Summary of Explainable and Human-grounded Ai For Decision Support Systems: the Theory Of Epistemic Quasi-partnerships, by John Dorsch and Maximilian Moll
Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships
by John Dorsch, Maximilian Moll
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an approach to developing AI decision support systems that provide human decision-makers with three types of human-grounded explanations: reasons, counterfactuals, and confidence. The authors argue that this approach, referred to as the RCC approach, is essential for meeting the demands of ethical and explainable AI (XAI). They begin by reviewing current empirical XAI literature on the relationship between various methods for generating model explanations and end-user accuracy. The authors demonstrate how current theories about what constitutes good human-grounded reasons do not adequately explain this evidence or offer sound ethical advice for development. Instead, they offer a novel theory of human-machine interaction: the theory of epistemic quasi-partnerships (EQP). This approach explains the empirical evidence, offers sound ethical advice, and entails adopting the RCC approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI more trustworthy and understandable. It’s all about how we can design artificial intelligence systems to explain themselves in a way that humans can understand. Right now, there are many different ways to make AI explain itself, but they don’t always work well or help us trust the AI. The authors of this paper propose a new approach called RCC (reasons, counterfactuals, and confidence) that provides three types of explanations for people who use the AI. They also introduce a new idea about how humans interact with machines, which they call epistemic quasi-partnerships. This idea helps us understand why the RCC approach is important and how it can help make AI more trustworthy. |
