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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Moon, Erina Seh-Young Guha, Shion Univ Toronto Toronto ON Canada

ISBN: (纸本)9798400703300

Homelessness is a humanitarian challenge affecting an estimated 1.6 billion people worldwide. In the face of rising homeless populations in developed nations and a strain on social services, government agencies are increasingly adopting data-driven models to determine one's risk of experiencing homelessness and assigning scarce resources to those in need. We conducted a systematic literature review of 57 papers to understand the evolution of these decision-making algorithms. We investigated trends in computational methods, predictor variables, and target outcomes used to develop the models using a human-centered lens and found that only 9 papers (15.7%) investigated model fairness and bias. We uncovered tensions between explainability and ecological validity wherein predictive risk models (53.4%) unduly focused on reductive explainability while resource allocation models (25.9%) were dependent on unrealistic assumptions and simulated data that are not useful in practice. Further, we discuss research challenges and opportunities for developing human-centered algorithms in this area.

关键词： algorithmic decision-making algorithmic bias risk assessments homelessness public sector

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Explaining decision-making Algorithms through UI: Strategies to Help Non-Expert Stakeholders 19

Explaining Decision-Making Algorithms through UI: Strategies...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Cheng, Hao-Fei Wang, Ruotong Zhang, Zheng O'Connell, Fiona Gray, Terrance Harper, F. Maxwell Zhu, Haiyi Univ Minnesota Minneapolis MN 55455 USA Macalester Coll St Paul MN 55105 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781450359702

Increasingly, algorithms are used to make important decisions across society. However, these algorithms are usually poorly understood, which can reduce transparency and evoke negative emotions. In this research, we seek to learn design principles for explanation interfaces that communicate how decision-making algorithms work, in order to help organizations explain their decisions to stakeholders, or to support users' "right to explanation". We conducted an online experiment where 199 participants used different explanation interfaces to understand an algorithm for making university admissions decisions. We measured users' objective and self-reported understanding of the algorithm. Our results show that both interactive explanations and "white-box" explanations (i.e. that show the inner workings of an algorithm) can improve users' comprehension. Although the interactive approach is more effective at improving comprehension, it comes with a trade-off of taking more time. Surprisingly, we also find that users' trust in algorithmic decisions is not affected by the explanation interface or their level of comprehension of the algorithm.

关键词： algorithmic decision-making Explanation Interfaces

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Challenges to large-scale digital organization: the case of Uber

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JOURNAL OF ORGANIZATION DESIGN 2017年第1期6卷 1-12页

作者： Jordan, John M. Penn State Smeal Coll Business Dept Supply Chain & Informat Syst University Pk PA 16802 USA

Mobile computing, the so-called Internet of Things, and the rapid expansion of Internet connectivity all over the world are combining to challenge long-standing assumptions about the mission, function, and reach of traditional organizational forms. Uber is a fast-growing company with several unique attributes: its drivers are not employees, the company does not own the majority of its productive infrastructure, and the management is often at odds with local law and custom. Uber's rapid rise to unprecedented scale serves to illustrate the gaps between traditional organizational assumptions and the reach of current technological capability. To address these gaps, we conclude by suggesting four principles for designing large-scale digital organizations.

关键词： Uber Coordination costs algorithmic decision-making Designing digital organizations Organizations as culture processors

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Explanations as Mechanisms for Supporting algorithmic Transparency

Explanations as Mechanisms for Supporting Algorithmic Transp...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Rader, Emilee Cotter, Kelley Cho, Janghee Michigan State Univ Media & Informat Dept E Lansing MI 48824 USA

ISBN: (纸本)9781450356206

Transparency can empower users to make informed choices about how they use an algorithmic decision-making system and judge its potential consequences. However, transparency is often conceptualized by the outcomes it is intended to bring about, not the specifics of mechanisms to achieve those outcomes. We conducted an online experiment focusing on how different ways of explaining Facebook's News Feed algorithm might affect participants' beliefs and judgments about the News Feed. We found that all explanations caused participants to become more aware of how the system works, and helped them to determine whether the system is biased and if they can control what they see. The explanations were less effective for helping participants evaluate the correctness of the system's output, and form opinions about how sensible and consistent its behavior is. We present implications for the design of transparency mechanisms in algorithmic decision-making systems based on these results.

