Instructional Systems Design to Reflect ETHICS in AI’s Rules of Engagement Learning for Future Warfare

By. Hyunsoo Kim Views. 1895


Purpose: Rules of Engagement(ROE) refer to rules or directives that define the circumstances, conditions, extent, manner, etc. of the application of force or action that can be considered provocative by the armed forces. ROE do not explain how results are achieved, but rather indicate what judgments are unacceptable. Focusing this, the purpose of this study is to propose an Instructional Systems Design(ISD) configured to reflect ethics in AI’s ROE learning for future warfare.

Method: This study uses Development Research Method for develop and propose an ISD. ISD refers to the creation of guidelines into smaller units of teaching or learning. If some guidelines are created for such ISD, it would set the composition and application of ROE, and AI will learn that guidelines through deep learning. And the AI makes a decision with this in the hypothetical dilemma situation where the application of the ROE is requested. Finally, human experts review and supplement the learning results of these neural networks. The sophistication of the AI’s learning and applying ROE would be achieved by feeding back this result to the ISD.

Results: This study understands that ROE would also be essential for AI or AI-equipped military robot systems. In this process, AI performs the task of making judgments related to applying ROE, which is the principle of action in specific situations. To do this, Ai’s deep learning first collects necessary information and makes decisions based on it. Next, the results of this learning are applied in a new hypothetical dilemma situation. Finally, human experts' evaluation and feedback on the results are continuously made. This series of processes can be presented as a model of ISD oriented towards the moral development of AI.

Conclusion: AI’s ROE learning converges to the learning of moral values. It focuses on the cognitive aspect of morality. Therefore, it would be possible to refine the cognitive moral judgment of deep learning by applying the learning hierarchy of taxonomy of educational objects and the logical test of validity of moral judgment oriented toward social justice. And the moral development of the neural network can be performed by modifying and complementing the results of human experts and feeding them back.

[Keywords] Artificial Intelligence, Military, Rules of Engagement, Ethics, Instructional Systems Design

[1] Gonzalo G & Park S & Cho H. Prospects for New Wars in the 21st Century. International Journal of Military Affairs, 6(3), 43-53 (2021). [Article]
[2] Choi J. Current and Future Considerations for the Use of Artificial Intelligence by the United States’ Department of Defense. Robotics & AI Ethics, 6(1), 1-6 (2021). [Article]
[3] Moon H. Study on the Improvement of Resource Management for the Establishment of a Smart Operation System for the Future War. International Journal of Military Affairs, 4(1), 26-32 (2019). [Article]
[4] Jeong I. A Study on How to Expand the Role of Military Mobilization Forces in the Event of a National Disaster -Focusing on the Local Government and Regional Reserve Forces-. International Journal of Military Affairs, 6(2), 39-48 (2021). [Article]
[5] Kim S & Cheung C. A Study on the Operation Key of Field Action Manual(FAM) That Works in Disaster Sites. International Journal of Military Affairs, 5(2), 34-42 (2020). [Article]
[6] Park G & Seo E & Shin H. A New Approach to Moral Injury of Soldiers during War. International Journal of Military Affairs, 4(1), 20-25 (2019). [Article]
[7] Rasch R & Kott A & Forbus K. Incorporating AI into Military Decision Making: An Experiment. Institute of Electrical and Electronics Engineers Intelligent Systems, 18(4), 18-26 (2003).
[8] Lee S. Authority for Issuing the Rules of Engagement of the Republic of Korea Armed Forces: Focused on the Scope of the Rules of Engagement Issuing Authority of the United Nations Command(UNC) and the Republic of Korea -United States Combined Forces Command(CFC)-. The Quarterly Journal of Defense Policy Studies, 33(4), 73-104 (2017).
[9] Frost-nielsen P. Bringing Military Conduct out of the Shadow of Law: Towards a Holistic Understanding of Rules of Engagement. Journal of Military Ethics, 17(1), 21-35. (2018).
[10] Kim J. Future Warfare and Artificial Intelligence Applications in the Republic of Korea Military: Problems and Alternatives. Robotics & AI Ethics, 6(2), 11-20 (2021). [Article]
[11] Hersh M. Professional Ethics and Social Responsibility: Military Work and Peacebuilding. International Federation of Automatic Control-papers On Line, 50(1), 10592-10602 (2017).
[12] Mittelstadt B. Principles Alone cannot Guarantee Ethical AI. Nature Machine Intelligence, 1(11), 501-507 (2019).
[13] Kim H. Approaches to Forming Ethical AI as an Artificial Moral Agent: Suggesting Virtue Education Method Through Comparison of Top-down and Bottom-up Approaches. Robotics & AI Ethics, 6(2), 44-51 (2021). [Article]
[14] Kim J & Kim D & Kim J & Ryu K. Design and Implementation of Real-time Parallel Engine for Discrete Event Wargame Simulation. The Korea Information Processing Society Transactions: Part A, 10(2), 111-122 (2003).
[15] Veziridis S & Karampelas P & Lekea I. Learn by Playing: A Serious War Game Simulation for Teaching Military Ethics. The Institute of Electrical and Electronics Engineer Global Engineering Education Conference, 1(1), 920-925 (2017).
[16] Walker L & de Vries B & Trevethan S. Moral Stages and Moral Orientations in Real-life and Hypothetical Dilemmas. Child Development, 58(3), 842-858 (1987).
[17] Bostyn D & Sevenhant S & Roets A. Of Mice, Men, and Trolleys: Hypothetical Judgment Versus Real-life Behavior in Trolley-style Moral Dilemmas. Psychological Science, 29(7), 1084-1093 (2018).
[18] Lind G. The Importance of Role-taking Opportunities for Self-sustaining Moral Development. Journal of Research in Education, 10(1), 9-15 (2000).
[19] Cummings R & Maddux C & Richmond A & Cladianos A. Moral Reasoning of Education Students: The Effects of Direct Instruction in Moral Development Theory and Participation in Moral Dilemma Discussion. Teachers College Record, 112(3), 621-644 (2010).
[20] Seddon G. The Properties of Bloom’s Taxonomy of Educational Objectives for the Cognitive Domain. Review of Educational Research, 48(2), 303-323 (1978).
[21] Cooper W. Book Reviews: Harrow, Anita J. A Taxonomy of the Psychomotor Domain: A Guide for Developing Behavioral Objectives. American Educational Research Journal, 10(4), 325-327 (1973).
[22] Stanny C. Reevaluating Bloom’s Taxonomy: What Measurable Verbs Can and Cannot Say about Student Learning. Education Sciences, 6(4), 37-47 (2016).
[23] Harshman R. School Bussing: A Moral Development Viewpoint. Educational Leadership, 34(4), 293-297 (1977).
[24] Smith B & Meux M & Coombs J & Nuthall G & Precians R. Abstracted from a Study of the Strategies of Teaching. Classroom Interaction Newsletter, 3(2), 1-9 (1968).
[25] Ahmeti K & Ramadani N. Determination of Kohlberg’s Moral Development Stages and Chronological Age. International Journal of Social and Human Sciences, 8(15-16), 37-48 (2021).
[26] Owe A & Baum D. Moral Consideration of Nonhumans in the Ethics of Artificial Intelligence. AI and Ethics, 1(1), 517-528 (2021).
[27] de Oliveira Fornasier M. The Regulation of the Use of Artificial Intelligence in Warfare: Between International Humanitarian Law(IHL) and Meaningful Human Control. Revista Jurídica da Presidência, 23(129), 67-93 (2021).
[28] Lee A. Analyzing the Effects of AI Education Program based on AI Tools. Robotics & AI Ethics, 6(2), 21-29 (2021). [Article]
[29] Allen C & Varner G & Zinser J. Prolegomena to Any Future Artificial Moral Agent. Journal of Experimental & Theoretical Artificial Intelligence, 12(3), 251-261 (2000).
[30] Kim H. Suggestion of Building the AI Code of Ethics through Deep Learning and Big Data Based AI. Robotics & AI Ethics, 6(1), 29-34 (2021). [Article]