Trust Junk and Evil Knobs: Calibrating Trust in AI Visualization

Emily Wall, Laura E. Matzen, Mennatallah El-Assady, Peta Masters, Helia Hosseinpour, Alex Endert, Rita Borgo, Polo Chau, Adam Perer, Harald T. Schupp, Hendrik Strobelt, Lace M. K. Padilla

Abstract: Many papers make claims about specific visualization techniques that are said to enhance or calibrate trust in AI systems. But a design choice that enhances trust in some cases appears to damage it in others. In this paper, we explore this inherent duality through an analogy with "knobs". Turning a knob too far in one direction may result in under-trust, too far in the other, over-trust or, turned up further still, in a confusing distortion. While the designs or so-called "knobs" are not inherently evil, they can be misused or used in an adversarial context and thereby manipulated to mislead users or promote unwarranted levels of trust in AI systems. When a visualization that has no meaningful connection with the underlying model or data is employed to enhance trust, we refer to the result as "trust junk." From a review of 65 papers, we identify nine commonly made claims about trust calibration. We synthesize them into a framework of knobs that can be used for good or "evil," and distill our findings into observed pitfalls for the responsible design of human-AI systems.

External IDs:dblp:conf/pacificvis/WallMEMHEBCPSSP24