Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Mixed citations
& Smith-Loud, J
Mixed citation behavior. Most common role is background (60%).
10
Pith papers citing it
Background
60% of classified citations
citation-role summary
background 5
citation-polarity summary
roles
background 5representative citing papers
Derives an approximate formula for the precision of top-q selections made by a panel of n AIs with average correlation ρ.
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
Gopher, a 280 billion parameter language model, achieves state-of-the-art performance on the majority of 152 tasks with largest gains in reading comprehension, fact-checking, and toxic language detection.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
PaLM 2 reports state-of-the-art results on language, reasoning, and multilingual tasks with improved efficiency over PaLM.
Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.
citing papers explorer
-
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
-
Quantifying how AI Panels improve precision
Derives an approximate formula for the precision of top-q selections made by a panel of n AIs with average correlation ρ.
-
Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
Generative LMs in laissez-faire open-ended prompting settings disproportionately generate subordinated portrayals of minoritized race, gender, and sexual orientation identities at rates hundreds to thousands of times higher than empowering ones.
-
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Gopher, a 280 billion parameter language model, achieves state-of-the-art performance on the majority of 152 tasks with largest gains in reading comprehension, fact-checking, and toxic language detection.
-
Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
-
Evaluating Structured Documentation as a Tool for Reflexivity in Dataset Development
Structured dataset documentation shows little engagement with major reflexivity themes from FAccT literature, leading to a new codebook and extended datasheet questions.
-
The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
-
PaLM 2 Technical Report
PaLM 2 reports state-of-the-art results on language, reasoning, and multilingual tasks with improved efficiency over PaLM.
-
Frozen Multimodal Embeddings for AI-Assisted Interview Assessment of Personality and Cognitive Ability
Frozen multimodal embeddings with trait-specific late fusion cut personality prediction MSE by 19% relative to baseline in the 2026 AVI challenge, while cognitive results are attributed to validation shortcuts rather than content-based inference.
- Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions