Abstract: Why is a given point in a dataset marked as an outlier by an off-the-shelf detection algorithm? Which feature(s) explain it the best? What is the best way to convince a human analyst that the point is indeed an outlier? We provide succinct, interpretable, and simple pictorial explanations of outlying behavior in multi dimensional real-valued datasets while respecting the limited attention of human analysts. Specifically, we propose to output a few pictures (focus-plots, ie., pairwise feature plots) from a few, carefully chosen feature sub-spaces. The proposed LookOut makes four contributions: (a) problem formulation: we introduce an “analyst-centered” problem formulation for explaining outliers via focus-plots, (b) explanation algorithm: we propose a plot-selection objective and the LookOut algorithm to approximate it with optimality guarantees, (c) generality: our explanation algorithm is both domain- and detector-agnostic, and (d) scalability: LookOut scales linearly with the size of input outliers to explain and the explanation budget. Our experiments show that LookOut performs near-ideally in terms of maximizing explanation objective on several real datasets, while producing fast, visually interpretable and intuitive results in explaining groundtruth outliers from several real-world datasets.