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Transparency in algorithmic and Human decision-making: Is There a Double Standard?

Philosophy and Technology 2019年第4期32卷 661-683页

作者： Zerilli, John Knott, Alistair Maclaurin, James Gavaghan, Colin Department of Philosophy University of Otago Dunedin New Zealand Department of Computer Science University of Otago Dunedin New Zealand Faculty of Law University of Otago Dunedin New Zealand

We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool. © 2018, Springer Nature B.V.

关键词： algorithmic decision-making Explainable AI Intentional stance Transparency

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Graph-based algorithmic design and decision-making framework for district heating and cooling plant positioning and network planning

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ADVANCED ENGINEERING INFORMATICS 2021年 50卷

作者： Ho, Chi-On Nie, Ting Su, Lingqi Yang, Zheng Schwegler, Ben Calvez, Philippe CSAI Lab ENGIE Unit 801A Mansion291 Fumin Rd Shanghai Peoples R China Stanford Univ 473 Via Ortega Stanford CA 94305 USA CSAI Lab ENGIE 4 Rue Josephine Baker Stains France

Although buildings are the single largest users of energy in cities, many individual building HVAC (heating, ventilation, and air conditioning) systems are energy inefficient. Sustainable district heating and cooling systems have been developed to address this inefficiency, however, the implementation of district heating and cooling systems is a complex, capital intensive multivariate problem. One critical engineering problem, the location of energy production plant and pipe network topology, has a major influence on the system performance as well as the life cycle cost, and therefore should be comprehensively studied and chosen. This paper proposes a novel algorithmic design and decision-making framework that uses multiple simultaneous criteria to generate and evaluate design alternatives of site selection and pipe network layout. The framework consists of three components: site location and topology generation, network sizing and evaluation, and multi-objective decisionmaking assistance. The proposed framework has been validated in a real-world district cooling greenfield project in China's metropolitan area. Results were compared against the engineers' best practices. It has shown that the proposed framework could find equally good or better designs in engineering and financial performance with CAPEX reduction of up to 15%.

关键词： District heating and cooling Network optimization Topology algorithmic decision-making GIS Graph

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A Minimalist Account of the Right To Explanation

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Philosophy and Technology 2025年第2期38卷 1-22页

作者： Grote, Thomas Paulo, Norbert Cluster of Excellence: “Machine Learning – New Perspectives for Science” University of Tuebingen Tuebingen Germany Institute of Philosophy Department of Social Sciences and Public Affairs Ludwig-Maximilians-Universität Munich University of the Bundeswehr Munich Munich Germany

Critiques of opaque machine learning models, used to guide consequential decisions, are gaining traction in moral philosophy. They are said to undermine informed self-advocacy, violate duties of consideration to decision-subjects, or reinforce existing power structures, among other things. Even though motivated by different normative frameworks, the received view is that the appropriate amelioration strategy is to make opaque machine learning models explainable. We challenge the received view. Focusing on Vredenburgh’s account of the ‘right to explanation’, we argue that the goals of informed self-advocacy cannot plausibly be satisfied by a combination of causal and rule-based normative explanations. Here we make three arguments in particular: (i) we highlight problems concerning the feasibility of causal explanations of the machine learning output for the purpose of social prediction;(ii) the relevant explanations are likely to be intellectually over-demanding since their correct interpretation will require a combination of domain-knowledge and statistical proficiency that cannot be presumed for laypersons;and (iii) shifting the burden to detect inadequacies to laypeople makes scrutinizing explanations very costly for them. Drawing on the empirical and philosophical literature on informed consent, we argue that it is more appropriate to revise the goal of the right to explanation from an interest in informed self-advocacy to enabling public trust: The public should be justified in believing that it can count on an institution using machine learning models competently, and that the goals for which they are used are morally legitimate. © The Author(s) 2025.

关键词： algorithmic decision-making Explanations Informed consent Machine learning Public trust

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Is artificial intelligence for medical professionals serving the patients?

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SYSTEMATIC REVIEWS 2024年第1期13卷 228页

作者： Wilhelm, Christoph Steckelberg, Anke Rebitschek, Felix G. Martin Luther Univ Halle Wittenberg Inst Hlth & Nursing Sci Magdeburger Str 8 D-06112 Halle Germany Univ Potsdam Fac Hlth Sci Brandenburg Harding Ctr Risk Literacy Virchowstr 2 D-14482 Potsdam Germany Max Planck Inst Human Dev Lentzeallee 94 D-14195 Berlin Germany

Backgroundalgorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)-regardless of the clinical *** the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by *** is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a signi

关键词： algorithmic decision-making ADM Artificial intelligence Patient relevant Healthcare professionals decision support

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The Datafication of Care in Public Homelessness Services 25

The Datafication of Care in Public Homelessness Services

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Erina Seh-Young Moon Devansh Saxena Dipto Das Shion Guha University of Toronto Toronto Ontario Canada University of Wisconsin-Madison Madison Wisconsin USA

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Exploring folk theories of algorithmic news curation for explainable design

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BEHAVIOUR & INFORMATION TECHNOLOGY 2022年第15期41卷 3346-3359页

作者： Ngo, Thao Kraemer, Nicole Univ Duisburg Essen Res Training Grp User Centred Social Media Dept Comp Sci & Appl Cognit Sci Forsthausweg 2 Duisburg Germany Univ Duisburg Essen Dept Comp Sci & Appl Cognit Sci Social Psychol Me Duisburg Germany

algorithmic news curation determines users' news exposure in the online environment. Despite its usefulness, it also comes along with the problem of algorithmic opacity. To combat this, explainable algorithmic news curation systems are necessary. One user-centered solution to design these systems can be achieved through the systematic exploration of user folk theories. For this, we conducted twelve in-depth semi-structured interviews to explore (1) the user preferences for explainable system design, and (2) folk theories of algorithmic news curation. By applying qualitative content analysis, we found a psychological trade-off between the desire for transparency and feelings of creepiness, thus a preference for explanations to be hidden. Furthermore, we identified eight assumptions of folk theories. The results are compared to previous folk theories and discussed in terms of the 'sweet spot' of system transparency. We conclude that exploring folk theories is a key requirement for designing explainable algorithmic news curation systems.

