AI Hiring Bias: Why LLMs Prefer Their Own Resumes
335
LLMs used in hiring unfairly prioritize resumes generated by their own model over human-written content, creating a systemic 'self-preference' bias.
AI Bias
Research, analysis, and discussion of biases embedded in AI systems, including ideological, political, demographic, and cultural biases in training data, model outputs, and alignment processes.
Reading List
The Risk of Cognitive Inbreeding in the AI Era
230
AI-assisted cognition threatens to stagnate human progress by narrowing the diversity of thought through the persistent biases of static AI models.
AI Facial Recognition Error Jails Innocent Woman for Six Months
261
An innocent grandmother lost her home and car after being wrongfully jailed for six months due to a facial recognition error by Fargo police.
The Dehumanizing Rise of the AI Job Interview
418
AI job interviews are a dehumanizing trend that prioritizes corporate efficiency while stripping candidates of the ability to evaluate their potential employers.
Claude Code’s Opinionated Tech Stack: DIY Over Buy
611
Claude Code favors a modern, developer-centric tech stack that prioritizes custom DIY solutions and specialized platforms over legacy enterprise tools and traditional cloud providers.
LLMs Aren’t Ideologically Neutral: A Black‑Box A/B Test Across Top Models
LLMs display distinct ideological leanings, so which model you choose can shape the guidance you get on political and social questions.
Wikimedia’s HRIA: Ensuring AI Strengthens Human-Centered Free Knowledge
122
Wikimedia is proactively charting how AI/ML affects human rights and will partner with communities to ensure AI strengthens, not undermines, human-driven free knowledge.
AI’s WEIRD Lens: US-Centric LLMs and How to Protect Cultural Insight
217
AI looks human only if your human is WEIRD and American, so researchers must actively protect cultural meaning when using LLMs.
AI as a Grantmaking Filter: Promise, Pitfalls, and the Human Judgment Gap
AI is entering grantmaking as a large-scale screening tool that can speed and potentially democratize funding, but bias and confidentiality concerns mean it should augment—not replace—human reviewers.