Abstract
Ethical dilemmas are complex scenarios involving a decision between conflicting choices related to ethical principles. While considering a case of an ethical dilemma in education presented in [17], it can be seen how, in these situations, it might be needed to take into consideration the student’s needs, preferences, and potentially conflicting goals, as well as their personal and social contexts. Due to this, planning and foreseeing ethically challenging situations in advance, which would be how ethical design is normally used in technological artifacts, is not enough. As AI systems become more autonomous, the amount of possible situations, choices and effects their actions can have grow exponentially. In this paper, we bring together the analysis of ethical dilemmas in education and the need to incorporate moral reasoning into the AI systems’ decision procedures. We argue how ethical design, although necessary, is not sufficient for that task and that artificial morality, or equivalent tools, are needed in order to integrate some sort of “ethical sensor” into autonomous systems taking a deeper role in an educational settings in order to enable them to, if not resolve, at least identify new ethically-relevant scenarios they are faced with.
This work has been supported by the project colMOOC “Integrating Conversational Agents and Learning Analytics in MOOCs”, co-funded by the European Commission (ref. 588438-EPP-1-2017-1-EL-EPPKA2-KA), and by a UOC postdoctoral stay.
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Notes
- 1.
Although the terms “ethics” and “morality” have slightly different definitions (one being a more reflective discipline, while the other one being more about prescription of behavior), we use them interchangeably in this work to refer to behaviors that are both in accordance to certain ethical principles, as well as considered to bear “good”, or “right” outcomes.
- 2.
In the story “Liar!” [5, ch. 6], precisely, a robot continuously lies to the characters in order to avoid hurting their feelings, which is an unintended understanding of the term “harm” that was not planned in the design of that robot.
- 3.
It is worth mentioning that these two approaches to ethical systems, ethical design and artificial morality, are not mutually exclusive. In fact, Moor points out in his work how the categories he defines in [19] are not exclusive either –an explicit ethical agent can easily be an ethical impact agent and an implicit ethical agent as well. Following this, furnishing an agent with some artificial morality mechanisms does not imply having to ditch ethical design approaches beforehand.
- 4.
This would then open up the Sorites question about “how low is low enough” for the system to make this decision, but this question falls outside the scope of this paper.
- 5.
It is worth recalling a recent case that occurred during 2020 in the UK in which, due to students being unable to attend an A-level exam due to the Covid-19 pandemic, an automated system was implemented in order to predict the student’s grades [16]. It turned out that the predictions made by the students’ teachers and the ones made by the automated system were quite different (being way lower in the automated prediction), which resulted in several protests that led to the UK government disregarding the automated predictions and following the human teachers’ predicted grades. This ties up directly with the fact that human teachers had access to this Personal layer of their students that the automated system, which was fed only on data of what we call the Educational layer, lacked.
- 6.
Learning analytics could help understand the student’s performance and dedication and provide some grounds for a more informed decision.
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Casas-Roma, J., Conesa, J., Caballé, S. (2021). Education, Ethical Dilemmas and AI: From Ethical Design to Artificial Morality. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Design and Evaluation. HCII 2021. Lecture Notes in Computer Science(), vol 12792. Springer, Cham. https://doi.org/10.1007/978-3-030-77857-6_11
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