Evidence-based explanation to promote fairness in AI systems
This addresses the need for transparency in AI systems to ensure fairness and responsibility in decision-making, particularly in sensitive domains like judicial scenarios, but it is a position paper and thus incremental in nature.
The paper tackles the problem of explaining AI-assisted decisions to promote fairness, proposing an evidence-based explanation design approach to 'tell the story of a decision' in fairness-sensitive contexts.
As Artificial Intelligence (AI) technology gets more intertwined with every system, people are using AI to make decisions on their everyday activities. In simple contexts, such as Netflix recommendations, or in more complex context like in judicial scenarios, AI is part of people's decisions. People make decisions and usually, they need to explain their decision to others or in some matter. It is particularly critical in contexts where human expertise is central to decision-making. In order to explain their decisions with AI support, people need to understand how AI is part of that decision. When considering the aspect of fairness, the role that AI has on a decision-making process becomes even more sensitive since it affects the fairness and the responsibility of those people making the ultimate decision. We have been exploring an evidence-based explanation design approach to 'tell the story of a decision'. In this position paper, we discuss our approach for AI systems using fairness sensitive cases in the literature.