Exploring the Cognitive Sense of Self in AI: Ethical Frameworks and Technological Advances for Enhanced Decision-Making

Exploring the Cognitive Sense of Self in AI: Ethical Frameworks and Technological Advances for Enhanced Decision-Making

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2024 by IJRES Journal
Volume-11 Issue-6
Year of Publication : 2024
Authors : Emily Barnes, James Hutson
DOI : 10.14445/23497157/IJRES-V11I6P119

How to Cite?

Emily Barnes, James Hutson, "Exploring the Cognitive Sense of Self in AI: Ethical Frameworks and Technological Advances for Enhanced Decision-Making," International Journal of Recent Engineering Science, vol. 11, no. 6, pp. 225-237, 2024. Crossref, https://doi.org/10.14445/23497157/IJRES-V11I6P119

Abstract
The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operational systems capable of ethical and adaptable behaviors. This study aims to synthesize existing knowledge, identify critical gaps in the literature, and highlight the implications of these findings for the future development of machine learning systems. Integrating insights from cognitive science, neuroscience, and ethical studies, this article seeks to provide a foundational framework for advancing emergent technologies that are both technologically robust and aligned with societal values. The significance of this research lies in its potential to guide the development of machine systems capable of complex decision-making and interactions, addressing both the moral and practical challenges of integrating such systems into daily human activities.

Keywords
Artificial Intelligence, Self-awareness, Cognitive science, Ethical frameworks, Decision-making.