关键词： algorithmic decision-making transparency explanations

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Beyond Predictive Algorithms in Child Welfare 24

Beyond Predictive Algorithms in Child Welfare

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50th Graphics Interface conference (GI)

作者： Moon, Erina Seh-Young Saxena, Devansh Maharaj, Tegan Guha, Shion Univ Toronto Toronto ON Canada Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9798400718281

Caseworkers in the child welfare (CW) sector use predictive decision-making algorithms built on risk assessment (RA) data to guide and support CW decisions. Researchers have highlighted that RAs can contain biased signals which flatten CW case complexities and that the algorithms may benefit from incorporating contextually rich case narratives, i.e. - the casenotes written by caseworkers. To investigate this hypothesized improvement, we quantitatively deconstructed two commonly used RAs from a United States CW agency. We trained classifier models to compare the predictive validity of RAs with and without casenote narratives and applied computational text analysis on casenotes to highlight topics uncovered in the casenotes. Our study finds that common risk metrics used to assess families and build CWS predictive risk models (PRMs) are unable to predict discharge outcomes for children who are not reunified with their birth parent(s). We also find that although casenotes cannot predict discharge outcomes, they contain contextual case signals. Given the lack of predictive validity of RA scores and casenotes, we propose moving beyond quantitative risk assessments for public sector algorithms and towards using contextual sources of information such as narratives to study public sociotechnical systems.

关键词： algorithmic decision-making algorithmic bias risk assessments child welfare public sector

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Conceptualizing algorithmic Stigmatization 23

Conceptualizing Algorithmic Stigmatization

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Andalibi, Nazanin Pyle, Cassidy Barta, Kristen Xian, Lu Jacobs, Abigail Z. Ackerman, Mark S. Univ Michigan Sch Informat Ann Arbor MI 48109 USA Univ Michigan Ctr Study Complex Syst Ann Arbor MI 48109 USA Univ Michigan Dept Comp Sci & Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9781450394215

algorithmic systems have infiltrated many aspects of our society, mundane to high-stakes, and can lead to algorithmic harms known as representational and allocative. In this paper, we consider what stigma theory illuminates about mechanisms leading to algorithmic harms in algorithmic assemblages. We apply the four stigma elements (i.e., labeling, stereotyping, separation, status loss/discrimination) outlined in sociological stigma theories to algorithmic assemblages in two contexts : 1) "risk prediction" algorithms in higher education, and 2) suicidal expression and ideation detection on social media. We contribute the novel theoretical conceptualization of algorithmic stigmatization as a sociotechnical mechanism that leads to a unique kind of algorithmic harm: algorithmic stigma. Theorizing algorithmic stigmatization aids in identifying theoretically-driven points of intervention to mitigate and/or repair algorithmic stigma. While prior theorizations reveal how stigma governs socially and spatially, this work illustrates how stigma governs sociotechnically.

关键词： stigma algorithms algorithmic decision-making algorithmic harm discrimination labeling stereotyping separation algorithmic stigma representational harm allocative harm higher education risk prediction suicide ideation mental health social media theory

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What's the Appeal? Perceptions of Review Processes for algorithmic decisions 22

What's the Appeal? Perceptions of Review Processes for Algor...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Lyons, Henrietta Wijenayake, Senuri Miller, Tim Velloso, Eduardo Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Univ Sydney Sch Architecture Design & Planning Sydney NSW Australia

ISBN: (纸本)9781450391573

If you were signifcantly impacted by an algorithmic decision, how would you want the decision to be reviewed? In this study, we explore perceptions of review processes for algorithmic decisions that difer across three dimensions: the reviewer, how the review is conducted, and how long the review takes. Using a choice-based conjoint analysis we fnd that people prefer review processes that provide for human review, the ability to participate in the review process, and a timely outcome. Using a survey, we fnd that people also see human review that provides for participation to be the fairest review process. Our qualitative analysis indicates that the fairest review process provides the greatest likelihood of a favourable outcome, an opportunity for the decision subject and their situation to be fully and accurately understood, human involvement, and dignity. These fndings have implications for the design of contestation procedures and also the design of algorithmic decision-making processes.

