Algorithmic nudge to make better choices: Evaluating effectiveness of XAI frameworks to reveal biases in algorithmic decision making to users
This addresses the issue of biased algorithmic decision-making for users exposed to problematic content, but it is incremental as it builds on existing XAI frameworks without new empirical validation.
The paper tackles the problem of algorithmic biases in content exposure by proposing XAI-based interventions, such as facts with fore warnings or counterfactual explanations, to help users make better choices, though no concrete results or numbers are provided.
In this position paper, we propose the use of existing XAI frameworks to design interventions in scenarios where algorithms expose users to problematic content (e.g. anti vaccine videos). Our intervention design includes facts (to indicate algorithmic justification of what happened) accompanied with either fore warnings or counterfactual explanations. While fore warnings indicate potential risks of an action to users, the counterfactual explanations will indicate what actions user should perform to change the algorithmic outcome. We envision the use of such interventions as `decision aids' to users which will help them make informed choices.