Reference
[1] Moscviciov Andrei et al., “Financial Ratio Analysis Used in the It Enterprises,” Annals of Faculty of Economics, vol. 1, no. 2, pp. 600-603, 2010.
[Google Scholar] [Publisher Link]
[2] Larissa M. Batrancea et al., “Crunching Numbers in the Quest for Spotting Bribery Acts: A Cross-Cultural Rundown,” The Ethics of Bribery, pp. 329-343, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Izuchukwu Kizito Okoli, and Osita Gregory Nnajiofor, “The Nature of Consciousness in the Context of Artificial Intelligence: Redefining Human-Technology Relationships,” UJAH: Unizik Journal of Arts and Humanities, vol. 25, no. 1, pp. 1-30, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Smita Panda, and Prabir Chandra Padhy, “Bridging the Gap: Intersecting Perspectives on Digital and Human Consciousness,” Comparative Analysis of Digital Consciousness and Human Consciousness: Bridging the Divide in AI Discourse, IGI Global, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Rocco J. Gennaro, “Consciousness and Implicit Self-Awareness: Eastern and Western Perspectives,” Consciousness Studies in Sciences and Humanities: Eastern and Western Perspectives, pp. 43-54, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Roberto Legaspi, Zhengqi He, and Taro Toyoizumi, “Synthetic Agency: Sense of Agency in Artificial Intelligence,” Current Opinion in Behavioral Sciences, vol. 29, pp. 84-90, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Andrew Oberg, “Souls and Selves: Querying an AI Self with a View to Human Selves and Consciousness,” Religions, vol. 14, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Jun Tani, and Jeffrey White, “Cognitive Neurorobotics and Self in the Shared World, a Focused Review of Ongoing Research,” Adaptive Behavior, vol. 30, no. 1, pp. 81-100, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Antonio Parziale, and Angelo Marcelli, “Understanding Upper-Limb Movements via Neurocomputational Models of the Sensorimotor System and Neurorobotics: Where We Stand,” Artificial Intelligence Review, vol. 57, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Verena V. Hafner et al., “Prerequisites for an Artificial Self,” Frontiers in Neurorobotics, vol. 14, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Carlo S. Regazzoni et al., “Multisensorial Generative and Descriptive Self-Awareness Models for Autonomous Systems,” Proceedings of the IEEE, vol. 108, no. 7, pp. 987-1010, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Anne Lauscher, “Life 3.0: being Human in the Age of Artificial Intelligence,” Internet Histories, Digital Technology, Culture and Society, vol. 3, no. 1, pp. 101-103, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Michael Levin, “Life, Death, and Self: Fundamental Questions of Primitive Cognition Viewed through the Lens of Body Plasticity and Synthetic Organisms,” Biochemical and Biophysical Research Communications, vol. 564, pp. 114-133, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Gary Marcus, and Ernest Davis, “Insights for AI from the Human Mind,” Communications of the ACM, vol. 64, no. 1, pp. 38-41, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Bojie Feng, Nady Slam, and Yingjin Xu, “A Social Self-Awareness Agent with Embodied Reasoning,” Journal of Artificial Intelligence and Consciousness, vol. 11, no. 1, pp. 17-33, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Srinath Srinivasa, and Jayati Deshmukh, “AI and the Sense of Self,” arxiv, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Xinyue Hao, Emrah Demir, and Daniel Eyers, “Exploring Collaborative Decision-Making: A Quasi-Experimental Study of Human and Generative AI Interaction,” Technology in Society, vol. 78, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Andrea Toma et al., “AI-Based Abnormality Detection at the PHY-Layer of Cognitive Radio by Learning Generative Models,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 21-34.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Yixin Zhu et al., “Dark, Beyond Deep: Journal of Artificial Intelligence and Consciousness,” Engineering, vol. 6, no. 3, pp. 310-345, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xin Su et al., “Generative Memory for Lifelong Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 1884-1898, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Andrea Soltoggio et al., “A Collective AI via Lifelong Learning and Sharing at the Edge,” Nature Machine Intelligence, vol. 6, pp. 251-264, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Nigel Greenwood et al., “Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes,” 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), Washington, USA, pp. 147-153, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Vanshika Vats et al., “A Survey on Human-AI Teaming with Large Pre-Trained Models,” arXiv, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Shannon Vallor, Brian Green, and Irina Raicu, “Ethics in Technology Practice,” The Markkula Center for Applied Ethics at Santa Clara University, Markkula Center for Applied Ethics, 2022.
[Google Scholar] [Publisher Link]
[25] Eva Kassens-Noor et al., “Living with Autonomy: Public Perceptions of an AI-Mediated Future,” Journal of Planning Education and Research, vol. 44, no. 1, pp. 375-386, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Zhehat Rebar Abdulqader, “A Responsible AI Development for Sustainable Enterprises A Review of Integrating Ethical AI with IoT and Enterprise Systems,” Journal of Information Technology and Informatics, vol. 3, no. 2, pp. 129-156, 2024.
[Google Scholar]
[27] Roberto Legaspi et al., “The Sense of Agency in Human–AI Interactions,” Knowledge-Based Systems, vol. 286, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Robert Kwiatkowski, and Hod Lipson, “Task-Agnostic Self-Modeling Machines,” Science Robotics, vol. 4, no. 26, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Madhurima Das, “Learning Agility: The Journey from Self-Awareness to Self-Immersion,” AI, Consciousness and the New Humanism, pp. 175-195, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Xi-Hui Jia, and Jui-Che Tu, “Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness,” Systems, vol. 12, no. 3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Sebastian Kahl et al., “Towards Autonomous Artificial Agents with an Active Self: Modeling Sense of Control in Situated Action,” Cognitive Systems Research, vol. 72, pp. 50-62, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Benn R. Konsynski, Abhishek Kathuria, and Prasanna P. Karhade, “Cognitive Reapportionment and the Art of Letting Go: A Theoretical Framework for the Allocation of Decision Rights,” Journal of Management Information Systems, vol. 41, no. 2, pp. 328-340, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Anita Ho, “Live like Nobody is Watching: Relational Autonomy in the Age of Artificial Intelligence Health Monitoring,” Oxford University Press, 2023.
[Google Scholar]
[34] Mitsuo Kawato, and Aurelio Cortese, “From Internal Models toward Metacognitive AI,” Biological Cybernetics, vol. 115, pp. 415-430, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Shijie Zheng et al., “Memory Repository for AI NPC,” IEEE Access, vol. 12, pp. 62581-62596, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Amani Alabed, Ana Javornik, and Diana Gregory-Smith, “AI Anthropomorphism and its Effect on Users' Self-Congruence and Self–AI Integration: A Theoretical Framework and Research Agenda,” Technological Forecasting & Social Change, vol. 182, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Raja Chatila et al., “Toward Self-Aware Robots,” Frontiers in Robotics and AI, vol. 5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Yousef Alhwaiti et al., “A Computational Deep Learning Approach for Establishing Long-Term Declarative Episodic Memory through One-Shot Learning,” Computers in Human Behavior, vol. 156, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Pei Wang, Xiang Li, and Patrick Hammer, “Self in NARS, an AGI System,” Frontiers in Robotics and AI, vol. 5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Eva Selenko et al., “Artificial Intelligence and the Future of Work: A Functional-Identity Perspective,” Current Directions in Psychological Science, vol. 31, no. 1, pp. 272-279, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Eric Schwitzgebel, “AI Systems Must Not Confuse Users about their Sentience or Moral Status,” Patterns, vol. 4, no. 8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Brian Patrick Green, “Ethical Reflections on Artificial Intelligence,” Scientia et Fides, vol. 6, no. 2, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Jaana Leikas, Raija Koivisto, and Nadezhda Gotcheva, “Ethical Framework for Designing Autonomous Intelligent Systems,” Journal of Open Innovation: Technology, Market and Complexity, vol. 5, no. 1, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Anna Jobin, Marcello Ienca, and Effy Vayena, “The Global Landscape of AI Ethics Guidelines,” Nature Machine Intelligence, pp. 389-399, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Louise A. Dennis, and Michael Fisher, “Verifiable Self-Aware Agent-Based Autonomous Systems,” Proceedings of the IEEE, vol. 108, no. 7, pp. 1011-1026, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[46] David J. Chalmers, “David J. Chalmers,” Neuron. vol. 111, no. 21. pp. 3341-3343, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Jolien C. Francken, “An Academic Survey on Theoretical Foundations, Common Assumptions and the Current State of Consciousness Science,” Neuroscience of Consciousness, vol. 2022, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Joyjit Chatterjee, and Nina Dethlefs, “This New Conversational AI Model can be Your Friend, Philosopher, and Guide ... and Even Your Worst Enemy,” Patterns, vol. 4, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Euclides Lourenco Chuma, and Gabriel Gomes de Oliveira, “Generative AI for Business Decision-Making: A Case of ChatGPT,” Management Science and Business Decisions, vol. 3, no. 1, pp. 5-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]