Formal Assessment of the Moral Worth of Artificial Intelligence

PDF
Published: Apr 16, 2026
DOI: https://doi.org/10.12681/jpentai.43702
Keywords:
History of AI Moral Worth Responsible AI Virtue Ethics
Flavio Soares Correa da Silva
Institute of Advanced Studies, University of Sao Paulo – Cidade Universitaria ASO, Sao Paulo, Brazil.
Abstract

In recent years, the name Artificial Intelligence (AI) has admitted interpretations beyond the borders of Science and Technology, reaching out to the realms of commercial and social phenomena. Ethical issues have resulted from this polysemy, leading to the development of policies and methodologies to define, assess and safeguard ethical standards in the development and adoption of AI products. In the present article, we unfold the different interpretations of AI and introduce a formal framework based on which computational tools can be built to support the definition and utilisation of ethical standards. The framework is grounded on virtue ethics and the moral worth appraisal of AI tools, grounding the formulation of declarative logic programs which can be computed to indicate the moral worth of the development and adoption of AI products.

Article Details
  • Section
  • Research Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons 4.0 (CC-BY 4.0) license, that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

Downloads
Download data is not yet available.
References
Ahn, S. Y., & Park, D. J. (2018). Corporate social responsibility and corporate longevity: The mediating role of social capital and moral legitimacy in Korea. Journal of Business Ethics, 150(1), 117-134. https://doi.org/10.1007/s10551-016-3161-3
Apt, K. R. (1990). Logic Programming, Handbook of Theoretical Computer Science. Van Leeuween (Manag. Ed) Vol, 2, 493-574.
Apt, K. R., Blair, H. A., & Walker, A. (1988). Towards a theory of declarative knowledge. In Foundations of deductive databases and logic programming (pp. 89-148). Morgan Kaufmann.
Apt, K. R. (1997). From logic programming to Prolog (Vol. 362). London: Prentice Hall.
Augustine, M. T. (2024). A survey on universal approximation theorems. arXiv:2407.12895. https://doi.org/10.48550/arXiv.2407.12895
Barrow, N. (2024). Anthropomorphism and AI hype. AI and Ethics, 4(3), 707-711. https://doi.org/10.1007/s43681-024-00454-1
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).
Brennan, T. (2015). The stoic theory of virtue. In The Routledge companion to virtue ethics (pp. 31-50). Routledge.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Science and engineering ethics, 24(2), 505-528. https://doi.org/10.1007/s11948-017-9901-7
De La Vega, N., Razin, N., & Cohen, N. (2023). What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement. arXiv:2303.11249. https://doi.org/10.48550/arXiv.2303.11249
Eén, N., & Sörensson, N. (2003, May). An extensible SAT-solver. In International conference on theory and applications of satisfiability testing (pp. 502-518). Berlin, Heidelberg: Springer Berlin Heidelberg.
Eiben, A. E., & Smith, J. E. (2015). Introduction to evolutionary computing. springer.
Ekundayo, O. S., & Ezugwu, A. E. (2025). Deep learning: Historical overview from inception to actualization, models, applications and future trends. Applied Soft Computing, 181, 113378. https://doi.org/10.1016/j.asoc.2025.113378
Fagin, R., Halpern, J. Y., Moses, Y., & Vardi, M. (2004). Reasoning about knowledge. MIT press.
Fensel, D. (2001). Ontologies. In Ontologies: A silver bullet for knowledge management and electronic commerce (pp. 11-18). Berlin, Heidelberg: Springer Berlin Heidelberg.
Fitting, M., & Ben-Jacob, M. (1990). Stratified, weak stratified, and three-valued semantics. Fundamenta Informaticae, 13(1), 19-33. https://doi.org/10.3233/FI-1990-13104
Floridi, L. (2024). Why the AI hype is another tech bubble. Philosophy & Technology, 37(4), 128. https://doi.org/10.1007/s13347-024-00817-w
Gambelin, O. (2024). Responsible AI: Implement an Ethical Approach in Your Organization. Kogan Page Publishers.
Gavranović, B. (2024). Fundamental Components of Deep Learning: A category-theoretic approach. arXiv:2403.13001. https://doi.org/10.48550/arXiv.2403.13001
Gebru, T., & Torres, É. P. (2024). The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence. First Monday, 29(4). https://doi.org/10.5210/fm.v29i4.13636
Inie, N., Druga, S., Zukerman, P., & Bender, E. M. (2024, June). From" AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 2322-2347).
Jedan, C. (2009). Stoic Virtues: Chrysippus and the Religious Character of Stoic Ethics. A&C Black.
Jia, Y., Peng, G., Yang, Z., & Chen, T. (2024). Category-theoretical and topos-theoretical frameworks in machine learning: A survey. arXiv:2408.14014. https://doi.org/10.48550/arXiv.2408.14014
Johri, P., Khatri, S. K., Al-Taani, A. T., Sabharwal, M., Suvanov, S., & Kumar, A. (2021, March). Natural language processing: History, evolution, application, and future work. In Proceedings of 3rd International Conference on Computing Informatics and Networks: ICCIN 2020 (pp. 365-375). Singapore: Springer Singapore.
Kolaitis, P. G. (1991). The expressive power of stratified logic programs. Information and Computation, 90(1), 50-66. https://doi.org/10.1016/0890-5401(91)90059-B
Kopalidis, T., Solachidis, V., Vretos, N., & Daras, P. (2024). Advances in facial expression recognition: A survey of methods, benchmarks, models, and datasets. Information, 15(3), 135. https://doi.org/10.3390/info15030135
