Key points are not available for this paper at this time.
Explicable AI in education (XAIED) has been proposed as a way to improve trust and ethical practice in algorithmic education. Based on a critical review of the literature, this paper argues that XAI should be understood as part of a wider socio-technical turn in AI. The socio-technical perspective indicates that explicability is a relative term. Consequently, XAIED mediation strategies developed and implemented across education stakeholder communities using language that is not just ‘explicable’ from an expert or technical standpoint, but explainable and interpretable to a range of stakeholders including learners. The discussion considers the impact of XAIED on several educational stakeholder types in light of the transparency of algorithms and the approach taken to explaination. Problematising the propositions of XAIED shows that XAI is not a full solution to the issues raised by AI, but a beginning and necessary precondition for meaningful discourse about possible futures.
Robert Farrow (Fri,) studied this question.