# Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships

arXiv: https://arxiv.org/abs/2409.14839 · HTML: https://mlsanity.com/paper/2409.14839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making](https://mlsanity.com/paper/2001.02114/) (2020)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://mlsanity.com/paper/2005.01831/) (2020)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [A Human-Grounded Evaluation of SHAP for Alert Processing](https://mlsanity.com/paper/1907.03324/) (2019)
- [Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants](https://mlsanity.com/paper/2108.01923/) (2021)
- [Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence](https://mlsanity.com/paper/2206.02790/) (2022)
- [Designing Trustworthy User Interfaces](https://mlsanity.com/paper/2202.12915/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Mechanistic Explanatory Strategy for XAI](https://mlsanity.com/paper/2411.01332/) (2024)
- [Stop Saying "AI"](https://mlsanity.com/paper/2602.17729/) (2026)
- [AI, Trust, and Teaming: The Humans-as-Handlers Approach for Autonomous and Opaque AI Systems](https://mlsanity.com/paper/2607.00523/) (2026)

## Abstract (arXiv)

In the context of AI decision support systems (AI-DSS), we argue that meeting the demands of ethical and explainable AI (XAI) is about developing AI-DSS to provide human decision-makers with three types of human-grounded explanations: reasons, counterfactuals, and confidence, an approach we refer to as the RCC approach. We begin by reviewing current empirical XAI literature that investigates the relationship between various methods for generating model explanations (e.g., LIME, SHAP, Anchors), the perceived trustworthiness of the model, and end-user accuracy. We demonstrate how current theories about what constitutes good human-grounded reasons either do not adequately explain this evidence or do not offer sound ethical advice for development. Thus, we offer a novel theory of human-machine interaction: the theory of epistemic quasi-partnerships (EQP). Finally, we motivate adopting EQP and demonstrate how it explains the empirical evidence, offers sound ethical advice, and entails adopting the RCC approach.