Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society最新文献_第8页

Human Comprehension of Fairness in Machine Learning 机器学习中人类对公平的理解

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-12-17 DOI: 10.1145/3375627.3375819

Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz

{"title":"Human Comprehension of Fairness in Machine Learning","authors":"Debjani Saha, Candice Schumann, Duncan C. McElfresh, John P. Dickerson, Michelle L. Mazurek, Michael Carl Tschantz","doi":"10.1145/3375627.3375819","DOIUrl":"https://doi.org/10.1145/3375627.3375819","url":null,"abstract":"Bias in machine learning has manifested injustice in several areas, with notable examples including gender bias in job-related ads [4], racial bias in evaluating names on resumes [3], and racial bias in predicting criminal recidivism [1]. In response, research into algorithmic fairness has grown in both importance and volume over the past few years. Different metrics and approaches to algorithmic fairness have been proposed, many of which are based on prior legal and philosophical concepts [2]. The rapid expansion of this field makes it difficult for professionals to keep up, let alone the general public. Furthermore, misinformation about notions of fairness can have significant legal implications. Computer scientists have largely focused on developing mathematical notions of fairness and incorporating them in fielded ML systems. A much smaller collection of studies has measured public perception of bias and (un)fairness in algorithmic decision-making. However, one major question underlying the study of ML fairness remains unanswered in the literature: Does the general public understand mathematical definitions of ML fairness and their behavior in ML applications? We take a first step towards answering this question by studying non-expert comprehension and perceptions of one popular definition of ML fairness, demographic parity [5]. Specifically, we developed an online survey to address the following: (1) Does a non-technical audience comprehend the definition and implications of demographic parity? (2) Do demographics play a role in comprehension? (3) How are comprehension and sentiment related? (4) Does the application scenario affect comprehension?","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78540446","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: 12

AI and Holistic Review: Informing Human Reading in College Admissions 人工智能和整体评论:在大学招生中告知人类阅读

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-12-17 DOI: 10.1145/3375627.3375871

AJ Alvero, Noah Arthurs, A. Antonio, B. Domingue, Ben Gebre-Medhin, Sonia Giebel, M. Stevens

引用次数: 17

Balancing the Tradeoff Between Clustering Value and Interpretability 平衡聚类值和可解释性之间的权衡

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-12-17 DOI: 10.1145/3375627.3375843

Sandhya Saisubramanian, Sainyam Galhotra, S. Zilberstein

{"title":"Balancing the Tradeoff Between Clustering Value and Interpretability","authors":"Sandhya Saisubramanian, Sainyam Galhotra, S. Zilberstein","doi":"10.1145/3375627.3375843","DOIUrl":"https://doi.org/10.1145/3375627.3375843","url":null,"abstract":"Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This paper addresses the problem of generating interpretable clusters, given features of interest that signify interpretability to an end-user, by optimizing interpretability in addition to common clustering objectives. We propose a β-interpretable clustering algorithm that ensures that at least β fraction of nodes in each cluster share the same feature value. The tunable parameter β is user-specified. We also present a more efficient algorithm for scenarios with β!=!1$ and analyze the theoretical guarantees of the two algorithms. Finally, we empirically demonstrate the benefits of our approaches in generating interpretable clusters using four real-world datasets. The interpretability of the clusters is complemented by generating simple explanations denoting the feature values of the nodes in the clusters, using frequent pattern mining.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81039764","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: 29

Learning Norms from Stories: A Prior for Value Aligned Agents 从故事中学习规范:价值一致主体的先验

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-12-07 DOI: 10.1145/3375627.3375825

Spencer Frazier, Md Sultan Al Nahian, Mark O. Riedl, Brent Harrison

引用次数: 28

Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments 人工智能中的艰难选择:通过社会技术承诺解决规范不确定性

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-11-20 DOI: 10.1145/3375627.3375861

Roel Dobbe, T. Gilbert, Yonatan Dov Mintz

引用次数: 11

The AI Liability Puzzle and a Fund-Based Work-Around 人工智能责任难题和基于基金的解决方案

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-11-18 DOI: 10.1145/3375627.3375806

