ColonScopeX: Leveraging Explainable Expert Systems with Multimodal Data for Improved Early Diagnosis of Colorectal Cancer

Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths and the third most prevalent malignant tumour worldwide. Early detection of CRC remains problematic due to its non-specific and often embarrassing symptoms, which patients frequently overlook or hesitate to report to clinicians. Crucially, the stage at which CRC is diagnosed significantly impacts survivability, with a survival rate of 80-95\% for Stage I and a stark decline to 10\% for Stage IV. Unfortunately, in the UK, only 14.4\% of cases are diagnosed at the earliest stage (Stage I). In this study, we propose ColonScopeX, a machine learning framework utilizing explainable AI (XAI) methodologies to enhance the early detection of CRC and pre-cancerous lesions. Our approach employs a multimodal model that integrates signals from blood sample measurements, processed using the Savitzky-Golay algorithm for fingerprint smoothing, alongside comprehensive patient metadata, including medication history, comorbidities, age, weight, and BMI. By leveraging XAI techniques, we aim to render the model's decision-making process transparent and interpretable, thereby fostering greater trust and understanding in its predictions. The proposed framework could be utilised as a triage tool or a screening tool of the general population. This research highlights the potential of combining diverse patient data sources and explainable machine learning to tackle critical challenges in medical diagnostics.

Measuring Fairness in Financial Transaction Machine Learning Models

Mastercard, a global leader in financial services, develops and deploys machine learning models aimed at optimizing card usage and preventing attrition through advanced predictive models. These models use aggregated and anonymized card usage patterns, including cross-border transactions and industry-specific spending, to tailor bank offerings and maximize revenue opportunities. Mastercard has established an AI Governance program, based on its Data and Tech Responsibility Principles, to evaluate any built and bought AI for efficacy, fairness, and transparency. As part of this effort, Mastercard has sought expertise from the Turing Institute through a Data Study Group to better assess fairness in more complex AI/ML models. The Data Study Group challenge lies in defining, measuring, and mitigating fairness in these predictions, which can be complex due to the various interpretations of fairness, gaps in the research literature, and ML-operations challenges.