paper-with-me

Papers

Adding Why to What? Analyses of an Everyday Explanation

2023-08-08 · Lutz Terfloth, Michael Schaffer, Heike M. Buhl, Carsten Schulte

In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.

📄 PDF Abstract BibTeX arXiv:2308.04187

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Focus 설명 없음

Similar Papers 제목 키워드 기반

What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments

2019-11-01 · David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan

Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView s…

What Gets Echoed? Understanding the ``Pointers'' in Explanations of Persuasive Arguments

2019-11-01 · IJCNLP 2019 11 · David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan

Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView s…

Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures

2021-08-08 · Devleena Das, Sonia Chernova

When interacting in unstructured human environments, occasional robot failures are inevitable. When such failures occur, everyday people, rather than trained technicians, will be the first to respond. Existing natural la…

Descriptive

How can we trust opaque systems? Criteria for robust explanations in XAI

2025-08-18 · Florian J. Boge, Annika Schuster arxiv

Deep learning (DL) algorithms are becoming ubiquitous in everyday life and in scientific research. However, the price we pay for their impressively accurate predictions is significant: their inner workings are notoriousl…

Reimagining Anomalies: What If Anomalies Were Normal?

2024-02-22 · Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz 외

Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a …

Anomaly Detectioncounterfactual