Andrew Lo | Searcharxiv

arXiv2026

Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager

The growing prominence of large language models (LLMs) in daily life has heightened concerns that LLMs exhibit many of the same gender-related biases as their creators. In the context of hiring decisions, we quantify the degree to which LLMs perpetuate societal biases and investigate prompt engineering as a bias mitigation technique. Our findings suggest that for a given resum\'e, an LLM is more likely to hire a female candidate and perceive them as more qualified, but still recommends lower pay relative to male candidates.

cs.CY↗

arXiv2017

Is Smaller Better: A Proposal To Consider Bacteria For Biologically Inspired Modeling

Bacteria are easily characterizable model organisms with an impressively complicated set of capabilities. Among their capabilities is quorum sensing, a detailed cell-cell signaling system that may have a common origin with eukaryotic cell-cell signaling. Not only are the two phenomena similar, but quorum sensing, as is the case with any bacterial phenomenon when compared to eukaryotes, is also easier to study in depth than eukaryotic cell-cell signaling. This ease of study is a contrast to the only partially understood cellular dynamics of neurons. Here we review the literature on the strikingly neuron-like qualities of bacterial colonies and biofilms, including ion-based and hormonal signaling, and action potential-like behavior. This allows them to feasibly act as an analog for neurons that could produce more detailed and more accurate biologically-based computational models. Using bacteria as the basis for biologically feasible computational models may allow models to better harness the tremendous ability of biological organisms to make decisions and process information. Additionally, principles gleaned from bacterial function have the potential to influence computational efforts divorced from biology, just as neuronal function has in the abstract influenced countless machine learning efforts.

q-bio.NC↗