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

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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
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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
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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
{"title":"Bias in Artificial Intelligence Models in Financial Services","authors":"Ángel Pavón Pérez","doi":"10.1145/3514094.3539561","DOIUrl":"https://doi.org/10.1145/3514094.3539561","url":null,"abstract":"","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"191 1","pages":"908"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76933461","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: 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
{"title":"To Scale: The Universalist and Imperialist Narrative of Big Tech","authors":"Jessica de Jesus de Pinho Pinhal","doi":"10.1145/3461702.3462474","DOIUrl":"https://doi.org/10.1145/3461702.3462474","url":null,"abstract":"","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"3 1","pages":"267-268"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84278797","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: 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
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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
{"title":"Toward Implementing the Agent-Deed-Consequence Model of Moral Judgment in Autonomous Vehicles","authors":"Veljko Dubljević","doi":"10.1145/3375627.3375853","DOIUrl":"https://doi.org/10.1145/3375627.3375853","url":null,"abstract":"Autonomous vehicles (AVs) and accidents they are involved in attest to the urgent need to consider the ethics of AI. The question dominating the discussion has been whether we want AVs to behave in a 'selfish' or utilitarian manner. Rather than considering modeling self-driving cars on a single moral system like utilitarianism, one possible way to approach programming for AI would be to reflect recent work in neuroethics. The Agent-Deed-Consequence (ADC) model [1-4] provides a promising account while also lending itself well to implementation in AI. The ADC model explains moral judgments by breaking them down into positive or negative intuitive evaluations of the Agent, Deed, and Consequence in any given situation. These intuitive evaluations combine to produce a judgment of moral acceptability. This explains the considerable flexibility and stability of human moral judgment that has yet to be replicated in AI. This paper examines the advantages and disadvantages of implementing the ADC model and how the model could inform future work on ethics of AI in general.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85247049","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: 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
{"title":"Trade-offs in Fair Redistricting","authors":"Zachary Schutzman","doi":"10.1145/3375627.3375802","DOIUrl":"https://doi.org/10.1145/3375627.3375802","url":null,"abstract":"What constitutes a 'fair' electoral districting plan is a discussion dating back to the founding of the United States and, in light of several recent court cases, mathematical developments, and the approaching 2020 U.S. Census, is still a fiercely debated topic today. In light of the growing desire and ability to use algorithmic tools in drawing these districts, we discuss two prototypical formulations of fairness in this domain: drawing the districts by a neutral procedure or drawing them to intentionally induce an equitable electoral outcome. We then generate a large sample of districting plans for North Carolina and Pennsylvania and consider empirically how compactness and partisan symmetry, as instantiations of these frameworks, trade off with each other -- prioritizing the value of one of these necessarily comes at a cost in the other.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76015668","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: 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
Proposal for Type Classification for Building Trust in Medical Artificial Intelligence Systems 在医疗人工智能系统中建立信任的类型分类建议
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2020-02-07 DOI: 10.1145/3375627.3375846
Arisa Ema, Katsue Nagakura, Takanori Fujita
{"title":"Proposal for Type Classification for Building Trust in Medical Artificial Intelligence Systems","authors":"Arisa Ema, Katsue Nagakura, Takanori Fujita","doi":"10.1145/3375627.3375846","DOIUrl":"https://doi.org/10.1145/3375627.3375846","url":null,"abstract":"This paper proposes the establishment of Medical Artificial Intelligence (AI) Types (MA Types)\"that classify AI in medicine not only by technical system requirements but also implications to healthcare workers' roles and users/patients. MA Types can be useful to promote discussion regarding the purpose and application of the clinical site. Although MA Types are based on the current technologies and regulations in Japan, but that does not hinder the potential reform of the technologies and regulations. MA Types aims to facilitate discussions among physicians, healthcare workers, engineers, public/patients and policymakers on AI systems in medical practices.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82747480","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: 3
An Invitation to System-wide Algorithmic Fairness 对全系统算法公平性的邀请
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2020-02-07 DOI: 10.1145/3375627.3375860
Efrén Cruz Cortés, D. Ghosh
{"title":"An Invitation to System-wide Algorithmic Fairness","authors":"Efrén Cruz Cortés, D. Ghosh","doi":"10.1145/3375627.3375860","DOIUrl":"https://doi.org/10.1145/3375627.3375860","url":null,"abstract":"We propose a framework for analyzing and evaluating system-wide algorithmic fairness. The core idea is to use simulation techniques in order to extend the scope of current fairness assessments by incorporating context and feedback to a phenomenon of interest. By doing so, we expect to better understand the interaction among the social behavior giving rise to discrimination, automated decision making tools, and fairness-inspired statistical constraints. In particular, we invite the community to use agent based models as an explanatory tool for causal mechanisms of population level properties. We also propose embedding these into a reinforcement learning algorithm to find optimal actions for meaningful change. As an incentive for taking a system-wide approach , we show through a simple model of predictive policing and trials that if we limit our attention to one portion of the system, we may determine some blatantly unfair practices as fair, and be blind to overall unfairness.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"872 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83361143","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: 5
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