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.
No publications found.