Case Studies
A selection of engagements where MIRAI’s methodology has been applied to real systems in production. Each case study includes the context, the analysis performed, and the results obtained.
Enhancing Correctness, Fairness and Coherence in AI Chatbots
How can organisations ensure that AI-powered chatbots deliver correct, fair, and coherent information to employees? This question is at the heart of a recent assessment, conducted with the proprietary MIRAI Toolbox on a chatbot internally deployed within the HR Office of a large financial Institution. MIRAI was asked to perform [...]
Enhancing Fairness in Resignation Prediction with BRIO
At MIRAI, we believe that fairness in AI is a shared responsibility. In our latest challenge with a partner in digital development, we applied our proprietary BRIO framework to evaluate the fairness of a machine learning model designed to predict employee resignations. The Challenge The task: assess whether the model's [...]
Enhancing Fairness in Fraud Detection with BRIO
At MIRAI, we believe that fairness in AI is a shared responsibility. In our latest challenge with a partner in the insurance sector, we applied our proprietary BRIO framework to evaluate the fairness of a fraud prediction algorithm used to triage insurance claims for potential fraud. The Challenge The [...]
Enhancing Fairness in Credit Scoring with BRIO
At MIRAI, we're championing responsible AI. Our BRIO tool offers a model‑agnostic approach to detect bias and assess unfairness risk in AI systems. In this case study, we explore how BRIO operates in the critical domain of credit scoring. The full study is published here and here. 1. Why [...]
