Fairness in machine learning, particularly in classifiers, is receiving increasing attention. However, most studies on this topic focus on fairness metrics for a limited number of predefined groups and do not address fairness across intersectional subgroups. In this paper, we investigate ways to improve subgroup fairness where subgroups are defined by the intersection of protected attributes. Specifically, our paper reveals the correlation between the representation bias of training data and model fairness. We demonstrate that biased sample collection due to historical biases and a lack of control over data collection can lead to unfairness in learned models. We introduce the concept of an “Implicit Biased Set (IBS)”, which refers to regions in the intersectional attribute space where positive and negative examples are not proportionately represented. For example, if our training data set has a disproportionate representation of black male recidivists, then criminal risk assessment tools are more likely to discriminate against black males, even if they are innocent. We propose an efficient pre-processing approach that initially identifies IBS and then employs techniques to remedy the data collection within IBS. Our evaluation shows that our method effectively mitigates various subgroup biases regardless of the downstream machine learning models used.

Predicate pushdown is a widely adopted query optimization. Existing systems and prior work mostly use pattern-matching rules to decide when a predicate can be pushed through certain operators like join or groupby. However, challenges arise in optimizing for data science pipelines due to the widely used non-relational operators and user-defined functions (UDF) that existing rules would fail to cover. In this paper, we present MagicPush, which decides predicate pushdown using a search-verification approach.MagicPush searches for candidate predicates on pipeline input, which is often not the same as the predicate to be pushed down, and verifies that the pushdown does not change pipeline output with full correctness guarantees. Our evaluation on TPC-H queries and 200 real-world pipelines sampled from GitHub Notebooks shows that MagicPush substantially outperforms a strong baseline that uses a union of rules from prior work - it is able to discover new pushdown opportunities and better optimize 42 real-world pipelines with up to 99% reduction in running time, while discovering all pushdown opportunities found by the existing baseline on remaining cases.

Data analytics often make sense of large data sets by generalization: aggregating from the detailed data to a more general context. Given a dataset, misleading generalizations can sometimes be drawn from a cherry-picked level of aggregation to obscure substantial subgroups that oppose the generalization. Our goal is to detect and explain cherry-picked generalizations by refining the corresponding aggregate queries. We demonstrate OREO, a system to compute a support score of the given statement to quantify the quality of the generalization; that is, whether the aggregated result is an accurate reflection of the data. To better understand the resulting score, our system also identifies significant counterexamples and alternative statements that better represent the data at hand. We will demonstrate the utility of OREO for investigating generalizations, by interacting with the VLDB'22 participants who will use the OREO interface for statement validation and explanation.

Generalizing from detailed data to statements in a broader context is often critical for users to make sense of large data sets. Correspondingly, poorly constructed generalizations might convey misleading information even if the statements are technically supported by the data. For example, a cherry-picked level of aggregation could obscure substantial sub-groups that oppose the generalization. We present a framework for detecting and explaining cherry-picked generalizations by refining aggregate queries. We present a scoring method to indicate the appropriateness of the generalizations. We design efficient algorithms for score computation. For providing a better understanding of the resulting score, we also formulate practical explanation tasks to disclose significant counterexamples and provide better alternatives to the statement. We conduct experiments using real-world data sets and examples to show the effectiveness of our proposed evaluation metric and the efficiency of our algorithmic framework.