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4th International Conference onHuman Aspects of IT for the Aged Population (ITAP) held as Part of the 20th International Conference on Human-Computer Interaction (HCI International)

作者： Thorstensen, Erik OsloMet Oslo Metropolitan Univ POB 4 St Olays Plass N-0130 Oslo Norway

ISBN: (纸本)9783319920375;9783319920368

In the field of assisted living technologies, one central strand is to investigate how smart homes might fulfill ambitions for older adults to live longer at home. With the advent of the General Data Protection Regulative (GDPR), there are clear regulations demanding consent to automated decision-making regarding health. This contribution to applied ethics in the field of algorithmic decision-making opens up some of the possible dilemmas in the intersection between the smart home ambition and the GDPR with specific attention to the possible trade-offs between privacy and well-being through a future case, to the learning goals in a future smart home with health detection systems, and presents different approaches to advance consent.

关键词： Privacy Consent Responsible innovation Applied ethics algorithmic decision-making

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Child Welfare System: Interaction of Policy, Practice and Algorithms 20

Child Welfare System: Interaction of Policy, Practice and Al...

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21st ACM International Conference on Supporting Group Work (GROUP)

作者： Saxena, Devansh Guha, Shion Badillo-Urquiola, Karla Wisniewski, Pamela Marquette Univ Milwaukee WI 53233 USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781450367677

This paper focuses on understanding the collaborative work of multi-disciplinary teams in the child welfare system (CWS). CWS workers participate in meetings mediated by policies in place, current child-welfare practice, as well as algorithms that offer recommendations. We conducted 25 observations of these meetings to assess how algorithms aid decision-making in a domain where decisions often come down to the policies and practices in place. Our findings suggest that the algorithm works fairly well at recommending placement settings, however, these recommendations are often overridden because of policy or legal requirements. Moreover, re-appropriation of the placement algorithm to prescribe the rates for foster parents has led to unintended consequences. This poster identifies uses cases of the algorithm in place, scenarios where conflicts arise between the algorithm and policy/practice, as well as how these conflicts are addressed. Our work identifies a need for human-centered algorithms that can better support child welfare practice.

关键词： Child Welfare System algorithmic decision-making Human-Centered Algorithm Design

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Precarity: Modeling the Long Term Effects of Compounded decisions on Individual Instability 21

Precarity: Modeling the Long Term Effects of Compounded Deci...

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4th AAAI/ACM Conference on AI, Ethics, and Society (AIES)

作者： Nokhiz, Pegah Ruwanpathirana, Aravinda Kanchana Patwari, Neal Venkatasubramanian, Suresh Univ Utah Sch Comp Salt Lake City UT 84112 USA Washington Univ McKelvey Sch Engn St Louis MO 63110 USA

ISBN: (纸本)9781450384735

When it comes to studying the impacts of decision making, the research has been largely focused on examining the fairness of the decisions, the long-term effects of the decision pipelines, and utility-based perspectives considering both the decision-maker and the individuals. However, there has hardly been any focus on precarity which is the term that encapsulates the instability in people's lives. That is, a negative outcome can overspread to other decisions and measures of well-being. Studying precarity necessitates a shift in focus - from the point of view of the decision-maker to the perspective of the decision subject. This centering of the subject is an important direction that unlocks the importance of parting with aggregate measures to examine the long-term effects of decision making. To address this issue, in this paper, we propose a modeling framework that simulates the effects of compounded decision-making on precarity over time. Through our simulations, we are able to show the heterogeneity of precarity by the non-uniform ruinous aftereffects of negative decisions on different income classes of the underlying population and how policy interventions can help mitigate such effects.

