The Societal Impacts of Algorithmic Decision-Making最新文献

The Societal Impacts of Algorithmic Decision-Making 算法决策的社会影响

The Societal Impacts of Algorithmic Decision-Making Pub Date : 2023-09-07 DOI: 10.1145/3603195

Manish Raghavan

引用次数: 0

The Externalities of Exploration and How Data Diversity Helps Exploitation 探索的外部性和数据多样性如何帮助开发

The Societal Impacts of Algorithmic Decision-Making Pub Date : 2018-06-01 DOI: 10.1145/3603195.3603199

Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

{"title":"The Externalities of Exploration and How Data Diversity Helps Exploitation","authors":"Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu","doi":"10.1145/3603195.3603199","DOIUrl":"https://doi.org/10.1145/3603195.3603199","url":null,"abstract":"Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users for information that will lead to better decisions in the future. Recently, concerns have been raised about whether the process of exploration could be viewed as unfair, placing too much burden on certain individuals or groups. Motivated by these concerns, we initiate the study of the externalities of exploration - the undesirable side effects that the presence of one party may impose on another - under the linear contextual bandits model. We introduce the notion of a group externality, measuring the extent to which the presence of one population of users impacts the rewards of another. We show that this impact can in some cases be negative, and that, in a certain sense, no algorithm can avoid it. We then study externalities at the individual level, interpreting the act of exploration as an externality imposed on the current user of a system by future users. This drives us to ask under what conditions inherent diversity in the data makes explicit exploration unnecessary. We build on a recent line of work on the smoothed analysis of the greedy algorithm that always chooses the action that currently looks optimal, improving on prior results to show that a greedy approach almost matches the best possible Bayesian regret rate of any other algorithm on the same problem instance whenever the diversity conditions hold, and that this regret is at most $tilde{O}(T^{1/3})$. Returning to group-level effects, we show that under the same conditions, negative group externalities essentially vanish under the greedy algorithm. Together, our results uncover a sharp contrast between the high externalities that exist in the worst case, and the ability to remove all externalities if the data is sufficiently diverse.","PeriodicalId":256315,"journal":{"name":"The Societal Impacts of Algorithmic Decision-Making","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117054508","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: 52

On Fairness and Calibration 论公平性与校准

The Societal Impacts of Algorithmic Decision-Making Pub Date : 2017-09-06 DOI: 10.1145/3603195.3603198

Geoff Pleiss, Manish Raghavan, Felix Wu, J. Kleinberg, Kilian Q. Weinberger

引用次数: 694

Authors’ Biographies/Index 作者的传记/索引

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1016/b978-0-12-805305-8.00020-0

Manish Raghavan

引用次数: 277

Inherent Tradeoffs in the Fair Determination of Risk Scores 公平确定风险评分的内在权衡

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603197

引用次数: 1

Algorithmic Monoculture and Social Welfare 算法单一文化与社会福利

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603211

𝜃 𝜀

引用次数: 0

On Fairness and Calibration 论公平性与校准

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603207

引用次数: 0

How Do Classifiers Induce Agents to Behave Strategically? 分类器如何诱导智能体策略性行为?

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603201

引用次数: 0

Inherent Tradeoffs in the Fair Determination of Risk Scores 公平确定风险评分的内在权衡

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603206

引用次数: 0

The Externalities of Exploration and How Data Diversity Helps Exploitation 探索的外部性和数据多样性如何帮助开发

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI: 10.1145/3603195.3603208

引用次数: 1