  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.
