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Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
The simultaneous inhibition of HDACs and BET proteins has shown promising anti-proliferative effects against different cancer types, including the difficult to treat pancreatic cancer. In this work, the strategy of concurrently targeting HDACs and BET proteins was pursued by developing different types of dual inhibitors.
By developing a novel scaffold that selectively inhibits HDAC1/2 together with BET proteins in cells, an effective tool for the investigation of pancreatic cancer, and other diseases which are sensitive to epigenetic processes, was created. The compound’s small size further gives the opportunity to further develop the inhibitor towards optimized pharmacokinetic properties, potentially resulting in a drug for cancer treatment.
A second novel approach that was pursued, was the development of a small-molecule degrader, targeting HDACs and BET proteins. Through synthesizing a variety of different molecules, a compound that was capable of lowering BRD4 levels and, at the same time, increasing histone acetylation was developed. While additional mechanistic investigations are needed to verify the degradation, the potent antiproliferative effects in pancreatic cancer cells encourage further studies following this alternative new strategy.