Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society最新文献

Ethical Obligations to Provide Novelty 提供新颖性的道德义务

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462555

Paige Golden, D. Danks

引用次数: 1

Designing Shapelets for Interpretable Data-Agnostic Classification 为可解释的数据不可知分类设计Shapelets

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462553

Riccardo Guidotti, A. Monreale

{"title":"Designing Shapelets for Interpretable Data-Agnostic Classification","authors":"Riccardo Guidotti, A. Monreale","doi":"10.1145/3461702.3462553","DOIUrl":"https://doi.org/10.1145/3461702.3462553","url":null,"abstract":"Time series shapelets are discriminatory subsequences which are representative of a class, and their similarity to a time series can be used for successfully tackling the time series classification problem. The literature shows that Artificial Intelligence (AI) systems adopting classification models based on time series shapelets can be interpretable, more accurate, and significantly fast. Thus, in order to design a data-agnostic and interpretable classification approach, in this paper we first extend the notion of shapelets to different types of data, i.e., images, tabular and textual data. Then, based on this extended notion of shapelets we propose an interpretable data-agnostic classification method. Since the shapelets discovery can be time consuming, especially for data types more complex than time series, we exploit a notion of prototypes for finding candidate shapelets, and reducing both the time required to find a solution and the variance of shapelets. A wide experimentation on datasets of different types shows that the data-agnostic prototype-based shapelets returned by the proposed method empower an interpretable classification which is also fast, accurate, and stable. In addition, we show and we prove that shapelets can be at the basis of explainable AI methods.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125630995","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: 2

Person, Human, Neither: The Dehumanization Potential of Automated Image Tagging 人，人，两者都不是:自动图像标记的非人性化潜力

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462567

Pinar Barlas, K. Kyriakou, S. Kleanthous, Jahna Otterbacher

引用次数: 4

Fairness and Data Protection Impact Assessments 公平和数据保护影响评估

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462528

A. Kasirzadeh, Damian Clifford

{"title":"Fairness and Data Protection Impact Assessments","authors":"A. Kasirzadeh, Damian Clifford","doi":"10.1145/3461702.3462528","DOIUrl":"https://doi.org/10.1145/3461702.3462528","url":null,"abstract":"In this paper, we critically examine the effectiveness of the requirement to conduct a Data Protection Impact Assessment (DPIA) in Article 35 of the General Data Protection Regulation (GDPR) in light of fairness metrics. Through this analysis, we explore the role of the fairness principle as introduced in Article 5(1)(a) and its multifaceted interpretation in the obligation to conduct a DPIA. Our paper argues that although there is a significant theoretical role for the considerations of fairness in the DPIA process, an analysis of the various guidance documents issued by data protection authorities on the obligation to conduct a DPIA reveals that they rarely mention the fairness principle in practice. Our analysis questions this omission, and assesses the capacity of fairness metrics to be truly operationalized within DPIAs. We conclude by exploring the practical effectiveness of DPIA with particular reference to (1) technical challenges that have an impact on the usefulness of DPIAs irrespective of a controller's willingness to actively engage in the process, (2) the context dependent nature of the fairness principle, and (3) the key role played by data controllers in the determination of what is fair.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116334079","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

Governing Algorithmic Systems with Impact Assessments: Six Observations 治理算法系统与影响评估:六个观察

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462580

E. A. Watkins, E. Moss, Jacob Metcalf, Ranjit Singh, M. C. Elish

{"title":"Governing Algorithmic Systems with Impact Assessments: Six Observations","authors":"E. A. Watkins, E. Moss, Jacob Metcalf, Ranjit Singh, M. C. Elish","doi":"10.1145/3461702.3462580","DOIUrl":"https://doi.org/10.1145/3461702.3462580","url":null,"abstract":"Algorithmic decision-making and decision-support systems (ADS) are gaining influence over how society distributes resources, administers justice, and provides access to opportunities. Yet collectively we do not adequately study how these systems affect people or document the actual or potential harms resulting from their integration with important social functions. This is a significant challenge for computational justice efforts of measuring and governing AI systems. Impact assessments are often used as instruments to create accountability relationships and grant some measure of agency and voice to communities affected by projects with environmental, financial, and human rights ramifications. Applying these tools-through Algorithmic Impact Assessments (AIA)-is a plausible way to establish accountability relationships for ADSs. At the same time, what an AIA would entail remains under-specified; they raise as many questions as they answer. Choices about the methods, scope, and purpose of AIAs structure the conditions of possibility for AI governance. In this paper, we present our research on the history of impact assessments across diverse domains, through a sociotechnical lens, to present six observations on how they co-constitute accountability. Decisions about what type of effects count as an impact; when impacts are assessed; whose interests are considered; who is invited to participate; who conducts the assessment; how assessments are made publicly available, and what the outputs of the assessment might be; all shape the forms of accountability that AIAs engender. Because AlAs are still an incipient governance strategy, approaching them as social constructions that do not require a single or universal approach offers a chance to produce interventions that emerge from careful deliberation.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124603851","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

