5 citations · 6 across the 7 of their papers we have counts for
8 papers
Latent space bias directions in LLMs capture confidence, not fairness
Stephanie Buttigieg, Maeve Madigan, Parameswaran Kamalaruban +1
Activation steering has gained popularity as a lightweight inference-time debiasing technique for large language models. However, prior work reports that steering vectors generalis…
MINT: A Universal Zero-Shot Predictor for Transaction Data
Parameswaran Kamalaruban, Viktor Drobnyi, Maeve Madigan +3
Banks analyse sequential financial transaction data to perform many tasks, including fraud prevention, credit risk assessment and offer personalization. To improve the predictive a…
Log-Likelihood, Simpson's Paradox, and the Detection of Machine-Generated Text
Tom Kempton, Viktor Drobnyi, Maeve Madigan +1
The ability to reliably distinguish human-written text from that generated by large language models is of profound societal importance. The dominant approach to this problem exploi…
Emergent Bias and Fairness in Multi-Agent Decision Systems
Maeve Madigan, Parameswaran Kamalaruban, Glenn Moynihan +3
Multi-agent systems have demonstrated the ability to improve performance on a variety of predictive tasks by leveraging collaborative decision making. However, the lack of effectiv…
Fairness-Aware Low-Rank Representation Fine-Tuning
Parameswaran Kamalaruban, Mark Anderson, Stuart Burrell +3
Pre-trained foundation models can be efficiently adapted for specific tasks using Low-Rank Adaptation (LoRA), but the fairness properties of these adapted classifiers remain undere…
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges
Parameswaran Kamalaruban, Yulu Pi, Stuart Burrell +4
Ensuring fairness in transaction fraud detection models is vital due to the potential harms and legal implications of biased decision-making. Despite extensive research on algorith…
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
Piotr Skalski, David Sutton, Stuart Burrell +2
Machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineere…
Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems
Iker Perez, Jason Wong, Piotr Skalski +4
Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in…