# Explanation as Question Answering based on a Task Model of the Agent's Design

arXiv: https://arxiv.org/abs/2206.05030 · HTML: https://mlsanity.com/paper/2206.05030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 8 Jun 2022.

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

- [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019)
- [A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges](https://mlsanity.com/paper/2007.13069/) (2020)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Agent Smith: Teaching Question Answering to Jill Watson](https://mlsanity.com/paper/2112.13677/) (2021)
- [Explanation as Question Answering based on Design Knowledge](https://mlsanity.com/paper/2112.09616/) (2021)

## Built on by (most cited first, 2 of 2)

- [Combining Cognitive and Generative AI for Self-explanation in Interactive AI Agents](https://mlsanity.com/paper/2407.18335/) (2024)
- [Designing a Communication Bridge between Communities: Participatory Design for a Question-Answering AI Agent](https://mlsanity.com/paper/2308.00813/) (2023)

## Abstract (arXiv)

We describe a stance towards the generation of explanations in AI agents that is both human-centered and design-based. We collect questions about the working of an AI agent through participatory design by focus groups. We capture an agent's design through a Task-Method-Knowledge model that explicitly specifies the agent's tasks and goals, as well as the mechanisms, knowledge and vocabulary it uses for accomplishing the tasks. We illustrate our approach through the generation of explanations in Skillsync, an AI agent that links companies and colleges for worker upskilling and reskilling. In particular, we embed a question-answering agent called AskJill in Skillsync, where AskJill contains a TMK model of Skillsync's design. AskJill presently answers human-generated questions about Skillsync's tasks and vocabulary, and thereby helps explain how it produces its recommendations.