Tag
This article explores how AI choices today shape future human rights, using the Dutch SyRI risk profiling system as a case study where efficiency and optimization led to privacy violations and human rights issues.
This study audits CLIP models for gender bias in Metropolitan Museum artwork metadata, finding no statistically significant bias but emphasizing the need for multivariate confound control in AI fairness assessments.
This study examines how large language models judge urban safety based on neighborhood names versus coordinates, revealing that names carry both crime signal and demographic bias, with bias scaling with geographic knowledge.
This paper audits confidence-based label filtering in automated credit default prediction, revealing income-conditional recall disparities and showing that blinding models to sensitive attributes does not eliminate fairness gaps when proxies and institutional biases persist.
Ars Technica reports on the limitations of AI-based content moderation, highlighting biases against marginalized groups and the need for human oversight, while noting Reddit's expansion of Rules Hub to give human moderators more control.
A lawsuit by Meta employees highlights the difficulty of proving discrimination when AI systems are used in firing decisions.
Stanford HAI reports that AI hiring tools can yield racial bias and systemic rejection due to algorithmic monocultures, where similar models lead to widespread discrimination.
This paper conducts a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, finding that racial steering in housing recommendations is an emergent behavior of the model's interpretive license, varying by user identity and city context.
This large-scale study of 3.4 million job applicants across 156 employers reveals that algorithmic monocultures in hiring algorithms from a single vendor cause racial disparities and systemic rejections, with 25.87% of Black applicants and 14.74% of Asian applicants adversely impacted.
After X’s translation feature update, traffic in the Chinese creator community has generally declined. The algorithm favors English content, making it difficult for Chinese content to reach global users, potentially impacting industries reliant on Chinese content creation.
A research paper analyzing how algorithmic monoculture in hiring—where many employers use the same vendor's screening algorithms—leads to systematic rejection of the same individuals and racial groups, using a dataset of 3 million applicants.
The article argues that AI could resolve political inefficiencies by optimizing for societal outcomes rather than self-interest, while acknowledging the significant risks of manipulation and authoritarianism.