Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909710440988672 |
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| author | Spillner, Laura Zargham, Nima Pomarlan, Mihai Porzel, Robert Malaka, Rainer |
| author_facet | Spillner, Laura Zargham, Nima Pomarlan, Mihai Porzel, Robert Malaka, Rainer |
| contents | The need for explanations in AI has, by and large, been driven by the desire to increase the transparency of black-box machine learning models. However, such explanations, which focus on the internal mechanisms that lead to a specific output, are often unsuitable for non-experts. To facilitate a human-centered perspective on AI explanations, agents need to focus on individuals and their preferences as well as the context in which the explanations are given. This paper proposes a personalized approach to explanation, where the agent tailors the information provided to the user based on what is most likely pertinent to them. We propose a model of the agent's worldview that also serves as a personal and dynamic memory of its previous interactions with the same user, based on which the artificial agent can estimate what part of its knowledge is most likely new information to the user. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations Spillner, Laura Zargham, Nima Pomarlan, Mihai Porzel, Robert Malaka, Rainer Artificial Intelligence Human-Computer Interaction The need for explanations in AI has, by and large, been driven by the desire to increase the transparency of black-box machine learning models. However, such explanations, which focus on the internal mechanisms that lead to a specific output, are often unsuitable for non-experts. To facilitate a human-centered perspective on AI explanations, agents need to focus on individuals and their preferences as well as the context in which the explanations are given. This paper proposes a personalized approach to explanation, where the agent tailors the information provided to the user based on what is most likely pertinent to them. We propose a model of the agent's worldview that also serves as a personal and dynamic memory of its previous interactions with the same user, based on which the artificial agent can estimate what part of its knowledge is most likely new information to the user. |
| title | Finding Uncommon Ground: A Human-Centered Model for Extrospective Explanations |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.21571 |