Related papers: Measuring the Sources of Taste-Based Discriminatio…
Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users' choices. This paper presents an experimental protocol for measuring the degree to which positively or…
Research on underrepresented populations is essential for fostering greater diversity within the software industry. Team diversity is important for reasons that go beyond ethics. Diversity contributes to greater innovation and productivity,…
In observational studies of discrimination, the most common statistical approaches consider either the rate at which decisions are made (benchmark tests) or the success rate of those decisions (outcome tests). Both tests, however, have…
Objective: The goal of this study is to understand how people experience sexism and sexual harassment in the workplace by discovering themes in 2,362 experiences posted on the Everyday Sexism Project's website everydaysexism.com. Method:…
LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task allocation, they raise concerns of fairness. Prior studies have…
Background: As most of the software development organizations are male-dominated, female developers encountering various negative workplace experiences reported feeling like they "do not belong". Exposures to discriminatory expletives or…
In this paper, we extend Gary Becker's empirical analysis of the marriage market to same-sex couples. Becker's theory rationalizes the well-known phenomenon of homogamy among different-sex couples: individuals mate with their likes because…
We explore conclusions a person draws from observing society when he allows for the possibility that individuals' outcomes are affected by group-level discrimination. Injecting a single non-classical assumption, that the agent is…
As LLMs are increasingly applied in socially impactful settings, concerns about gender bias have prompted growing efforts both to measure and mitigate such bias. These efforts often rely on evaluation tasks that differ from natural language…
We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For…
Outcome tests are a popular method for detecting bias in lending, hiring, and policing decisions. These tests operate by comparing the success rate of decisions across groups. For example, if loans made to minority applicants are observed…
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hierarchical gender hiring bias in Large Language Models…
Large language models (LLMs) are increasingly being introduced in workplace settings, with the goals of improving efficiency and fairness. However, concerns have arisen regarding these models' potential to reflect or exacerbate social…
Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list…
The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although…
One of the goals of fairness research in NLP is to measure and mitigate stereotypical biases that are propagated by NLP systems. However, such work tends to focus on single axes of bias (most often gender) and the English language.…
In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of…
We have designed an online survey to understand the status quo of four dimensions of gender biases among the contemporary computing organizations. Our preliminary results found almost one-third of the respondents have reported first-hand…
This paper proposes a novel empirical strategy to measure cultural justifications of domestic violence within households, with direct implications for demographic behavior and gender inequality. Leveraging survey data on individual…
Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis.…