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Despite progress in the field, significant parts of current XAI research arestill not on solid conceptual, ethical, or methodological grounds.Unfortunately, these unfounded parts are not on the decline but continue togrow. Many explanation techniques are still proposed without clarifying theirpurpose. Instead, they are advertised with ever more fancy-looking heatmaps oronly seemingly relevant benchmarks. Moreover, explanation techniques aremotivated with questionable goals, such as building trust, or rely on strongassumptions about the 'concepts' that deep learning algorithms learn. In thispaper, we highlight and discuss these and other misconceptions in current XAIresearch. We also suggest steps to make XAI a more substantive area ofresearch.

Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research