Explainability

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Why health AI interfaces must adapt to user expertise

ai, AI (Artificial Intelligence), AI in Action, Artificial Intelligence, bias, Computer vision, dermatology, diagnostic tools, Explainability, Features, Health, Healthcare, Healthcare & Wellness AI, Human-AI Relationships, interface, medical, MIT, natural language, Natural Language Processing (NLP), patient safety, Research, study, Trust, Bias & Fairness

MIT researchers and collaborators found that AI explainability tools in the health sector can produce sharply different results depending on who uses them. When applied to skin disease diagnosis, non-experts improved their accuracy with AI assistance, although the improvement largely came from deferring to the model. Primary care providers showed a different pattern: they performed […]

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Secure governance accelerates financial AI revenue growth

ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, AI in Action, AI Market Trends, Artificial Intelligence, Compliance, data lineage, Deep Dives, Explainability, Features, Finance, Finance AI, financial ai, governance, Governance, Regulation & Policy, Inside AI, MLOps, Opinion, regulation, Security, strategy, Trust, Bias & Fairness, World of Work

Financial institutions are learning to deploy compliant AI solutions for greater revenue growth and market advantage. For the better part of ten years, financial institutions viewed AI primarily as a mechanism for pure efficiency gains. During that era, quantitative teams programmed systems designed to discover ledger discrepancies or eliminate milliseconds from automated trading execution times.

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Deep Learning With Keras To Predict Customer Churn

Artificial Intelligence, Explainability, Tabular Data, TensorFlow/Keras

Using Keras to predict customer churn based on the IBM Watson Telco Customer Churn dataset. We also demonstrate using the lime package to help explain which features drive individual model predictions. In addition, we use three new packages to assist with Machine Learning: recipes for preprocessing, rsample for sampling data and yardstick for model metrics.

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