Fairness and Machine Fairness 公平与机器公平

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462577

Clinton Castro, David R. O'Brien, Ben Schwan

引用次数: 1

Machine Learning and the Meaning of Equal Treatment 机器学习与平等对待的意义

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462556

J. Simons, Sophia Adams Bhatti, Adrian Weller

{"title":"Machine Learning and the Meaning of Equal Treatment","authors":"J. Simons, Sophia Adams Bhatti, Adrian Weller","doi":"10.1145/3461702.3462556","DOIUrl":"https://doi.org/10.1145/3461702.3462556","url":null,"abstract":"Approaches to non-discrimination are generally informed by two principles: striving for equality of treatment, and advancing various notions of equality of outcome. We consider when and why there are trade-offs in machine learning between respecting formalistic interpretations of equal treatment and advancing equality of outcome. Exploring a hypothetical discrimination suit against Facebook, we argue that interpretations of equal treatment which require blindness to difference may constrain how machine learning can be deployed to advance equality of outcome. When machine learning models predict outcomes that are unevenly distributed across racial groups, using those models to advance racial justice will often require deliberately taking race into account. We then explore the normative stakes of this tension. We describe three pragmatic policy options underpinned by distinct interpretations and applications of equal treatment. A status quo approach insists on blindness to difference, permitting the design of machine learning models that compound existing patterns of disadvantage. An industry-led approach would specify a narrow set of domains in which institutions were permitted to use protected characteristics to actively reduce inequalities of outcome. A government-led approach would impose positive duties that require institutions to consider how best to advance equality of outcomes and permit the use of protected characteristics to achieve that goal. We argue that while machine learning offers significant possibilities for advancing racial justice and outcome-based equality, harnessing those possibilities will require a shift in the normative commitments that underpin the interpretation and application of equal treatment in non-discrimination law and the governance of machine learning.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126750912","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: 10

The Ethics of Datasets: Moving Forward Requires Stepping Back 数据集伦理:前进需要后退

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462643

Arvind Narayanan

引用次数: 2

Monitoring AI Services for Misuse 监控人工智能服务的滥用

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462566

S. A. Javadi, Chris Norval, Richard Cloete, Jatinder Singh

{"title":"Monitoring AI Services for Misuse","authors":"S. A. Javadi, Chris Norval, Richard Cloete, Jatinder Singh","doi":"10.1145/3461702.3462566","DOIUrl":"https://doi.org/10.1145/3461702.3462566","url":null,"abstract":"Given the surge in interest in AI, we now see the emergence of Artificial Intelligence as a Service (AIaaS). AIaaS entails service providers offering remote access to ML models and capabilities at arms-length', through networked APIs. Such services will grow in popularity, as they enable access to state-of-the-art ML capabilities, 'on demand', 'out of the box', at low cost and without requiring training data or ML expertise. However, there is much public concern regarding AI. AIaaS raises particular considerations, given there is much potential for such services to be used to underpin and drive problematic, inappropriate, undesirable, controversial, or possibly even illegal applications. A key way forward is through service providers monitoring their AI services to identify potential situations of problematic use. Towards this, we elaborate the potential for 'misuse indicators' as a mechanism for uncovering patterns of usage behaviour warranting consideration or further investigation. We introduce a taxonomy for describing these indicators and their contextual considerations, and use exemplars to demonstrate the feasibility analysing AIaaS usage to highlight situations of possible concern. We also seek to draw more attention to AI services and the issues they raise, given AIaaS' increasing prominence, and the general calls for the more responsible and accountable use of AI.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134582703","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: 9

Face Mis-ID: An Interactive Pedagogical Tool Demonstrating Disparate Accuracy Rates in Facial Recognition 面部识别错误:一种展示面部识别不同准确率的交互式教学工具

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society Pub Date : 2021-07-21 DOI: 10.1145/3461702.3462627

Daniella Raz, Corinne Bintz, Vivian Guetler, Aaron Tam, Michael A. Katell, Dharma Dailey, Bernease Herman, P. Krafft, Meg Young

{"title":"Face Mis-ID: An Interactive Pedagogical Tool Demonstrating Disparate Accuracy Rates in Facial Recognition","authors":"Daniella Raz, Corinne Bintz, Vivian Guetler, Aaron Tam, Michael A. Katell, Dharma Dailey, Bernease Herman, P. Krafft, Meg Young","doi":"10.1145/3461702.3462627","DOIUrl":"https://doi.org/10.1145/3461702.3462627","url":null,"abstract":"This paper reports on the making of an interactive demo to illustrate algorithmic bias in facial recognition. Facial recognition technology has been demonstrated to be more likely to misidentify women and minoritized people. This risk, among others, has elevated facial recognition into policy discussions across the country, where many jurisdictions have already passed bans on its use. Whereas scholarship on the disparate impacts of algorithmic systems is growing, general public awareness of this set of problems is limited in part by the illegibility of machine learning systems to non-specialists. Inspired by discussions with community organizers advocating for tech fairness issues, we created the Face Mis-ID Demo to reveal the algorithmic functions behind facial recognition technology and to demonstrate its risks to policymakers and members of the community. In this paper, we share the design process behind this interactive demo, its form and function, and the design decisions that honed its accessibility, toward its use for improving legibility of algorithmic systems and awareness of the sources of their disparate impacts.","PeriodicalId":197336,"journal":{"name":"Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117144854","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