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The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers

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COMPUTERS IN HUMAN BEHAVIOR 2021年 123卷

作者： Langer, Markus Landers, Richard N. Univ Saarland Fachrichtung Psychol Saarbrucken Germany Univ Minnesota Dept Psychol Minneapolis MN USA

Advances in artificial intelligence are increasingly leading to the automation and augmentation of decision processes in work contexts. Although research originally generally focused upon decision-makers, the perspective of those targeted by automated or augmented decisions (whom we call "second parties") and parties who observe the effects of such decisions (whom we call "third parties") is now growing in importance and attention. We review the expanding literature investigating reactions to automated and augmented decision-making by second and third parties. Specifically, we explore attitude (e.g., evaluations of trustworthiness), perception (e.g., fairness perceptions), and behavior (e.g., reverse engineering of automated decision processes) outcomes of second and third parties. Additionally, we explore how characteristics of the a) decision-making process, b) system, c) second and third party, d) task, and e) outputs and outcomes moderate these effects, and provide recommendation for future research. Our review summarizes the state of the literature in these domains, concluding a) that reactions to automated decisions differ across situations in which there is remaining human decision control (i.e., augmentation contexts), b) that system design choices (e.g., transparency) are important but underresearched, and c) that the generalizability of findings might suffer from excessive reliance on specific research methodologies (e.g., vignette studies).

关键词： Automated and augmented decision-making Artificial intelligence algorithmic decision-making Perceptions Attitudes Review paper

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The Human Rights Implications of the Use of AI in the Digital Welfare State: Lessons Learned from the Dutch SyRI Case

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HUMAN RIGHTS LAW REVIEW 2022年第2期22卷

作者： Rachovitsa, Adamantia Johann, Niclas Univ Groningen Fac Law Int Law Groningen Netherlands Univ Edinburgh Scottish Res Ctr IP & Technol Law Sch Law Edinburgh Midlothian Scotland

The article discusses the human rights implications of algorithmic decision-making in the social welfare sphere. It does so against the background of the 2020 Hague's District Court judgment in a case challenging the Dutch government's use of System Risk Indication-an algorithm designed to identify potential social welfare fraud. Digital welfare state initiatives are likely to fall short of meeting basic requirements of legality and protecting against arbitrariness. Moreover, the intentional opacity surrounding the implementation of algorithms in the public sector not only hampers the effective exercise of human rights but also undermines proper judicial oversight. The analysis unpacks the relevance and complementarity of three legal/regulatory frameworks governing algorithmic systems: data protection, human rights law and algorithmic accountability. Notwithstanding these frameworks' invaluable contribution, the discussion casts doubt on whether they are well-suited to address the legal challenges pertaining to the discriminatory effects of the use of algorithmic systems.

关键词： algorithmic decision-making artificial intelligence digital welfare state discriminatory effects data protection right to private life

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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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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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Unveiling algorithmic power: exploring the impact of automated systems on disabled people's engagement with social services

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DISABILITY & SOCIETY 2023年第11期39卷 3004-3029页

作者： van Toorn, Georgia Scully, Jackie Leach Univ New South Wales ARC Ctr Excellence Automated Decis Making Soc ADM Randwick Australia

Points of InterestComputer-based tools, or algorithms, are increasingly being used to assess who is eligible for public disability *** research investigates disabled people's concerns about the use of these tools. It uses a case study from Australia, where algorithmic assessments were recently proposed and trialled by the *** about technology's impact on resource allocation in disability support regimes include insensitivity to individual lived realities and reduction of disability to a score of bodily *** avoid harm to disabled people, the research recommends an approach to assessment that better addresses contextual factors and lived experiences of disability. This article examines the impact of algorithmic systems on disabled people's interactions with social services, focusing on a case study of algorithmic decision-making in Australia's National Disability Insurance Scheme (NDIS). Through interviews and document analysis, we explore future visions and concerns related to increased automation in NDIS planning and eligibility determinations. We show that while individuals may not fully comprehend the inner workings of algorithmic systems, they develop their own understandings and judgments, shedding light on how power operates through the datafication of disability. The article highlights the significance of addressing epistemic justice concerns, urging a reevaluation of dominant modes of understanding and assessing disability through algorithmic categorisation, while advocating for more nuanced approaches that acknowledge disability's embeddedness in social relations. The findings have implications for the future use of algorithmic decision-making in the NDIS and disability welfare provision more broadly.