关键词： Precarity algorithmic decision-making Long-term effects Economics

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The Impact of Functional and Psychological Barriers on Algorithm Aversion - An IRT Perspective 1

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21st International-Federation-of-Information-Processing (IFIP)-Working-Group- 6.11 Conference on e-Business, e-Services and e-Society (I3E)

作者： Mahmud, Hasan Islam, A. K. M. Najmul Mitra, Ranjan Kumar Hasan, Ahmed Rizvan LUT Univ Lappeenranta 53850 Finland Univ Dhaka Dhaka 1000 Bangladesh

ISBN: (数字)9783031153426

ISBN: (纸本)9783031153426;9783031153419

The application of artificial intelligence (AI) in decision-making is regarded as the most impactful disruption in an organization's digitalization. However, the benefits of the algorithmic decision can be leveraged only if the managers of an organization adopt this technology. Research found that despite the superior performance of algorithms, people discount algorithmic decisions either deliberately or unintentionally, a phenomenon known as algorithm aversion. In this regard, the current study seeks to investigate whether managers' innovation resistance, measured by different barriers, has any impact on algorithm aversion. Analyzing the survey data of 167 bank/financial managers, we found that while value barriers, tradition barriers, and image barriers are significantly associated with algorithm aversion, such relationships are absent in the case of usage barriers and risk barriers. The findings of this study have several theoretical and practical implications.

关键词： Algorithm aversion Innovation resistance theory decision making algorithmic decision-making Artificial intelligence

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Creation of the algorithmic management questionnaire: A six-phase scale development process

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HUMAN RESOURCE MANAGEMENT 2024年第1期63卷 25-44页

作者： Parent-Rocheleau, Xavier Parker, Sharon K. K. Bujold, Antoine Gaudet, Marie-Claude HEC Montreal Montreal PQ Canada Curtin Univ Ctr Transformat Work Design Perth WA Australia

There is an increasing body of research on algorithmic management (AM), but the field lacks measurement tools to capture workers' experiences of this phenomenon. Based on existing literature, we developed and validated the algorithmic management questionnaire (AMQ) to measure the perceptions of workers regarding their level of exposure to AM. Across three samples (overall n = 1332 gig workers), we show the content, factorial, discriminant, convergent, and predictive validity of the scale. The final 20-item scale assesses workers' perceived level of exposure to algorithmic: monitoring, goal setting, scheduling, performance rating, and compensation. These dimensions formed a higher order construct assessing overall exposure to algorithmic management, which was found to be, as expected, negatively related to the work characteristics of job autonomy and job complexity and, indirectly, to work engagement. Supplementary analyses revealed that perceptions of exposure to AM reflect the objective presence of AM dimensions beyond individual variations in exposure. Overall, the results suggest the suitability of the AMQ to assess workers' perceived exposure to algorithmic management, which paves the way for further research on the impacts of these rapidly accelerating systems.

关键词： algorithmic control algorithmic decision-making algorithmic management gig work measurement scale

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Reclaiming public service ethics through algorithms: Implications for teaching and development

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TEACHING PUBLIC ADMINISTRATION 2024年第2期42卷 251-268页

作者： Morrison, Alexandra Wellstead, Adam M. Dickinson, Helen Michigan Technol Univ Dept Social Sci Houghton MI USA UNSW Canberra Sch Business Canberra ACT Australia Michigan Technol Univ Dept Social Sci Houghton MI 49931 USA

The use of algorithms and automation of public services is not new, but in recent years there has been a step change in processing power and a decrease in the price of these technologies, which means we are seeing more widespread use. These advances are reframing our perception of what matters in ways that impact the ethical dimensions of day-to-day life. In turn, these changes challenge long-standing assumptions about public service ethics and how it is taught. In this multidisciplinary authored paper, we argue that public service leaders must be attentive to ethical questions that converge around adopting "data-driven" techniques, including algorithmic decision-making ( ). algorithmic and technology focused ethics question assumptions about the current deficits within public service ethics pedagogy in public service programs and university programs and the future direction of the discipline. To do so raises longstanding but neglected questions about the public services' role in the state and recovering what Rohr refers to as the 'ethics of the office.' This, we argue, will have implications for teaching public service ethics.

关键词： Public service ethics algorithmic decision-making training and development

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Measuring the Biases that Matter The Ethical and Casual Foundations for Measures of Fairness in Algorithms 19

Measuring the Biases that Matter The Ethical and Casual Foun...