关键词： algorithmic decision-making contestability reviewability algorithmic fairness accountability and transparency

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Organizational decision-making in the Age of Big Data and Artificial Intelligence

Organizational Decision-Making in the Age of Big Data and Ar...

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作者： Luong, Anh City University of New York

学位级别：Ph.D., Doctor of Philosophy

This dissertation examines organizational decision-making in the context of big data and artificial intelligence (machine learning) technologies. There are three studies. All three focus on collaborative decision-making in organizations, with study 1 examining it in the context of big data, while study 2 and 3 in the context of artificial intelligence. Study 1: This study examines the impact of different manners of presenting information on collaborative decision-making performance. Using controlled economic experiments, I assign participants with a resource allocation decision-making task (adapted from the game theoretic public goods provision problem) and examine the collaborative outcomes of groups when exposed to different levels of information aggregation and visualization formats. Interestingly, the results show that in certain cases, the more effective means of presenting information for individuals (i.e., graphs or tables compared to raw data) do not bode as well for groups. This study contributes to the information visualization literature, which has mostly looked at individual decision-making, by examining the collaborative task context and by combining perspectives from experimental game theory, cognitive fit and information processing theories. Methodologically, this study also contributes to the information visualization literature by accounting for the dynamics of collaborative decision-making over time. Study 2: Organizations increasingly deploy artificial intelligence (AI) systems to automate specific tasks and assist human experts in organizational decision-making. In this study, I focus on complex task settings wherein human decision-makers work with AI systems. Using credit authorization for consumer loans as our specific context, I conduct an economic experiment with a repeated round design to investigate how organizations can create business value from the new human–machine collaborative decision-making paradigm. This study contributes to extant

关键词： algorithmic bias algorithmic decision-making Data visualization Experimental economics Fairness Human-machine collaboration

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'It's Reducing a Human Being to a Percentage';Perceptions of Justice in algorithmic decisions 18

'It's Reducing a Human Being to a Percentage';Perceptions of...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Binns, Reuben Van Kleek, Max Veale, Michael Lyngs, Ulrik Zhao, Jun Shadbolt, Nigel Univ Oxford Dept Comp Sci Oxford England UCL Dept Sci Technol Engn & Publ Policy London England

ISBN: (纸本)9781450356206

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

关键词： algorithmic decision-making explanation justice fairness machine learning transparency

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WHEN POLITICIZATION STOPS ALGORITHMS IN CRIMINAL JUSTICE

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BRITISH JOURNAL OF CRIMINOLOGY 2021年第3期61卷 832-851页

作者： Koenig, Pascal D. Wenzelburger, Georg TU Kaiserslautern Dept Polit Sci Erwin Schrodinger Str 57 D-67653 Kaiserslautern Germany

The present paper sheds light on the conditions for successfully implementing pretrial risk assessment tools by looking at barriers to their implementation and, specifically, political factors that can derail this process. Drawing on theoretical assumptions about the role of agenda-setting and public discourse in policy change, we examine three US states where the implementation of pretrial tools was halted or heavily restricted. Our findings suggest that a politicization of these tools plays a central role for thwarting their implementation. This has less to do with their technical properties or performance. Rather, through reaching high-level politics, those tools are publicly linked to concerns about opacity, fairness and public safety such that it simply becomes politically safer for politicians to stick with the status quo.

关键词： risk assessment algorithmic decision-making pretrial agenda-setting policy termination

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