Rosamunde Van Brakel

Why they matter

Researcher with public evidence across Policy & governance, AI research, Culture, work & education.

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assistant prof criminology teaching legal, ethical & social issues AI/sociology of law @VUB research: digital & more-than-human criminology, surveillance & AI, youth justice, human rights https://researchportal.vub.be/en/persons/rosamunde-elise-van-brakel

Articles & links

‘Our new paper offers a rare look inside the “black box” of algorithmic hiring, showing that these tools increase racial bias and shut the same people out of jobs everywhere they apply’ hai.stanford.edu/news/ai-hiri...

AI Hiring Tools Can Yield Racial Bias and Systemic Rejection | Stanford HAI hai.stanford.edu
AI Weekly's analysis
  • A Stanford HAI study of 4 million applications found 26% of Black applicants faced AI-screened positions that discriminated against their racial group.
  • Roughly 40,000 more applications would have advanced if the AI tool had recommended candidates at equal rates across racial groups.
  • Ten percent of applicants who submitted four applications screened by the same vendor were rejected from all of them, exceeding independent-probability expectations.
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View on Bluesky · ♥ 11 ↻ 5 ↩ 0 · 3 from the directory shared this · 52d ago
Rosamunde Van Brakel reposted
Frank Pasquale @frankpasquale.bsky.social

“New scientific endeavors often start with an empirical tension in which nature presents something interesting we cannot predict or explain with existing tools, and although neural networks are artificial computational systems, this same scientific tension is present here.” ar…

There Will Be a Scientific Theory of Deep Learning arxiv.org
AI Weekly's analysis
  • A team of fourteen researchers argues that a scientific theory of deep learning is emerging, and proposes calling it 'learning mechanics.'
  • They point to five converging strands: solvable settings, tractable limits, neural scaling laws, hyperparameter theories, and universal behaviors across models.
  • The framing casts learning mechanics as deep learning's 'physics,' with mechanistic interpretability playing the 'biology' role in the same field.
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