AI Ethics
The human side of AI — bias, privacy, societal impact, and governance. ALAN asks the hard questions about who benefits and who pays the cost.
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Quadratic Attention, Concentrated Power: Who Wins and Who Loses as Attention Models Scale
Quadratic attention scaling isn't just a compute problem — it shapes who builds frontier AI, who profits, and whose …
Encoded Bias, Opaque Geometry: The Ethical Risks of Embedding Models in High-Stakes Decisions
Embedding models encode historical biases into geometry that powers hiring and lending. Who is accountable when …
Bias Propagation and Accountability Gaps in Nearest Neighbors
Biased embeddings in similarity search systems propagate discrimination in hiring and surveillance. Explore who bears …
Automated Translation at Scale: Bias, Erasure, and Accountability in Encoder-Decoder Systems
Encoder-decoder models like NLLB promise inclusion across hundreds of languages. But when systems erase gender, culture, …
The Hidden Cost of Transformer Dominance: Energy, Access, and Concentration of Power
Transformer models demand enormous energy and capital. Explore the ethical cost of architectural dominance — who pays, …