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Human collaboration with AI agents in national health governance: organizational circumstances under which data analysts and medical experts follow or deviate from AI.

Project description

For fair algorithms in national healthcare

While the potential benefits of the use of big data, algorithms and artificial intelligence (AI) in national health governance may be significant, concerns about ethics have been raised. Existing research has typically focused on legal or theoretical perspectives rather than the socio-cultural contexts in which these systems are developed and used. Funded by the European Research Council, the Health-AI project will examine the decision-making processes of those who work with AI, in six different national settings, to gain a better understanding of when and why humans intervene in or resist AI. By using innovative anthropological methods, the project aims to develop a theory on the factors that determine the ethical use of AI in healthcare. The research will also connect anthropological insights with the expertise of AI developers, health decision-makers, and policy institutions to further the development of fair AI.

Objective

This project will study a multi-sited ethnography of a currently evolving revolution in global health systems: big data/AI-informed national health governance. With health data being considered countries future oil, public and scholarly concerns about algorithmic ethics rise. Research has long shown that datasets in AI (re)produce social biases, discriminate and limit personal autonomy. This literature, however, has merely focused on AI design and institutional frameworks, examining the subject through legal, technocratic and philosophical perspectives, whilst overlooking the socio-cultural context in which big data and AI systems are embedded, most particularly organizations in which human agents collaborate with AI. This is problematic, as frameworks for ethical AI currently consider human oversight crucial, assuming that humans will correct or resist AI when needed; while empirical evidence for this assumption is extremely thin. Very little is known about when and why people intervene or resist AI. Research done consists of single, mostly Western studies, making it impossible to generalize findings. The innovative force of our research is fourfold: 1) To empirically analyze decisive moments in which data-analysts follow or deviate AI: moments deeply impacting national health policies and individual human lives. 2) To do research in six national settings with various governmental frameworks and in different organizational contexts, enabling us to contrast findings, eventually leading to a theory on the contextual, organizational factors underlying ethical AI. 3) To use innovative anthropological methods of future-scenarioing, which will enrich the anthropological discipline by developing and finetuning future-focused research. 4) The research connects anthropological insights with the expertise of AI-developers, and partners with relevant health decisionmakers and policy-institutions, allowing to both analyze and contribute to fair AI.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2022-STG

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Host institution

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 499 961,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 499 961,00

Beneficiaries (1)

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