Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations
This addresses the need for more accessible AI explanations for non-experts, though it is incremental as it builds on existing human-centered explanation concepts.
The paper tackles the problem of AI explanations being unsuitable for non-experts by proposing a personalized approach that tailors information to user preferences and context, resulting in a model that estimates what knowledge is new to the user based on previous interactions.
The need for explanations in AI has, by and large, been driven by the desire to increase the transparency of black-box machine learning models. However, such explanations, which focus on the internal mechanisms that lead to a specific output, are often unsuitable for non-experts. To facilitate a human-centered perspective on AI explanations, agents need to focus on individuals and their preferences as well as the context in which the explanations are given. This paper proposes a personalized approach to explanation, where the agent tailors the information provided to the user based on what is most likely pertinent to them. We propose a model of the agent's worldview that also serves as a personal and dynamic memory of its previous interactions with the same user, based on which the artificial agent can estimate what part of its knowledge is most likely new information to the user.