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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/3/107 |
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| 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 |
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