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Are algorithms always fair? The study on public preferences toward algorithmic decision-making: A case study from the perspectives of decision scenarios and social roles

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ECONOMICS & POLITICS 2025年第1期37卷 420-441页

作者： Wang, Bing Luo, Longxiang Wang, Xiuli Tsinghua Univ Sch Journalism & Commun Beijing Peoples R China Univ Illinoisat Urbana Champaign Coll Media Champaign IL USA Peking Univ Sch New Media Beijing Peoples R China Univ Calif Davis Dept Commun Davis CA USA

The integration of algorithmic decision-making into daily life gives rise to a need to understand public attitudes toward this phenomenon. This study uses online experiments to explore how decision scenarios and roles influence public preferences for algorithms. In-depth interviews were conducted to examine interpretations of algorithmic fairness. The findings indicate a preference for algorithms, yet a stronger preference for human decision-making in ethically complex scenarios. decision-makers demonstrate greater acceptance of algorithms. Participants perceive algorithmic fairness from social and technical perspectives, emphasizing autonomy and transparency. Despite a general preference for algorithms, concerns persist, revealing a nuanced view of algorithmic fairness as a form of societal power.

关键词： algorithmic decision-making algorithmic preference human-machine relations online experiment public decision-making

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Province of Origin, decision-making Bias, and Responses to Bureaucratic Versus algorithmic decision-making

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PUBLIC ADMINISTRATION REVIEW 2025年第0期

作者： Wang, Ge Zhang, Zhejun Xie, Shenghua Guo, Yue Cent China Normal Univ Fac Polit Sci Sch Publ Adm Wuhan Peoples R China Beijing Normal Univ Sch Govt Beijing Peoples R China

As algorithmic decision-making (ADM) becomes prevalent in certain public sectors, its interaction with traditional bureaucratic decision-making (BDM) evolves, especially in contexts shaped by regional identities and decision-making biases. To explore these dynamics, we conducted two survey experiments within traffic enforcement scenarios, involving 4816 participants across multiple provinces. Results indicate that non-native residents perceived ADM as fairer and more acceptable than BDM when they did not share a province of origin with local bureaucrats. Both native and non-native residents showed a preference for ADM in the presence of bureaucratic and algorithmic biases but preferred BDM when such biases were absent. When bureaucratic and algorithmic biases coexisted, the lack of a shared province of origin further reinforced non-native residents' perception of ADM as fairer and more acceptable than BDM. Our findings reveal the complex interplay among province of origin, decision-making biases, and responses to different decision-making approaches.

关键词： algorithmic decision-making bureaucratic decision-making decision-making bias province of origin representative bureaucracy

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Unveiling civil servants' preferences: Human-machine matching vs. regulating algorithms in algorithmic decision-making--Insights from a survey experiment

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GOVERNMENT INFORMATION QUARTERLY 2025年第1期42卷

作者： Li, Huanhuan Sun, Zongfeng Xi, Jiacheng Shandong Univ Sch Polit Sci & Publ Adm Qingdao Campus Jinan 266237 Peoples R China Sun Yat Sen Univ Sch Govt Guangzhou 510275 Peoples R China

While research has explored trust in algorithmic decision-making, the factors shaping civil servants' trust perceptions remain underexamined. Using public value theory and technology adoption frameworks, this study employs a survey experiment to analyze the effects of human-machine matching and algorithm regulation on civil servants' trust and adoption inclination. The findings indicate that both factors independently influence adoption inclination, with trust perceptions mediating this relationship, but no interaction effect is observed. Addressing gaps in technology acceptance and ethical frameworks, this study highlights the importance of algorithm regulation and human-machine matching in advancing algorithmic governance and achieving public value through procedural and performance dimensions, offering practical implications for policy and governance.

