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It takes two to tango: a synergistic approach to human-machine decision making

Project description

Human-centric AI decision support systems

AI possesses the potential to enhance decision-making, mitigate bias and decrease cognitive overload. Nevertheless, instilling trust in the reliability of AI remains essential. The EU-funded TANGO project will forge hybrid decision support systems that harmonise human and machine values and objectives, fortifying Europe’s prominence in human-centric AI. It will formulate a cognitive theory of mutual comprehension and hybrid decision-making, create explainable AIs attuned to cognition, and facilitate a ‘human-in-the-loop’ co-evolution of human decision-making and machine learning models. TANGO is set to execute four impactful use cases: providing support to women during pregnancy and postpartum, aiding surgical teams, assisting loan officers and applicants in credit lending assessments, and guiding public policymakers in devising incentives and distributing funds.

Objective

Artificial Intelligence (AI) holds enormous potential for enhancing human decisions, improving cognitive overload and lowering bias in high-stakes scenarios. Adoption of AI-based support systems in such applications is however minimal, chiefly due to the difficulty of assessing their assumptions, limitations and intentions. In order to realise the promise of AI for individuals, society and economy, people should feel they can trust AIs in terms of reliability, capacity to understand the human’s needs, and guarantees that they are genuinely aiming at helping them. TANGO will develop the theoretical basis and computational framework for hybrid decision support systems (HDSS) in which humans and machines are aligned in terms of values and goals, know their respective strengths, and work together to reach an optimal decision. To this end, TANGO will develop: 1) A cognitive theory of mutual understanding and hybrid decision making, of intuitive vs deliberative approaches to decision making and of how they affect our trust in human and AI teammates. 2) Cognition-aware explainable AIs implementing synergistic human-machine interaction, enabling machines to determine what information a specific decision maker (e.g. layperson vs expert) needs, or does not need, to reach an informed decision. 3) A “Human-in-the-loop” co-evolution of human decision making and machine learning models building on bi-directional, explanation-augmented interlocution. The TANGO framework will be evaluated on four high impact use cases, namely supporting: i) women during pregnancy and postpartum, ii) surgical teams in intraoperative decision making, iii) loan officers and applicants in credit lending decision processes, and iv) public policy makers in designing incentives and allocating funds. Success in these case studies will establish TANGO as the framework of reference for developing a new generation of synergistic AI systems, and will strengthen the leadership of Europe in human-centric AI.

Fields of science (EuroSciVoc)

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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-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL4-2022-HUMAN-02

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Coordinator

UNIVERSITA DEGLI STUDI DI TRENTO
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.

€ 1 211 938,75
Address
VIA CALEPINA 14
38122 TRENTO
Italy

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Region
Nord-Est Provincia Autonoma di Trento Trento
Activity type
Higher or Secondary Education Establishments
Links
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.

€ 1 211 938,75

Participants (20)

Partners (3)

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