Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
It critiques foundational issues in XAI, highlighting incremental concerns rather than new solutions.
The paper identifies fundamental misconceptions in current XAI research, such as unclear purposes and questionable goals, and suggests steps to improve the field's substance.
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.