关键词： algorithmic decision-making Human-machine matching Algorithm regulation Trust perception Usage inclinations

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algorithmic decision-making and system destructiveness: A case of automatic debt recovery

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EUROPEAN JOURNAL OF INFORMATION SYSTEMS 2022年第3期31卷 313-338页

作者： Rinta-Kahila, Tapani Someh, Ida Gillespie, Nicole Indulska, Marta Gregor, Shirley Univ Queensland Business Sch Business Informat Syst Brisbane Qld Australia Univ Queensland Business Sch Management Brisbane Qld Australia Australian Natl Univ Fac Business & Econ Res Sch Management Canberra ACT Australia

Governments are increasingly relying on algorithmic decision-making (ADM) to deliver public services. Recent information systems literature has raised concerns regarding ADM's negative unintended consequences, such as widespread discrimination, which in extreme cases can be destructive to society. The extant empirical literature, however, has not sufficiently examined the destructive effects of governmental ADM. In this paper, we report on a case study of the Australian government's "Robodebt" programme that was designed to automatically calculate and collect welfare overpayment debts from citizens but ended up causing severe distress to citizens and welfare agency staff. Employing perspectives from systems thinking and organisational limits, we develop a research model that explains how a socially destructive government ADM programme was initiated, sustained, and delegitimized. The model offers a set of generalisable mechanisms that can benefit investigations of ADM's consequences. Our findings contribute to the literature of unintended consequences of ADM and demonstrate to practitioners the importance of setting up robust governance infrastructures for ADM programmes.

关键词： algorithmic decision-making Destructive systems Organisational limits Systems thinking Case study

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algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence

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ORGANIZATION 2019年第5期26卷 655-672页

作者： Bader, Verena Kaiser, Stephan Bundeswehr Univ Munich Human Resources & Org Neubiberg Germany Bundeswehr Univ Munich Sch Econ & Management Werner Heisenberg Weg 39 D-85579 Neubiberg Germany Bundeswehr Univ Munich Neubiberg Germany

Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans' attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans' decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.

关键词： algorithmic and human-based intelligence algorithmic decision-making artificial intelligence interface workplace decisions

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algorithmic decision-making, Agency Costs, and Institution-Based Trust

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Philosophy and Technology 2024年第2期37卷 68页

作者： Dowding, Keith Taylor, Brad R. School of Politics and International Relations Australian National University Canberra ACT Australia School of Business & amp School of Humanities and Communication University of Southern Queensland Springfield Central QLD Australia

Algorithm decision making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives. © The Author(s) 2024.

关键词： algorithmic decision-making Asymmetric Information Explainable AI Institution-based Trust Principal-agent Theory

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algorithmic decision-making, Statistical Evidence and the Rule of Law

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EPISTEME-A JOURNAL OF INDIVIDUAL AND SOCIAL EPISTEMOLOGY 2024年第4期21卷 1241-1264页

作者： Chiao, Vincent Univ Toronto Fac Law Toronto ON Canada

The rapidly increasing role of automation throughout the economy, culture and our personal lives has generated a large literature on the risks of algorithmic decision-making, particularly in high-stakes legal settings. algorithmic tools are charged with bias, shrouded in secrecy, and frequently difficult to interpret. However, these criticisms have tended to focus on particular implementations, specific predictive techniques, and the idiosyncrasies of the American legal-regulatory regime. They do not address the more fundamental unease about the prospect that we might one day replace judges with algorithms, no matter how fair, transparent, and intelligible they become. The aim of this paper is to propose an account of the source of that unease, and to evaluate its plausibility. I trace foundational unease with algorithmic decision-making in the law to the powerful intuition that there is a basic moral and legal difference between showing that something is true of many people just like you and showing that it is true of you. Human judgment attends to the exception;automation insists on blindly applying the rule. I show how this intuitive thought is connected to both epistemological arguments about the value of statistical evidence, as well as to court-centered conceptions of the rule of law. Unease with algorithmic decision-making in the law thus draws on an intuitive principle that underpins a disparate range of views in legal philosophy. This suggests the principle is deeply ingrained. Nonetheless, I argue that the powerful intuition is not as decisive as it may seem, and indeed runs into significant epistemological and normative challenges. At an epistemological level, I show how concerns about statistical evidence's ability to track the truth can be resolved by adopting a probabilistic, rather than modal, conception of truth-tracking. At a normative level, commitment to highly individualized decision-making co-exists with equally ingrained and competing principle