Olivia J. Erd'elyi, G'abor Erd'elyi

{"title":"The AI Liability Puzzle and a Fund-Based Work-Around","authors":"Olivia J. Erd'elyi, G'abor Erd'elyi","doi":"10.1145/3375627.3375806","DOIUrl":"https://doi.org/10.1145/3375627.3375806","url":null,"abstract":"Certainty around the regulatory environment is crucial to facilitate responsible AI innovation and its social acceptance. However, the existing legal liability system is inapt to assign responsibility where a potentially harmful conduct and/or the harm itself are unforeseeable, yet some instantiations of AI and/or the harms they may trigger are not foreseeable in the legal sense. The unpredictability of how courts would handle such cases makes the risks involved in the investment and use of AI incalculable, creating an environment that is not conducive to innovation and may deprive society of some benefits AI could provide. To tackle this problem, we propose to draw insights from financial regulatory best-practices and establish a system of AI guarantee schemes. We envisage the system to form part of the broader market-structuring regulatory framework, with the primary function to provide a readily available, clear, and transparent funding mechanism to compensate claims that are either extremely hard or impossible to realize via conventional litigation. We propose at least partial industry-funding, with funding arrangements depending on whether it would pursue other potential policy goals.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80985175","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: 8

"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations “我怎么骗得了你?”:通过误导性黑匣子解释操纵用户信任

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-11-15 DOI: 10.1145/3375627.3375833

Himabindu Lakkaraju, O. Bastani

引用次数: 184

Fair Allocation through Selective Information Acquisition 通过选择性信息获取实现公平分配

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-11-07 DOI: 10.1145/3375627.3375823

William Cai, Johann D. Gaebler, Nikhil Garg, Sharad Goel

{"title":"Fair Allocation through Selective Information Acquisition","authors":"William Cai, Johann D. Gaebler, Nikhil Garg, Sharad Goel","doi":"10.1145/3375627.3375823","DOIUrl":"https://doi.org/10.1145/3375627.3375823","url":null,"abstract":"Public and private institutions must often allocate scarce resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelihood of repaying a loan. But when the quality of information differs across candidates (e.g., if some applicants lack traditional credit histories), common lending strategies can lead to disparities across groups. Here we consider a setting in which decision makers---before allocating resources---can choose to spend some of their limited budget further screening select individuals. We present a computationally efficient algorithm for deciding whom to screen that maximizes a standard measure of social welfare. Intuitively, decision makers should screen candidates on the margin, for whom the additional information could plausibly alter the allocation. We formalize this idea by showing the problem can be reduced to solving a series of linear programs. Both on synthetic and real-world datasets, this strategy improves utility, illustrating the value of targeted information acquisition in such decisions. Further, when there is social value for distributing resources to groups for whom we have a priori poor information---like those without credit scores---our approach can substantially improve the allocation of limited assets.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86362598","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: 23

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods 愚弄LIME和SHAP:对事后解释方法的对抗性攻击

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-11-06 DOI: 10.1145/3375627.3375830

Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju

{"title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","authors":"Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju","doi":"10.1145/3375627.3375830","DOIUrl":"https://doi.org/10.1145/3375627.3375830","url":null,"abstract":"As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77347056","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: 516

CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models 一个提供解释和分析黑盒模型公平性和鲁棒性的通用框架

Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2019-05-20 DOI: 10.1145/3375627.3375812

Shubham Sharma, Jette Henderson, Joydeep Ghosh

{"title":"CERTIFAI: A Common Framework to Provide Explanations and Analyse the Fairness and Robustness of Black-box Models","authors":"Shubham Sharma, Jette Henderson, Joydeep Ghosh","doi":"10.1145/3375627.3375812","DOIUrl":"https://doi.org/10.1145/3375627.3375812","url":null,"abstract":"Concerns within the machine learning community and external pressures from regulators over the vulnerabilities of machine learning algorithms have spurred on the fields of explainability, robustness, and fairness. Often, issues in explainability, robustness, and fairness are confined to their specific sub-fields and few tools exist for model developers to use to simultaneously build their modeling pipelines in a transparent, accountable, and fair way. This can lead to a bottleneck on the model developer's side as they must juggle multiple methods to evaluate their algorithms. In this paper, we present a single framework for analyzing the robustness, fairness, and explainability of a classifier. The framework, which is based on the generation of counterfactual explanations through a custom genetic algorithm, is flexible, model-agnostic, and does not require access to model internals. The framework allows the user to calculate robustness and fairness scores for individual models and generate explanations for individual predictions which provide a means for actionable recourse (changes to an input to help get a desired outcome). This is the first time that a unified tool has been developed to address three key issues pertaining towards building a responsible artificial intelligence system.","PeriodicalId":93612,"journal":{"name":"Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75945728","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