Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
원문 초록
Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts are not on the decline but continue to grow. Many explanation techniques are still proposed without clarifying their purpose. Instead, they are advertised with ever more fancy-looking heatmaps or only seemingly relevant benchmarks. Moreover, explanation techniques are motivated with questionable goals, such as building trust, or rely on strong assumptions about the 'concepts' that deep learning algorithms learn. In this paper, we highlight and discuss these and other misconceptions in current XAI research. We also suggest steps to make XAI a more substantive area of research.
함께 살펴볼 논문
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Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano Things
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Do Protein Transformers Have Biological Intelligence?
TIMING: Temporality-Aware Integrated Gradients for Time Series Explanation
A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
Time series saliency maps: explaining models across multiple domains
토론
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