This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: In 2001, Brenda Andrew was convicted of murdering her husband, and in 2004 she was sentenced to death. But 21 years later, the Supreme Court found that prosecutors' evidence about her sex life, appearance and perceived failings as a wife and mother could be unduly prejudicial and remanded the case to a lower court.
Inspired in part by this decision, Cornell information science researchers, together with Cornell Law School faculty, found that prosecutors invoke a range of gender stereotypes against women defendants to increase the likelihood of convictions and death sentences. In particular, lawyers use terms associated with gender roles, sexualization, appearance, parenting and LGBTQ+ identity more frequently in the trials of women than of men. The findings could help spur systemic changes in how female defendants are viewed, and talked about, in court.
"Creating methods that can be applied to real cases to identify individual instances of bias is important in correcting injustices in the legal system," said Emma Harvey, a doctoral student in information science and co-author of "Fundamentally Unfair: Analysis of Gendered Discourse in U.S. Capital Trials," which was presented at the ACM Conference on AI, Ethics, and Society, held Oct. 12–14 in Malmo, Sweden. "Our long-term goal, especially in collaboration with the folks at Cornell Law, is thinking about how to address all of these different pernicious forms of biases and taking steps to mitigate them concretely," said Allison Koenecke, assistant professor of information science at Cornell Tech and the Cornell Ann S.
Bowers College of Computing and Information Science. The lead author is Camille Harris, assistant professor of computer science at the University of San Francisco and a former postdoctoral researcher in Koenecke's lab. Harvey and Andrea Wen-Yi Wang, also a doctoral student in information science, are co-authors.
The team employed natural language processing (NLP) to analyze thousands of pages of transcripts from legal proceedings involving defendants in murder trials. Co-authors Sandra Babcock and Rachel Goldberg, clinical professors of law at Cornell Law School, compiled the corpus of U.S. capital trial transcripts—137 in all, including those of all cisgender women currently on death row. Babcock is faculty director of the Cornell Center on the Death Penalty Worldwide, and over the last 30 years has helped defend hundreds of people facing execution around the world.
"Our research findings draw on the power of NLP and the nuance captured by qualitative coding," Babcock said. "Our project illustrates how scholars in disparate fields can work together to produce rigorous, multilayered analysis of voluminous legal texts." Babcock is among a handful of legal scholars who've explored the idea of gender bias in capital cases. One common theme: the false concept that women possess an intrinsic maternal instinct leads to harsh judgments of their parenting skills.
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