This is the fifth blog in a series about a recent workshop organised by the iToBoS project on explainable artificial intelligence (xAI) and social and ethical issues related to AI.
The event was hosted by project partners from Trilateral Research as part of a broader effort to engage with stakeholders ahead of a report on privacy, data protection, social and ethical issues, and xAI. The event attracted 19 participants, including patient advocates, clinicians, and experts in IT, law and ethics.
The event included a breakout room focused exclusively on xAI, which refers to methods to explain AI decision-making. Why is this so important? The iToBoS project is developing an intelligent full body scanner and diagnostics tool to deliver faster, personalized melanoma diagnoses. But AI models present the “black box” problem, meaning they provide no explanation of how outputs are produced, so human end users aren’t able to provide meaningful oversight. To counter this, xAI methods can be applied to break down the processes which lead to certain results, rendering them understandable to human end users and therefore acceptable to use in high-stakes settings such as healthcare.
The next discussion question in the breakout room was: How can the implemented xAI methods, such as Layer-wise Relevance Propagation (LRP) and Concept Relevance Propagation (CRP), enhance non-technical people’s understanding of the iToBoS AI models? Are there other techniques that might be more intuitive and user-friendly? The responses to this question are reflected in the word map in Figure 1.