Related papers: Gender differences in research grant allocation --…

Gender norms are widely recognized as key determinants of persistent gender gaps in the labor market. However, our understanding of the drivers of gender norms, and their implications for preferences, remain lacking. This paper addresses…

Digital collaborative platforms have become crucial venues of career advancement and individual success in many creative fields, from engineering to the arts. Indirect gender discrimination is a key component to gendered disadvantage on…

Recent studies show that large language models (LLMs) exhibit self-preference bias when serving as judges, meaning they tend to favor their own responses over those generated by other models. Existing methods typically measure this bias by…

The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date…

This study examines a basic assumption of peer review, namely, the idea that there is a consensus on evaluation criteria among peers, which is a necessary condition for the reliability of peer judgements. Empirical evidence indicating that…

The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to…

Recent studies have shown that the proposal peer review processes employed by a variety of organizations to allocate astronomical telescope time produce outcomes that are systematically biased depending on whether proposal's principal…

Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals and institutions. More then ever before, hiring, promotion, and funding decisions rely on a variety of impact metrics that cannot disentangle…

Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems.…

Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that…

Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant…

In this work, I point out the negative implications for diversity in graduate school resulting from the use of cutoff scores on the GRE in the admissions process. In light of the data presented, as well as a swelling body of evidence…

Academic meritocracy is jeopardized by systematic imbalances; for example, whereas Black and Hispanic individuals constitute over 30% of the U.S. population, they represent fewer than 10% of tenured academics in science and engineering.…

Online dating platforms have gained widespread popularity as a means for individuals to seek potential romantic relationships. While recommender systems have been designed to improve the user experience in dating platforms by providing…

While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing sequential decision-making to positively impact long-term social…

Conversations around diversity and inclusion in software engineering often focus on gender and racial disparities. However, the State of Devs 2025 survey with 8,717 participants revealed that other forms of discrimination are similarly…

Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness.…

Several recent investigations indicate the existence of gender-related systematic trends in the peer review of proposals for observations on astronomical facilities. This includes the National Radio Astronomy Observatory (NRAO) where there…

The rapid development of SpeechLLM-based conversational AI systems has created a need for robustly benchmarking these efforts, including aspects of fairness and bias. At present, such benchmarks typically rely on multiple choice question…

In an era where AI-driven hiring is transforming recruitment practices, concerns about fairness and bias have become increasingly important. To explore these issues, we introduce a benchmark, FAIRE (Fairness Assessment In Resume…