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Designing Ethical Agency for Adaptive Instructional Systems: The FATE of Learning and Assessment

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Adaptive Instructional Systems. Design and Evaluation (HCII 2021)

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Abstract

Adaptive Instructional Systems (AIS) have the potential to provide students with a flexible, dynamic learning environment in a manner that might not be possible with the limited resources of human instructors. In addition to technical knowledge learning engineering also requires considering the values and ethics associated with the creation, development, and implementation of instruction and assessment techniques such as fairness, accountability, transparency, and ethics (FATE). Following a review of the ethical dimensions of psychometrics, I will consider specific ethical dimensions associated with AIS (e.g., cybersecurity and privacy issues, invidious selection processes) and techniques that can be adopted to address these concerns (e.g., differential item function, l-diversity). By selectively introducing quantitative methods that align with principles of ethical design, I argue that AIS can be afforded a minimal ethical agency.

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Notes

  1. 1.

    To avoid terminological issues and overlap between the acronym for adaptive instructional systems and autonomous/intelligence systems, I will simply refer to the latter as autonomous systems in that all of these systems have some level of autonomy, i.e., a subset of operations are carried on without user control or supervision.

  2. 2.

    For the purpose of brevity, I will refer to both of these systems as adaptive instruction systems as the technical distinction is not relevant to the present discussion.

  3. 3.

    Namely, his approach to welfare economics assumes that the goal should be to maximize expected utility.

  4. 4.

    Current work in autonomous systems that demonstrate ‘empathy’ are promising. However, at present these systems are generally focused on cognitive empathy rather than affective empathy. It remains an open question though whether affect cannot simply be modelled by differentially weighting variables or outcomes.

  5. 5.

    Messick’s use of ideologies might reflect a comparable concept to Foucault’s episteme.

  6. 6.

    Due to the potential for attacks resulting from homogeneity of a dataset or associations between multiple variables for k-anonymization, l-diversity promises to be a more effective means to preserve anonymity for low-diversity datasets wherein there is little diversity.

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Authors and Affiliations

  1. Department of Psychology, Institute for Data Science, Carleton University, Ottawa, Canada

    Jordan Richard Schoenherr

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Correspondence to Jordan Richard Schoenherr .

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  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Schoenherr, J.R. (2021). Designing Ethical Agency for Adaptive Instructional Systems: The FATE of Learning and Assessment. 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_18

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