Harnessing Metacognition for Safe and Responsible AI

The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems....

Full description

Saved in:
Bibliographic Details
Main Authors: Peter B. Walker, Jonathan J. Haase, Melissa L. Mehalick, Christopher T. Steele, Dale W. Russell, Ian N. Davidson
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Technologies
Subjects:
Online Access:https://www.mdpi.com/2227-7080/13/3/107
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1850280415876612096
author Peter B. Walker
Jonathan J. Haase
Melissa L. Mehalick
Christopher T. Steele
Dale W. Russell
Ian N. Davidson
author_facet Peter B. Walker
Jonathan J. Haase
Melissa L. Mehalick
Christopher T. Steele
Dale W. Russell
Ian N. Davidson
author_sort Peter B. Walker
collection DOAJ
description The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems.
format Article
id doaj-art-4f1815afc8974d5a8bc3b8bef95c55bc
institution OA Journals
issn 2227-7080
language English
publishDate 2025-03-01
publisher MDPI AG
record_format Article
series Technologies
spelling doaj-art-4f1815afc8974d5a8bc3b8bef95c55bc2025-08-20T01:48:46ZengMDPI AGTechnologies2227-70802025-03-0113310710.3390/technologies13030107Harnessing Metacognition for Safe and Responsible AIPeter B. Walker0Jonathan J. Haase1Melissa L. Mehalick2Christopher T. Steele3Dale W. Russell4Ian N. Davidson5Defense Health Agency, 7700 Arlington Blvd., Suite 5101, Falls Church, VA 22042, USAEvergreen Knoll Court, Alexandria, VA 22303, USADefense Health Agency, 7700 Arlington Blvd., Suite 5101, Falls Church, VA 22042, USAAcrophase Consulting LLC, 12605, War Admiral Way, North Potomac, MD 20878, USADepartment of Psychiatry, Uniformed Services University, 4301, Jones Bridge Rd, Bethesda, MD 20814, USADepartment of Computer Science, University of California, Davis, CA 95616, USAThe rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems.https://www.mdpi.com/2227-7080/13/3/107metacognition in AIactive learningAI safetyethical AIAI transparencyAI responsibility
spellingShingle Peter B. Walker
Jonathan J. Haase
Melissa L. Mehalick
Christopher T. Steele
Dale W. Russell
Ian N. Davidson
Harnessing Metacognition for Safe and Responsible AI
Technologies
metacognition in AI
active learning
AI safety
ethical AI
AI transparency
AI responsibility
title Harnessing Metacognition for Safe and Responsible AI
title_full Harnessing Metacognition for Safe and Responsible AI
title_fullStr Harnessing Metacognition for Safe and Responsible AI
title_full_unstemmed Harnessing Metacognition for Safe and Responsible AI
title_short Harnessing Metacognition for Safe and Responsible AI
title_sort harnessing metacognition for safe and responsible ai
topic metacognition in AI
active learning
AI safety
ethical AI
AI transparency
AI responsibility
url https://www.mdpi.com/2227-7080/13/3/107
work_keys_str_mv AT peterbwalker harnessingmetacognitionforsafeandresponsibleai
AT jonathanjhaase harnessingmetacognitionforsafeandresponsibleai
AT melissalmehalick harnessingmetacognitionforsafeandresponsibleai
AT christophertsteele harnessingmetacognitionforsafeandresponsibleai
AT dalewrussell harnessingmetacognitionforsafeandresponsibleai
AT ianndavidson harnessingmetacognitionforsafeandresponsibleai