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Introduction to special issue algorithmic transparency in government: Towards a multi-level perspective

INFORMATION POLITY 2020年第4期25卷 409-417页

作者： Giest, Sarah Grimmelikhuijsen, Stephan Leiden Univ Inst Publ Adm Leiden Netherlands Univ Utrecht Sch Governance Utrecht Netherlands

The editorial sets the stage for the special issue on algorithmic transparency in government. The papers in the issue bring together transparency challenges experienced across different levels of government, including macro-, meso-, and microlevels. This highlights that transparency issues transcend different levels of government - from European regulation to individual public bureaucrats. With a special focus on these links, the editorial sketches a future research agenda for transparency-related challenges. Highlighting these linkages is a first step towards seeing the bigger picture of why transparency mechanisms are put in place in some scenarios and not in others. Finally, this introduction present an agenda for future research, which opens the door to comparative analyses for future research and new insights for policymakers.

关键词： algorithmic decision-making Artificial Intelligence multi-level analysis regulatory context organizational norms discretion

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"Because AI is 100% right and safe": User Attitudes and Sources of AI Authority in India 22

"Because AI is 100% right and safe": User Attitudes and Sour...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Kapania, Shivani Siy, Oliver Clapper, Gabe Meena, Azhagu S. P. Sambasivan, Nithya Google Res Bengaluru India Google Res Cambridge MA USA Google Res Seattle WA USA

ISBN: (纸本)9781450391573

Most prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority-AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.

关键词： artificial intelligence perceptions of AI algorithmic decision-making India

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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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Why the Fine, AI? The Effect of Explanation Level on Citizens' Fairness Perception of AI-based Discretion in Public Administrations 24

Why the Fine, AI? The Effect of Explanation Level on Citizen...

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ACM CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Aljuneidi, Saja Heuten, Wilko Abdenebaoui, Larbi Wolters, Maria Boll, Susanne OFFIS Inst Informat Technol Oldenburg Germany Carl von Ossietzky Univ Oldenburg Oldenburg Germany

ISBN: (纸本)9798400703300

The integration of Artificial Intelligence into decision-making processes within public administration extends to AI-systems that exercise administrative discretion. This raises fairness concerns among citizens, possibly leading to AI-systems abandonment. Uncertainty persists regarding explanation elements impacting citizens' perception of fairness and technology adoption level. In a video-vignette online-survey (N=847), we investigated the impact of explanation levels on citizens' perceptions of informational fairness, distributive fairness, and system adoption level. We enhanced explanations in three stages: none, factor explanations, culminating in factor importance explanations. We found that more detailed explanations improved informational and distributive fairness perceptions, but did not afect citizens' willingness to reuse the system. Interestingly, citizens with higher AI-literacy expressed greater willingness to adopt the system, regardless of the explanation levels. Qualitative fndings revealed that greater human involvement and appeal mechanisms could positively infuence citizens' perceptions. Our findings highlight the importance of citizen-centered design of AI-based decision-making in public administration.

关键词： algorithmic decision-making Administrative discretion Informational fairness Distributive fairness Explainable AI

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Between technochauvinism and human-centrism: Can algorithms improve decision-making in democratic politics?

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EUROPEAN POLITICAL SCIENCE 2022年第1期21卷 132-149页

作者： Koenig, Pascal D. Wenzelburger, Georg TU Kaiserslautern Dept Social Sci Bldg 57POB 3049 D-67653 Kaiserslautern Germany

The promise of algorithmic decision-making (ADM) lies in its capacity to support or replace human decision-making based on a superior ability to solve specific cognitive tasks. Applications have found their way into various domains of decision-making-and even find appeal in the realm of politics. Against the backdrop of widespread dissatisfaction with politicians in established democracies, there are even calls for replacing politicians with machines. Our discipline has hitherto remained surprisingly silent on these issues. The present article argues that it is important to have a clear grasp of when and how ADM is compatible with political decision-making. While algorithms may help decision-makers in the evidence-based selection of policy instruments to achieve pre-defined goals, bringing ADM to the heart of politics, where the guiding goals are set, is dangerous. Democratic politics, we argue, involves a kind of learning that is incompatible with the learning and optimization performed by algorithmic systems.

关键词： algorithmic decision-making Artificial intelligence Democratic politics Evidence Legitimacy Policy making

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Public opinion and persuasion of algorithmic fairness: assessment of communication protocol performance for use in simulation-based reinforcement learning training

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International Journal of Information Technology (Singapore) 2024年第2期16卷 687-696页

作者： Ahmad, Muhamad Fazil Faculty of Applied Social Sciences Universiti Sultan Zainal Abidin Terengganu Kuala Nerus 21300 Malaysia

As the popularity of AI continues to grow, the techniques used to train AI systems have become increasingly intriguing. Reinforcement learning (RL) in simulation environments provides a safe and effective training approach for AI. However, the performance of the communication protocol between the reinforcement learning system and the simulation environment significantly impacts the overall system performance. This research aims to investigate the impact of communication protocols on algorithmic fairness by exploring public opinion and persuasion. The test software uses reinforcement learning data of floating-point numbers and images to assess communication methods, including Sockets, ***, gRPC, and ZeroMQ. Results indicate that Sockets and ZeroMQ exhibit similar performance in transferring floats, while ZeroMQ outperforms Sockets when it comes to transmitting images. Sockets are preferred when dealing with larger datasets. ZeroMQ stands out due to its speed and user-friendly simplicity, making it a suitable choice for reinforcement learning in simulation, employing Unreal Engine, AGX Dynamics, and Stable Baselines3. In the conducted experiments, reinforcement learning is found to be the most time-consuming component, followed by simulation, with communication constituting half of the total time. As the complexity of the system increases, the time spent on reinforcement learning and simulation is expected to grow faster than that of communication, emphasizing the need to optimize other components. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： AI ethics algorithmic decision-making Algorithms Artificial intelligence (AI) Fairness Machine learning (ML) Socio-technical design

