Situated Interpretation and Data: Explainability to Convey Machine Misalignment

Abstract

Explainable AI must simultaneously help people understand the world, the AI, and when the AI is misaligned to the world. We propose situated interpretation and data (SID) as a design technique to satisfy these requirements. We trained two machine learning algorithms, one transparent and one opaque, to predict future patient events that would require an emergency response team (ERT) mobilization. An SID display combined the outputs of the two algorithms with patient data and custom annotations to implicitly convey the alignment of the transparent algorithm to the underlying data. SID displays were shown to 30 nurses with 10 actual patient cases. Nurses reported their concern level (1–10) and intended response (1–4) for each patient. For all cases where the algorithms predicted no ERT (correctly or incorrectly), nurses correctly differentiated ERT from non-ERT in both concern and response. For all cases where the algorithms predicted an ERT, nurses differentiated ERT from non-ERT in response, but not concern. Results also suggest that nurses’ reported urgency was unduly influenced by misleading algorithm guidance in cases where the algorithm overpredicted and underpredicted the future ERT. However, nurses reported concern that was as or more appropriate than the predictions in 8 of 10 cases and differentiated ERT from non-ERT cases better than both algorithms, even the more accurate opaque algorithm, when the two predictions conflicted. Therefore, SID appears a promising design technique to reduce, but not eliminate, the negative impacts of misleading opaque and transparent algorithms.