Preventing Discriminatory Decision-making in Evolving Data Streams

Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Second, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Adding fairness constraints to this already complicated task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream ($FS^2$), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to evaluate and compare the trade-off between performance and fairness of different bias mitigation methods efficiently. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature

ZFP36 loss-mediated BARX1 stabilization promotes malignant phenotypes by transactivating master oncogenes in NSCLC

Abstract Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with high morbidity and mortality worldwide. Although the dysregulation of BARX1 expression has been shown to be associated with malignant cancers, including NSCLC, the underlying mechanism remains elusive. In this study, we identified BARX1 as a common differentially expressed gene in lung squamous cell carcinoma and adenocarcinoma. Importantly, we uncovered a novel mechanism behind the regulation of BARX1, in which ZFP36 interacted with 3’UTR of BARX1 mRNA to mediate its destabilization. Loss of ZFP36 led to the upregulation of BARX1, which further promoted the proliferation, migration and invasion of NSCLC cells. In addition, the knockdown of BARX1 inhibited tumorigenicity in mouse xenograft. We demonstrated that BARX1 promoted the malignant phenotypes by transactivating a set of master oncogenes involved in the cell cycle, DNA synthesis and metastasis. Overall, our study provides insights into the mechanism of BARX1 actions in NSCLC and aids a better understanding of NSCLC pathogenesis