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Algorithmic Decision-Making and the Control Problem

John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan

Minds and Machines 29 (4):555-578 (2019)

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A metaphysical account of agency for technology governance.Sadjad Soltanzadeh - forthcoming - AI and Society:1-12.details The way in which agency is conceptualised has implications for understanding human–machine interactions and the governance of technology, especially artificial intelligence (AI) systems. Traditionally, agency is conceptualised as a capacity, defined by intrinsic properties, such as cognitive or volitional facilities. I argue that the capacity-based account of agency is inadequate to explain the dynamics of human–machine interactions and guide technology governance. Instead, I propose to conceptualise agency as impact. Agents as impactful entities can be identified at different levels: from the (...) Download Export citation Bookmark
Attention, moral skill, and algorithmic recommendation.Nick Schuster & Seth Lazar - forthcoming - Philosophical Studies:1-26.details Recommender systems are artificial intelligence technologies, deployed by online platforms, that model our individual preferences and direct our attention to content we’re likely to engage with. As the digital world has become increasingly saturated with information, we’ve become ever more reliant on these tools to efficiently allocate our attention. And our reliance on algorithmic recommendation may, in turn, reshape us as moral agents. While recommender systems could in principle enhance our moral agency by enabling us to cut through the information (...) Download Export citation Bookmark
Ethics of Artificial Intelligence.Stefan Buijsman, Michael Klenk & Jeroen van den Hoven - forthcoming - In Nathalie Smuha (ed.), Cambridge Handbook on the Law, Ethics and Policy of AI. Cambridge University Press.details Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due to the context (...) Download Export citation Bookmark
The decision-point-dilemma: Yet another problem of responsibility in human-AI interaction.Laura Crompton - 2021 - Journal of Responsible Technology 7:100013.details AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise (...) Download Export citation Bookmark 1 citation
Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care.Jaana Parviainen & Juho Rantala - 2022 - Medicine, Health Care and Philosophy 25 (1):61-71.details Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence (...) Download Export citation Bookmark 3 citations
Transparency and its roles in realizing greener AI.Omoregie Charles Osifo - 2023 - Journal of Information, Communication and Ethics in Society 21 (2):202-218.details Purpose The purpose of this paper is to identify the key roles of transparency in making artificial intelligence (AI) greener (i.e. causing lesser carbon dioxide emissions) during the design, development and manufacturing stages or processes of AI technologies (e.g. apps, systems, agents, tools, artifacts) and use the “explicability requirement” as an essential value within the framework of transparency in supporting arguments for realizing greener AI. Design/methodology/approach The approach of this paper is argumentative, which is supported by ideas from existing literature (...) Download Export citation Bookmark
Social impacts of algorithmic decision-making: A research agenda for the social sciences.Frauke Kreuter, Christoph Kern, Ruben L. Bach & Frederic Gerdon - 2022 - Big Data and Society 9 (1).details Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...) Download Export citation Bookmark 2 citations
“Computer says no”: Algorithmic decision support and organisational responsibility.Angelika Adensamer, Rita Gsenger & Lukas Daniel Klausner - 2021 - Journal of Responsible Technology 7-8 (C):100014.details Download Export citation Bookmark

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