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ACM Conference on Fairness, Accountability, and Transparency (FAT)

作者： Glymour, Bruce Herington, Jonathan Kansas State Univ Dept Philosophy Manhattan KS 66506 USA

ISBN: (纸本)9781450361255

Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence between class variables and the algorithmic score, conditional on target behaviors (e.g. recidivism or loan default). Fourth, measures of "score-relative error bias" capture probabilistic dependence between class variables and behavior, conditional on score. Several recent discussions have demonstrated a tradeoff between these different measures of algorithmic bias, and at least one recent paper has suggested conditions under which tradeoffs may be minimized. In this paper we use the machinery of causal graphical models to show that, under standard assumptions, the underlying causal relations among variables forces some tradeoffs. We delineate a number of normative considerations that are encoded in different measures of bias, with reference to the philosophical literature on the wrongfulness of disparate treatment and disparate impact. While both kinds of error bias are nominally motivated by concern to avoid disparate impact, we argue that consideration of causal structures shows that these measures are better understood as complicated and unreliable measures of procedural biases (i.e. disparate treatment). Moreover, while procedural bias is indicative of disparate treatment, we show that the measure of procedural bias one ought to adopt is dependent on the account of the wrongfulness of disparate treatment one endorses. Finally, given that neither score-relative nor behavior-relative measur

关键词： algorithmic decision-making fairness casual inference discrimination

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algorithmic ACCOUNTABILITY IN THE making

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SOCIAL PHILOSOPHY AND POLICY 2021年第2期38卷 111-127页

作者： Johnson, Deborah G. Univ Virginia Dept Engn & Soc Charlottesville VA 22903 USA

Algorithms are now routinely used in decision-making;they are potent components in decisions that affect the lives of individuals and the activities of public and private institutions. Although use of algorithms has many benefits, a number of problems have been identified with their use in certain domains, most notably in domains where safety and fairness are important. Awareness of these problems has generated public discourse calling for algorithmic accountability. However, the current discourse focuses largely on algorithms and their opacity. I argue that this reflects a narrow and inadequate understanding of accountability. I sketch an account of accountability that takes accountability to be a social practice constituted by actors, forums, shared beliefs and norms, performativity, and sanctions, and aimed at putting constraints on the exercise of power. On this account, algorithmic accountability is not yet constituted;it is in the making. The account brings to light a set of questions that must be addressed to establish it.

关键词： accountability algorithms algorithmic accountability artificial intelligence algorithmic decision-making social norms

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Metarules, Judgment and the algorithmic Future of Financial Regulation in the UK

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OXFORD JOURNAL OF LEGAL STUDIES 2023年第4期43卷 777-803页

作者： Georgosouli, Andromachi Queen Mary Univ London Ctr Commercial Law Studies CCLS Sch Law London England

UK financial regulators are experimenting with the conversion of rulebook content into machine-readable and executable code. A major driver of these initiatives is the belief that the use of algorithms will eliminate the need for human interpretation as a deliberative process, and that this would be a welcome development because it will improve effectiveness while cutting time and costs for regulators and the industry alike. In this article, I set out to explain why human interpretation should be preserved and further harnessed if data-driven governance is to work at all. To support my thesis, I bring attention to the limited translatability of rulebook content into code, and to the difficulties for machines to engage with the full spectrum of tasks of analogical reasoning. I further contend that it would be desirable to preserve human interpretation on procedural grounds pertaining to the legitimacy of financial regulators. I conclude with recommendations about the future design of the financial rulebooks.

关键词： financial regulation rules digitalisation algorithmic decision-making legitimacy governance code

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Algorithms: avoiding the implementation of institutional biases

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PUBLIC LIBRARY QUARTERLY 2018年第3期37卷 341-347页

作者： Ayre, Lori Craner, Jim Galecia Grp 15 Old Creek Rd Petaluma CA 94952 USA

Computer algorithms, the logic and code that power automated decision-making programs, increasingly dominate many aspects of modern society. There are already many examples of institutional biases - including ideological bias, racism, sexism, ableism being solidified in algorithms, causing harm to already underprivileged populations. This article explores library-specific and society-wide examples as well as efforts to prevent the implementation of these biases in the future.

关键词： algorithmic decision-making algorithms bias filter bubble inadvertent injustice institutional bias machine learning

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