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

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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 AIsimplementing synergistic human-machine interaction, enabling machinesto 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 teamsin intraoperative decision making, iii) loan officers and applicantsin 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