Project description
Understanding human decisions and more explainable AI solutions
In an era where Artificial Intelligence (AI) has achieved remarkable feats, a significant challenge persists: interpretability. While AI systems often outperform humans in various tasks, their decision-making processes remain opaque. In contrast, humans can articulate their decision-making strategies, albeit with varying accuracy, enabling knowledge sharing in society. With the support of the Marie Skłodowska-Curie Actions programme, the REPORT-IT project will explore how humans generate reportable knowledge during experiential learning. The project’s experiments model complex learning environments, tracking the development of metacognition, affect, and reportable knowledge over time. Specifically, the project will aim to discern whether these systems can replicate human-like patterns of metacognition and affect.
Objective
Current artificial intelligence (AI) surpasses human-level performance in a vast range of tasks. However, its decisional processes are opaque, referred to as the AI interpretability problem. Humans, on the other hand, can verbally describe their decisional processes and strategies. The accuracy of these reports varies, especially in complex environments. Yet, people often come up with reasonably accurate explanations for their decisions, thereby allowing knowledge transfer in society. However, the mechanisms of accurate verbal report generation remain unclear. Therefore, the main research objective of the REPORT-IT project is to study how humans generate adequate reportable knowledge during learning through experience. Inspired by the recent findings from research on metacognition (i.e. insight into one's own cognition) and cognition-emotion interaction, I will test the novel hypothesis that metacognition and learning-related affect support the emergence of reportable knowledge. In two experiments modeling complex learning environments (implicit category learning and probabilistic reward learning tasks), I will track the development of metacognition, affect, and reportable knowledge over time. This will allow me to evaluate the temporal relationships between these components and predict the emergence of reportable knowledge. In the final step of the project, I will study the behavior of deep neural networks (DNNs) in the exact same tasks and test whether DNNs can generate temporal patterns of metacognition and affect, as observed in humans. Thereby, the REPORT-IT project combines my expertise in implicit learning and affective science with expertise in neuroscience of consciousness and DNNs at the host institute (University of Amsterdam). This way, REPORT-IT will contribute to understanding how people generate reportable knowledge and, at the same time, provide new approaches for explainable AI.
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Keywords
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-PF-01
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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.
1012WX Amsterdam
Netherlands
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