Kunen, K. (1989). Signed data dependencies in logic programs. The Journal of Logic Programming, 7(3), 231-245. https://doi.org/10.1016/0743-1066(89)90022-8
LaGrandeur, K. (2024). The consequences of AI hype. AI and Ethics, 4(3), 653-656. https://doi.org/10.1007/s43681-023-00352-y
Lemke, C., & Monett, D. (2024). AI-based service systems: digital-ethical issues and their impact on value co-creation. In Handbook of Services and Artificial Intelligence (pp. 228-249). Edward Elgar Publishing.
Levine, Y., Yakira, D., Cohen, N., & Shashua, A. (2018, February). Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design. In International Conference on Learning Representations.
Markelius, A., Wright, C., Kuiper, J., Delille, N., & Kuo, Y. T. (2024). The mechanisms of AI hype and its planetary and social costs. AI and Ethics, 4(3), 727-742. https://doi.org/10.1007/s43681-024-00461-2
Markovits, J. (2010). Acting for the right reasons. Philosophical Review, 119(2), 201-242. https://doi.org/10.1215/00318108-2009-037
Markovits, J. (2012). Saints, heroes, sages, and villains. Philosophical Studies, 158(2), 289-311. https://doi.org/10.1007/s11098-012-9883-x
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE.
McLarney, E., Gawdiak, Y., Oza, N., Mattmann, C., Garcia, M., Maskey, M., ... & Little, C. (2021). Nasa framework for the ethical use of Artificial Intelligence (AI). NTRS - NASA Technical Reports Server.
Mollo, D. C., & Millière, R. (2023). The vector grounding problem. arXiv:2304.01481. https://doi.org/10.48550/arXiv.2304.01481
Monett, D., & Grigorescu, B. (2024, October). Deconstructing the AI myth: Fallacies and harms of algorithmification. In European Conference on e-Learning (Vol. 23, pp. 242-248). Academic Conferences International Limited.
Moskewicz, M. W., Madigan, C. F., Zhao, Y., Zhang, L., & Malik, S. (2001, June). Chaff: Engineering an efficient SAT solver. In Proceedings of the 38th annual Design Automation Conference (pp. 530-535).
O'Regan, G. (2024). Ethical and Legal Aspects of Computing: A Professional Perspective from Software Engineering. Springer Nature.
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11). https://doi.org/10.1016/j.patter.2021.100336
Perconti, P., & Plebe, A. (2020). Deep learning and cognitive science. Cognition, 203, 104365. https://doi.org/10.1016/j.cognition.2020.104365
Placani, A. (2024). Anthropomorphism in AI: hype and fallacy. AI and Ethics, 4(3), 691-698. https://doi.org/10.1007/s43681-024-00419-4
Prata, M., Masi, G., Berti, L., Arrigoni, V., Coletta, A., Cannistraci, I., ... & Bartolini, N. (2024). LOB-based deep learning models for stock price trend prediction: a benchmark study. Artificial Intelligence Review, 57(5), 116. https://doi.org/10.1007/s10462-024-10715-4
Rubachev, I., Kartashev, N., Gorishniy, Y., & Babenko, A. (2024). Tabred: Analyzing pitfalls and filling the gaps in tabular deep learning benchmarks. arXiv:2406.19380. https://doi.org/10.48550/arXiv.2406.19380
Shahriari, K., & Shahriari, M. (2017, July). IEEE standard review - Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. In 2017 IEEE Canada international humanitarian technology conference (IHTC) (pp. 197-201). IEEE.
Sliwa, P. (2016). Moral worth and moral knowledge. Philosophy and Phenomenological Research, 93(2), 393-418. https://doi.org/10.1111/phpr.12195
Snow, N. E. (Ed.). (2017). The Oxford handbook of virtue. Oxford University Press.
Tàpies, J., & Fernández Moya, M. (2012). Values and longevity in family business: evidence from a cross‐cultural analysis. Journal of Family Business Management, 2(2), 130-146. https://doi.org/10.1108/20436231211261871
UNESCO. (2022). Recommendation on the ethics of artificial intelligence. United Nations Educational, Scientific and Cultural Organization.
Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods and applications. The knowledge engineering review, 11(2), 93-136. https://doi.org/10.1017/S0269888900007797
Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27(11), 1134-1142. https://doi.org/10.1145/1968.1972
Valiant, L. G. (1999, May). Robust logics. In Proceedings of the thirty-first annual ACM symposium on Theory of Computing (pp. 642-651).
Vallor, S. (2016). Technology and the virtues: A philosophical guide to a future worth wanting. Oxford University Press.
Vallor, S. (2024). The AI mirror: How to reclaim our humanity in an age of machine thinking. Oxford University Press.
Vallor, S., & Rewark, W. J. (2018). An introduction to data ethics (Course module). Santa Clara, CA: Markkula Center for Applied Ethics.
Van Harmelen, F., Lifschitz, V., & Porter, B. (Eds.). (2008). Handbook of knowledge representation (Vol. 1). Elsevier.
Wan, Y., Bi, Z., He, Y., Zhang, J., Zhang, H., Sui, Y., ... & Yu, P. (2024). Deep learning for code intelligence: Survey, benchmark and toolkit. ACM Computing Surveys, 56(12), 1-41. https://doi.org/10.1145/3664597
Widder, D. G., & Hicks, M. (2024). Watching the generative AI hype bubble deflate. arXiv:2408.08778. https://doi.org/10.48550/arXiv.2408.08778
Wielemaker, J., Schrijvers, T., Triska, M., & Lager, T. (2012). SWI-PROLOG. Theory and Practice of Logic Programming, 12(1-2), 67-96. https://doi.org/10.1017/S1471068411000494
Wilks, Y. (2005). The history of natural language processing and machine translation. Encyclopedia of language and linguistics.