This post explores how bias can creep into word embeddings like word2vec, and I thought it might make it more fun (for me, at least) if I analyze a model trained on what you, my readers (all three of you), might have written.
Often when we talk about bias in word embeddings, we are talking about such things as bias against race or sex. But I’m going to talk about bias a little bit more generally to explore attitudes we have that are manifest in the words we use about any number of topics.
Recent Updates
- Comment Period Closes on California OHCA’s Proposed Emergency Regulations Expanding Private Equity, Hedge Fund, and MSO Reporting in Health Care Transactions
- DOJ Revises Justice Manual on Non-Binding Guidance and Qui Tam Dismissals: Practical Considerations
- Additional SBA Crackdown on Pandemic-Era Fraud Leads to Program and Loan Suspensions, Possible FCA Enforcement
- Federal Regulatory Views on Cybersecurity and AI Amidst a Growing Threat Landscape
- Remote Monitoring Services Under the 2027 PFS Proposed Rule: Epstein Becker Green Submits Comments to CMS