward Implementing the Agent-Deed-Consequence Model of Moral Judgment in Autonomous Vehicles 自动驾驶汽车道德判断的Agent-Deed-Consequence模型实现研究

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2020-02-07 DOI: 10.1145/3375627.3375853

Veljko Dubljević

引用次数: 0

Trade-offs in Fair Redistricting 公平选区划分的权衡

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2020-02-07 DOI: 10.1145/3375627.3375802

Zachary Schutzman

引用次数: 6

A Fairness-aware Incentive Scheme for Federated Learning 基于公平性的联邦学习激励机制

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2020-02-07 DOI: 10.1145/3375627.3375840

Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, D. Niyato, Qiang Yang

{"title":"A Fairness-aware Incentive Scheme for Federated Learning","authors":"Han Yu, Zelei Liu, Yang Liu, Tianjian Chen, Mingshu Cong, Xi Weng, D. Niyato, Qiang Yang","doi":"10.1145/3375627.3375840","DOIUrl":"https://doi.org/10.1145/3375627.3375840","url":null,"abstract":"In federated learning (FL), data owners \"share\" their local data in a privacy preserving manner in order to build a federated model, which in turn, can be used to generate revenues for the participants. However, in FL involving business participants, they might incur significant costs if several competitors join the same federation. Furthermore, the training and commercialization of the models will take time, resulting in delays before the federation accumulates enough budget to pay back the participants. The issues of costs and temporary mismatch between contributions and rewards have not been addressed by existing payoff-sharing schemes. In this paper, we propose the Federated Learning Incentivizer (FLI) payoff-sharing scheme. The scheme dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff gained by them and the waiting time for receiving payoff. Extensive experimental comparisons with five state-of-the-art payoff-sharing schemes show that FLI is the most attractive to high quality data owners and achieves the highest expected revenue for a data federation.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86110274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 143