关键词： automation algorithmic decision-making disability epistemic injustice distributive justice National Disability Insurance Scheme

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Data analytics (ab) use in healthcare fraud audits

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INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS 2021年 42卷

作者： Koreff, Jared Weisner, Martin Sutton, Steve G. Trinity Univ Sch Business 1 Trinity Pl San Antonio TX 78212 USA Univ Melbourne Fac Business & Econ Level 8198 Berkeley St Melbourne Vic 3010 Australia NHH Norwegian Sch Econ Helleveien 30 N-5045 Bergen Norway Univ Cent Florida Orlando FL 32816 USA

This study explores how government-adopted audit data analytic tools promote the abuse of power by auditors enabling politically sensitive processes that encourage industry-wide normalization of behavior. In an audit setting, we investigate how a governmental organization enables algorithmic decision-making to alter power relationships to effect organizational and industry-wide change. While prior research has identified discriminatory threats emanating from the deployment of algorithmic decision-making, the effects of algorithmic decision-making on inherently imbalanced power relationships have received scant attention. Our results provide empirical evidence of how systemic and episodic power relationships strengthen each other, thereby enabling the governmental organization to effect social change that might be too politically prohibitive to enact directly. Overall, the results suggest that there are potentially negative effects caused by the use of algorithmic decision-making and the resulting power shifts, and these effects create a different view of the level of purported success attained through auditor use of data analytics.

关键词： algorithmic decision-making Data analytics Government auditors Healthcare Medical fraud detection Power

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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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Psychology of AI: How AI impacts the way people feel, think, and behave

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CURRENT OPINION IN PSYCHOLOGY 2024年 58卷 101835页

作者： Williams, Gizem Yalcin Lim, Sarah Univ Texas Austin Dept Mkt Austin TX 78712 USA Univ Illinois Dept Business Adm Champaign IL 61821 USA

Over the past decade, artificial intelligence (AI) technologies have transformed numerous facets of our lives. In this article, we summarize key themes in emerging AI research in behavioral science. In doing so, we aim to unravel the multifaceted impacts of AI on people's emotions, cognition, and behaviors, offering nuanced insights into this rapidly evolving landscape. This article concludes with proposing promising avenues for future research, outlining areas for further exploration and methodological approaches to consider.

关键词： Artificial intelligence (AI) algorithmic decision-making Psychology of AI

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Transparent to whom? No algorithmic accountability without a critical audience

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INFORMATION COMMUNICATION & SOCIETY 2019年第14期22卷 2081-2096页

作者： Kemper, Jakko Kolkman, Daan Univ Amsterdam Amsterdam Sch Cultural Anal Spuistr 134 NL-1012 VB Amsterdam Netherlands Tech Univ Eindhoven Jheronimus Acad Data Sci Eindhoven Netherlands

Big data and data science transform organizational decision-making. We increasingly defer decisions to algorithms because machines have earned a reputation of outperforming us. As algorithms become embedded within organizations, they become more influential and increasingly opaque. Those who create algorithms may make arbitrary decisions in all stages of the ?data value chain?, yet these subjectivities are obscured from view. Algorithms come to reflect the biases of their creators, can reinforce established ways of thinking, and may favour some political orientations over others. This is a cause for concern and calls for more transparency in the development, implementation, and use of algorithms in public- and private-sector organizations. We argue that one elementary ? yet key ? question remains largely undiscussed. If transparency is a primary concern, then to whom should algorithms be transparent? We consider algorithms as socio-technical assemblages and conclude that without a critical audience, algorithms cannot be held accountable.

关键词： Data science algorithms transparency algorithmic accountability algorithmic decision-making glitch studies

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People's reactions to decisions by human vs. algorithmic decision-makers: the role of explanations and type of selection tests

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EUROPEAN JOURNAL OF WORK AND ORGANIZATIONAL PSYCHOLOGY 2024年第2期33卷 146-157页

作者： Wesche, Jenny S. Hennig, Frederike Kollhed, Christopher Sebastian Quade, Jessica Kluge, Soren Sonderegger, Andreas Free Univ Berlin Sozial Org & Wirtschaftspsychol Berlin Germany Bern Univ Appl Sci Business Bern Switzerland

Research suggests that people prefer human over algorithmic decision-makers at work. Most of these studies, however, use hypothetical scenarios and it is unclear whether such results replicate in more realistic contexts. We conducted two between-subjects studies (N=270;N=183) in which the decision-maker (human vs. algorithmic, Study 1 and 2), explanations regarding the decision- process (yes vs. no, Study 1 and 2), and the type of selection test (requiring human vs. mechanical skills for evaluation, Study 2) were manipulated. While Study 1 was based on a hypothetical scenario, participants in pre-registered Study 2 volunteered to participate in a qualifying session for an attractively remunerated product test, thus competing for real incentives. In both studies, participants in the human condition reported higher levels of trust and acceptance. Providing explanations also positively influenced trust, acceptance, and perceived transparency in Study 1, while it did not exert any effect in Study 2. Type of the selection test affected fairness ratings, with higher ratings for tests requiring human vs. mechanical skills for evaluation. Results show that algorithmic decision-making in personnel selection can negatively impact trust and acceptance both in studies with hypothetical scenarios as well as studies with real incentives.

关键词： algorithmic decision-making ADM technology acceptance trust explainable AI

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