Sex Bias in Graduate Admissions: Data from Berkeley

By Peter J. Bickel, E. Hammel, J. O'connell. 1975

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, neutral): “te different from, and even opposite to, associations found in the underlying subgroups. One of the better-known examples of Simpson’s paradox arose during the gender bias lawsuit against UC Berkeley [15]. Analysis of graduate school admissions data appeared to reveal a statistically significant bia”
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved (2018, background): “hical and legal needs to demonstrate a lack of discrimination with respect to protected classes [2, 26]. Examples of such decisions include employment and compensation [14, 20], university admissions [4], and sentence and bail setting [3, 7, 16]. Another example relevant to the financial services in”
Causal Reasoning for Algorithmic Fairness (2018, methodology): “The Berkeley admissions example [3] is often used to explain Simpson’s paradox [38] and highlight the importance of adjusting for covariates.”
Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data (2018, background): “Arguably the most famous example of Simpson’s paradox arose from a lawsuit alleging gender discrimination at UC Berkeley (Bickel, Hammel, and O’Connell 1975).”
Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data (2018, background): “Bickel et al. [4] attributed this effect to Simpson’s paradox. argued that the subtle correlations between the popularity of departments among the genders and their selectivity resulted in women applying to departments that were hardest to get into, which skewed analysis.”
Avoiding Discrimination through Causal Reasoning (2017, neutral): “Bickel [20] had shown earlier that a lower college-wide admission rate for women than for men was explained by the fact that women applied in more competitive departments.”
Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020, methodology): “We have aforementioned examples of Simpson’s paradox in college admission policies [26] and epidemiology [10].”
Exposing the probabilistic causal structure of discrimination (2015, neutral): “that women tended to apply to departments with lower rates of admission, while men tended to apply to departments with higher rates [1].”
A Review on Fairness in Machine Learning (2022, background): “One of the common examples that is used to illustrate the importance of causality to identify and mitigate unfairness is the gender bias in Berkeley admission in 1973 [24, 137].”
Detecting Simpson's Paradox (2018, background): “A famous example of Simpson’s paradox is in relative rates of graduate admission by gender that reverses when departments are considered individually (Bickel et al., 1975).”
Veridical data science (2019, background): “For instance, many researchers have studied paradoxes surrounding associations that lead to unstable estimates of causal eﬀects (53–55).”
Proxy Fairness (2018, background): “The classic example is when women were admitted to Berkeley’s graduate programs at overall lower rates than men in 1973, because they were applying to more selective departments on average [2].”