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Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions

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Abstract

This comprehensive analysis dives deep into the intricate interplay between artificial intelligence (AI) and human agency, examining the remarkable capabilities and inherent limitations of large language models (LLMs) such as GPT-3 and ChatGPT. The paper traces the complex trajectory of AI's evolution, highlighting its operation based on statistical pattern recognition, devoid of self-consciousness or innate comprehension. As AI permeates multiple spheres of human life, it raises substantial ethical, legal, and societal concerns that demand immediate attention and deliberation. The metaphorical illustration of humans as “shepherds” of AI, coupled with a thought-provoking examination of “agency without intelligence,” engenders deep reflections on the future of human-AI interplay and the associated philosophical, ethical, and practical ramifications. The paper further elucidates critical challenges associated with AI, such as ethical dilemmas, “agency without intelligence,” the role of human oversight, and the environmental impact of AI technologies. In the pursuit of solutions, the paper maps prospective directions, emphasizing the need for robust AI policies, improved AI literacy, sustainable AI practices, and a continued dialogue on the multifaceted issues surrounding AI. Through a holistic exploration of AI, this paper underlines the necessity for a multidisciplinary, collaborative approach to chart an ethical, beneficial, and sustainable AI future, enabling society to harness the potential of AI responsibly while mitigating the associated risks.

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The authors used ChatGPT for assessing the article.

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PPR devised the idea and wrote the article. PKD reviewed and gave expert comments.

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Correspondence to Partha Pratim Ray.

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Ray, P.P., Das, P.K. Charting the Terrain of Artificial Intelligence: a Multidimensional Exploration of Ethics, Agency, and Future Directions. Philos. Technol. 36, 40 (2023). https://doi.org/10.1007/s13347-023-00643-6

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