关键词： statistical evidence rule of law algorithmic decision-making legal proof

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Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of 'datification'

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JOURNAL OF STRATEGIC INFORMATION SYSTEMS 2015年第1期24卷 3-14页

作者： Newell, Sue Marabelli, Marco Univ Sussex Sch Business Management & Econ Brighton BN1 9RH E Sussex England Bentley Univ IPM Dept Waltham MA 02452 USA

Today, digital data are captured through a variety of devices that have the ability to monitor the minutiae of an individual's everyday life. These data are often processed by algorithms, which support (or drive) decisions (termed 'algorithmic decision-making' in this article). While the strategic value of these data (and subsequent analysis) for businesses is unquestionable, the implications for individuals and wider society are less clear. Therefore, in this Viewpoint article we aim to shed light on the tension between businesses - that increasingly profile customers and personalize products and services - and individuals, who, as McAfee and Blynjolfsson (2012, p. 5) suggest, are 'walking data generators' but are often unaware of how the data they produce are being used, and by whom and with what consequences. Issues associated with privacy, control and dependence arise, suggesting that social and ethical concerns related to the way business is strategically exploiting digitized technologies that increasingly support our everyday activities should be brought to the fore and thoughtfully discussed. In this article we aim to lay a foundation for this discussion in the IS community and beyond. (C) 2015 Elsevier B.V. All rights reserved.

关键词： algorithmic decision-making Digital traces Sensors Strategic Social and ethical issues

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WHEN JUSTICE IS BLIND TO ALGORITHMS : MULTILAYERED BLACKBOXING OF algorithmic decision-making IN THE PUBLIC SECTOR

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MIS QUARTERLY 2024年第4期48卷 1637-1662页

作者： Kronblad, Charlotta Essen, Anna Mahring, Magnus Gothenburg Univ Dept Appl Informat Technol Gothenburg Sweden Stockholm Sch Econ Dept Entrepreneurship Innovat & Technol House Inno Stockholm Sweden

Both research and public discourse have recently drawn attention to the downsides of algorithmic decision- making (ADM), highlighting how it can produce biased and discriminatory outcomes and also pose threats to social justice. We address such threats that emanate from but also go beyond algorithms per se, extending to how public agencies and legal institutions respond or fail to respond to the consequences of ADM. Drawing on a case study of the use of an ADM system in public school administration, we explore the practices through which public institutions avoided engagement with the detrimental consequences ofADM, leading to injustice. We provide a conceptual model outlining how organizational ignoring practices can lead to social and institutional blackboxing of an ADM system, engendering both social and legal injustice. Our work paves the way for interdisciplinary research on the multilayered blackboxing of ADM. We also extend algorithmic injustice research to include a legal dimension and provide practical implications in the form of a legal framework for ADM in the public sector.

关键词： algorithmic decision-making social justice organizational ignoring institutional blindness multilayered blackboxing legal justice public sector

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INFORMATION SYSTEMS FRONTIERS 2021年第2期23卷 477-494页

作者： Clapham, Benjamin Siering, Michael Gomber, Peter Goethe Univ Frankfurt Theodor W Adorno Pl 4 D-60323 Frankfurt Germany

algorithmic decision-making plays an important role in financial markets. Current tools in trading focus on popular companies which are discussed in thousands of news items. However, it remains unclear whether methodologies from the field of data analytics relying on large samples can also be applied to small datasets of less popular companies or whether these methodologies lead to the discovery of meaningless patterns resulting in economic losses. We analyze whether the impact of media sentiment on financial markets is influenced by two levels of investor attention and whether this impacts algorithmic decision-making. We find that the influence differs substantially between news and companies with high and low investor attention. We apply a trading simulation to outline the practical consequences of these interrelations for decision support systems. Our results are of high importance for financial market participants, especially for algorithmic traders that consider sentiment for investment decision support.

关键词： algorithmic decision-making decision support systems Sentiment analysis Investor attention Trading strategies Trading simulation

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