关键词： folk theory algorithmic decision-making explanation user interface qualitative research news curation

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Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期318卷 927-953页

作者： Abada, Ibrahim Lambin, Xavier Tchakarov, Nikolay Grenoble Ecole Management F-38000 Grenoble France ESSEC Business Sch 3 Av Bernard HirschCS 50105 F-95021 Cergy France THEMA 3 Av Bernard HirschBP 50105 F-95021 Cergy France

A burgeoning literature shows that self-learning algorithms may, under some conditions, reach seeminglycollusive outcomes: after repeated interaction, competing algorithms earn supra-competitive profits, at the expense of efficiency and consumer welfare. This paper offers evidence that such behavior can stem from insufficient exploration during the learning process and that algorithmic sophistication might increase competition. In particular, we show that allowing for more thorough exploration does lead otherwise seemingly-collusive Q-learning algorithms to play more competitively. We first provide a theoretical illustration of this phenomenon by analyzing the competition between two stylized Q-learning algorithms in a Prisoner's Dilemma framework. Second, via simulations, we show that some more sophisticated algorithms exploit the seemingly-collusive ones. Following these results, we argue that the advancement of algorithms in sophistication and computational capabilities may, in some situations, provide a solution to the challenge of algorithmic seeming collusion, rather than exacerbate it.

关键词： algorithmic decision-making Delegated decisions Machine learning Multi-agent reinforcement learning Tacit collusion

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When something goes wrong: Who is responsible for errors in ML decision-making?

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AI & SOCIETY 2024年第4期39卷 1891-1903页

作者： Berber, Andrea Sreckovic, Sanja Univ Belgrade Fac Philosophy Belgrade Serbia

Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It is important to have a principled way of assigning responsibility for such errors. The integral characteristics of ML, however, pose serious difficulties in defining responsibility and regulating ML decision-making. First, we elaborate on these characteristics and their epistemic and ethical implications. We then analyze possible general strategies for resolving the assignment of moral responsibility and show that, due to the specific way in which ML functions, each potential solution is problematic, whether we assign responsibility to humans, machines, or using hybrid models. Then, we shift focus on an alternative approach that bypasses moral responsibility and attempts to define legal liability independently through solutions such as informed consent and the no-fault compensation system. Both of these solutions prove unsatisfactory because they leave too much room for potential abuses of ML decision-making. We conclude that both ethical and legal solutions are fraught with serious difficulties. These difficulties prompt us to re-weigh the costs and benefits of using ML for high-stake decisions.

关键词： Machine learning algorithmic decision-making Opacity Responsibility Liability Hybrid responsibility Machine responsibility

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Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms

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TECHNOLOGY KNOWLEDGE AND LEARNING 2024年第2期29卷 1079-1107页

作者： Luenich, Marco Keller, Birte Marcinkowski, Frank Heinrich Heine Univ Dept Social Sci Dusseldorf Germany

Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students' fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality, equity, and need. Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 x 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against

关键词： algorithmic decision-making Higher education algorithmic fairness Distributive fairness Student support measures

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Clarity, surprises, and further questions in the Article 29 Working Party draft guidance on automated decision-making and profiling

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COMPUTER LAW & SECURITY REVIEW 2018年第2期34卷 398-404页

作者： Veale, Michael Edwards, Lilian UCL Dept Sci Technol Engn & Publ Policy STEaPP Boston House36-38 Fitzroy Sq London W1T 6EY England Univ Strathclyde Strathclyde Law Sch Glasgow Lanark Scotland

The Article 29 Data Protection Working Party's recent draft guidance on automated decision making and profiling seeks to clarify European data protection (DP) law's little-used right to prevent automated decision-making, as well as the provisions around profiling more broadly, in the run-up to the General Data Protection Regulation. In this paper, we analyse these new guidelines in the context of recent scholarly debates and technological concerns. They foray into the less-trodden areas of bias and non-discrimination, the significance of advertising, the nature of "solely" automated decisions, impacts upon groups and the inference of special categories of data at times, appearing more to be making or extending rules than to be interpreting them. At the same time, they provide only partial clarity - and perhaps even some extra confusion - around both the much discussed "right to an explanation" and the apparent prohibition on significant automated decisions concerning children. The Working Party appears to feel less mandated to adjudicate in these conflicts between the recitals and the enacting articles than to explore altogether new avenues. Nevertheless, the directions they choose to explore are particularly important ones for the future governance of machine learning and artificial intelligence in Europe and beyond. (C) 2017 Michael Veale & Lilian Edwards. Published by Elsevier Ltd.

关键词： Automated decision-making algorithmic decision-making Right to an explanation Right of access General Data Protection Regulation

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