Explaining Explaining

Sergei Nirenburg, Marjorie McShane, Kenneth Goodman, Sanjay Oruganti

2024

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 panels of a demonstration system in which a team of simulated robots collaborates on search task assigned by a human.

Paper Citation

in Harvard Style

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 - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 116-123. DOI: 10.5220/0013000600003886

in Bibtex Style

@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 - Volume 1: EXPLAINS},
year={2024},
pages={116-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013000600003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: 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