# From Philosophy to Interfaces: an Explanatory Method and a Tool Inspired by Achinstein's Theory of Explanation

arXiv: https://arxiv.org/abs/2109.04171 · HTML: https://mlsanity.com/paper/2109.04171/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [KBQA: Learning Question Answering over QA Corpora and Knowledge Bases](https://mlsanity.com/paper/1903.02419/) (2019)
- [LAReQA: Language-agnostic answer retrieval from a multilingual pool](https://mlsanity.com/paper/2004.05484/) (2020)
- [A Grounded Interaction Protocol for Explainable Artificial Intelligence](https://mlsanity.com/paper/1903.02409/) (2019)

## Built on by (most cited first, 8 of 8)

- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey](https://mlsanity.com/paper/2403.14496/) (2024)
- [Making Things Explainable vs Explaining: Requirements and Challenges under the GDPR](https://mlsanity.com/paper/2110.00758/) (2021)
- [Explanation-Aware Experience Replay in Rule-Dense Environments](https://mlsanity.com/paper/2109.14711/) (2021)
- [Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces](https://mlsanity.com/paper/2110.00762/) (2021)
- [Explanation User Interfaces: A Systematic Literature Review](https://mlsanity.com/paper/2505.20085/) (2025)
- [Beyond Technocratic XAI: The Who, What & How in Explanation Design](https://mlsanity.com/paper/2508.09231/) (2025)

## Abstract (arXiv)

We propose a new method for explanations in Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. Among the mainstream philosophical theories of explanation we identified one that in our view is more easily applicable as a practical model for user-centric tools: Achinstein's Theory of Explanation. With this work we aim to prove that the theory proposed by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive process answering questions. To this end we found a way to handle the generic (archetypal) questions that implicitly characterise an explanatory processes as preliminary overviews rather than as answers to explicit questions, as commonly understood. To show the expressive power of this approach we designed and implemented a pipeline of AI algorithms for the generation of interactive explanations under the form of overviews, focusing on this aspect of explanations rather than on existing interfaces and presentation logic layers for question answering. We tested our hypothesis on a well-known XAI-powered credit approval system by IBM, comparing CEM, a static explanatory tool for post-hoc explanations, with an extension we developed adding interactive explanations based on our model. The results of the user study, involving more than 100 participants, showed that our proposed solution produced a statistically relevant improvement on effectiveness (U=931.0, p=0.036) over the baseline, thus giving evidence in favour of our theory.