Field notes
2,343 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
Click the web, then scroll to zoom
Every note is in the list below. The web of topics draws here once the page has loaded.
7 of 2,343 notes match.
- Question answered
- Source note
- Topic
- Shared notes
Notes on bias
7 notes
Epistemic Subordination: Generative AI and the Infrastructure of Knowledge
The paper discusses how Generative AI encodes the dominant culture's way of knowing into its architecture, creating an epistemic condition known as 'epistemic subordination'. This affects minority epistemologies and…
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
Researchers used Concept Activation Vectors (CAVs) to analyze the bias of two automatic L2 speaking assessment systems: a text-based BERT grader and a multimodal grader based on Whisper. The study shows that the…
Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation
The research investigates self-distillation (SD), a method used in reinforcement learning with verifiable rewards, where a teacher conditioned on privileged information supplies supervision to a student. The study…
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
Researchers evaluate the bias in pre-trained language models used for AI-based recruitment systems, focusing on gender leakage from CV data. They analyze nine models using a synthetic dataset and a multi-task…
When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
The study investigates the inconsistency in Language Model (LLM) as judge scores when different evaluators are used. It compares two methods of upgrading LLMs: scaling Qwen3 dense judges and moving across MiniMax APIs…
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
The researchers explore the impact of biased judges in self-evolving agents, focusing on skill retirement, a mechanism that helps maintain the quality of an agent's knowledge base by retiring poorly performing skills…
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
The authors propose a new approach to make Electronic Health Records Foundation Models (FEMRs) more transparent by identifying the most influential tokens in the model's predictions. This is done using a…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