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The importance of effectiveness versus transparency and stakeholder involvement in citizens' perception of public sector algorithms

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PUBLIC MANAGEMENT REVIEW 2024年第4期26卷 1061-1082页

作者： Koenig, Pascal D. Felfeli, Julia Achtziger, Anja Wenzelburger, Georg Harvard Univ Fac Arts & Social Sci Cambridge MA 02138 USA Saarland Univ Dept Polit & Social Sci Saarbrucken Germany TU Kaiserslautern Dept Social Sci Kaiserslautern Germany

This paper sheds light on how much citizens value different features of public sector algorithms, specifically whether they prioritize effectiveness over transparency and stakeholder involvement in algorithm design or instead see effectiveness as less important. It does so with choice-based conjoint designs that present variants of algorithms used in policing and health care to respondents from representative German samples. Two studies with overall more than 3000 participants show that people are ready to trade away transparency and stakeholder involvement for small effectiveness gains. Citizens thus seem unlikely to demand accountable algorithms even in sensitive areas like policing and health care.

关键词： algorithmic decision-making public services front-line decision-making experiment choice-based conjoint analysis

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algorithmic Fairness from a Non-ideal Perspective 20

Algorithmic Fairness from a Non-ideal Perspective

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3rd AAAI/ACM Conference on AI, Ethics, and Society (AIES)

作者： Fazelpour, Sina Lipton, Zachary C. Carnegie Mellon Univ Dept Philosophy Pittsburgh PA 15213 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450371100

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In the hopes of mitigating these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might hope to observe in a fair world, offering a variety of algorithms that attempt to satisfy subsets of these parities or to trade off the degree to which they are satisfied against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and ideal worlds. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of their actions, naive applications of ideal thinking can lead to misguided policies. In this paper, we demonstrate a connection between the recent literature on fair machine learning and the ideal approach in political philosophy, and show that some recently uncovered shortcomings in proposed algorithms reflect broader troubles faced by the ideal approach. We work this analysis through for different formulations of fairness and conclude with a critical discussion of real-world impacts and directions for new research.

关键词： algorithmic decision-making fairness in machine learning political philosophy justice

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Data-driven Humanitarian Mapping and Policymaking: Toward Planetary-Scale Resilience, Equity, and Sustainability 22

Data-driven Humanitarian Mapping and Policymaking: Toward Pl...

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Gaikwad, Snehalkumar 'Neil' Iyer, Shankar Lunga, Dalton Yabe, Takahiro Liang, Xiaofan Ananthabhotla, Bhavani Behari, Nikhil Guggilam, Sreelekha Chi, Guanghua MIT Cambridge MA 02139 USA Meta Res Menlo Pk CA USA Oak Ridge Natl Lab Oak Ridge TN USA Georgia Inst Technol Atlanta GA 30332 USA Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781450393850

Human civilization faces existential threats in the forms of climate change, food insecurity, pandemics, international conflicts, forced displacements, and environmental injustice. These overarching humanitarian challenges disproportionately impact historically marginalized communities worldwide. UN OCHA estimates that 274 million people will need humanitarian support in 2022.1 Despite growing perils to human and environmental well-being, there remains a paucity of publicly-engaged computing research to inform the design of interventions. Data science and machine learning efforts exist, but they remain isolated from socioeconomic, environmental, cultural, and policy contexts at local and international scales. Moreover, biases and privacy infringements in data-intensive methods amplify existing inequalities and harms. The result is that proclaimed benefits of data-driven innovations may remain inaccessible to policymakers, practitioners, and underserved communities whose lives they intend to transform. To address gaps in knowledge and improve the livelihood of marginalized populations, we have established the Data-driven Humanitarian Mapping and Policymaking, an interdisciplinary initiative [1-3].

关键词： data-intensive computing and society algorithmic decision-making AI ethics data science and public policy community-based design human-AI collaboration climate change computational sustainability public interest technology remote sensing data systems

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Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions 24

Automated Rationale Generation: A Technique for Explainable ...

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24th ACM International Conference on Intelligent User Interfaces (IUI)

作者： Ehsan, Upol Tambwekar, Pradyumna Chan, Larry Harrison, Brent Riedl, Mark O. Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA Cornell Univ Dept Informat Sci Ithaca NY 14850 USA Univ Kentucky Dept Comp Sci Lexington KY USA

ISBN: (纸本)9781450362726

Automated rationale generation is an approach for real-time explanation generation whereby a computational model learns to translate an autonomous agent's internal state and action data representations into natural language. Training on human explanation data can enable agents to learn to generate human-like explanations for their behavior. In this paper, using the context of an agent that plays Frogger, we describe (a) how to collect a corpus of explanations, (b) how to train a neural rationale generator to produce different styles of rationales, and (c) how people perceive these rationales. We conducted two user studies. The first study establishes the plausibility of each type of generated rationale and situates their user perceptions along the dimensions of confidence, humanlike-ness, adequate justification, and understandability. The second study further explores user preferences between the generated rationales with regard to confidence in the autonomous agent, communicating failure and unexpected behavior. Overall, we find alignment between the intended differences in features of the generated rationales and the perceived differences by users. Moreover, context permitting, participants preferred detailed rationales to form a stable mental model of the agent's behavior.

关键词： Explainable AI rationale generation user perception algorithmic explanation algorithmic decision-making interpretability transparency Artificial Intelligence Machine Learning

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