Model robustness
When do models become unreliable?
Investigating how changing conditions expose failures that aggregate scores can miss.
Related workOur research connects model behaviour, data quality, and evaluation methods. We start with the conditions under which a system can fail, then ask what evidence would reveal it.
Questions we investigate
Data quality
What does the data leave out?
Examining missing examples, inconsistent records, and the relationship between data and model behaviour.
Related workEvaluation & assurance
What evidence supports a deployment decision?
Connecting test criteria, observed outcomes, and risk records so that decisions can be examined.
Related workLab notes
Original observations and attributed reading from the wider field of AI evaluation.
Reading note
Stanford HAI AI Index 2026: What Executives Should Track
Stanford HAI's 2026 AI Index is a core research reference for AI capability, adoption, investment, policy, and governance trends.
Source: hai.stanford.edu ↗
AI Governance
Reading note
EU AI Act Regulatory Framework: 2026 Official Reference
The European Commission's AI Act policy hub remains a primary official reference for risk-based AI obligations and enterprise governance planning in 2026.
Source: digital-strategy.ec.europa.eu ↗
AI Governance
Reading note
MIT AI Risk Repository: A Practical Taxonomy for Risk Registers
MIT's AI Risk Repository helps governance teams sort AI risks into categories that work for registers, controls, and executive review.
Source: airisk.mit.edu ↗
AI Risk Management
Reading note
European AI Office: Governance Reference for AI Act Implementation
The European AI Office provides the official EU coordination point for AI Act implementation, general-purpose AI oversight, and governance guidance.
Source: digital-strategy.ec.europa.eu ↗
AI Governance