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Authors: Sergei Nirenburg 1 ; Marjorie McShane 1 ; Kenneth Goodman 2 and Sanjay Oruganti 1

Affiliations: 1 Department of Cognitive Science, Rensselaer Polytechnic Institute, 110 8th St. Troy, NY 12180-3590, U.S.A. 2Institute for Bioethics and Health Policy, Miller School of Medicine, University of Miami, P.O. Box 016960 (M-825) Miami, FL 33101, U.S.A. ; 2 Institute for Bioethics and Health Policy, Miller School of Medicine, University of Miami, P.O. Box 016960 (M-825) Miami, FL 33101, U.S.A

Keyword(s): Explanation, Explainable AI, Cognitive Modeling.

Abstract: Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems – which account for almost all current AI – can’t explain because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining “explanation”. The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can’t fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood p anels of a demonstration system in which a team of simulated robots collaborates on search task assigned by a human. (More)

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Paper citation in several formats:
Nirenburg, S., McShane, M., Goodman, K. and Oruganti, S. (2024). Explaining Explaining. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 116-123. DOI: 10.5220/0013000600003886

@conference{explains24,
author={Sergei Nirenburg and Marjorie McShane and Kenneth Goodman and Sanjay Oruganti},
title={Explaining Explaining},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - EXPLAINS},
year={2024},
pages={116-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013000600003886},
isbn={978-989-758-720-7},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - EXPLAINS
TI - Explaining Explaining
SN - 978-989-758-720-7
AU - Nirenburg, S.
AU - McShane, M.
AU - Goodman, K.
AU - Oruganti, S.
PY - 2024
SP - 116
EP - 123
DO - 10.5220/0013000600003886
PB - SciTePress

- Science and Technology Publications, Lda.
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