  High-performing predictive models, such as neural nets, usually operate as
black boxes, which raises serious concerns about their interpretability. Local
feature attribution methods help to explain black box models and are therefore
a powerful tool for assessing the reliability and fairness of predictions. To
this end, most attribution models compare the importance of input features with
a reference value, often called baseline. Recent studies show that the baseline
can heavily impact the quality of feature attributions. Yet, we frequently find
simplistic baselines, such as the zero vector, in practice. In this paper, we
show empirically that baselines can significantly alter the discriminative
power of feature attributions. We conduct our analysis on tabular data sets,
thus complementing recent works on image data. Besides, we propose a new
taxonomy of baseline methods. Our experimental study illustrates the
sensitivity of popular attribution models to the baseline, thus laying the
foundation for a more in-depth discussion on sensible baseline methods for
tabular data.
