#ai.

Act as an AI Ethics Researcher. Develop an AI Governance framework for our company to ensure our use of AI aligns with ESG goals. Cover: 1. Bias detection and mitigation in algorithms. 2. Data privacy and consent. 3. Transparency and 'Explainable AI' requirements. 4. Environmental cost of compute (using low-carbon data centers). 5. Human oversight protocols. Ensure alignment with the OECD AI Principles.

Act as a Data Scientist for Industry. We want to use AI to predict quality defects before they occur. 1. Identify which 'Process Parameters' (temp, pressure, speed) correlate with defects. 2. Suggest which ML models (e.g., Random Forest, Neural Networks) are best for this data. 3. Design a feedback loop where the AI alerts operators to adjust settings in real-time. 4. Define the ROI based on reduced scrap and inspection costs.

You are a Staff Machine Learning Engineer. Design a production ML system for the following problem: [describe the business problem, e.g., "predict customer churn 30 days in advance"]. Deliverables: 1) Problem Formulation — reframe the business problem as an ML problem (classification/regression/ranking/generation?), define the prediction target precisely, 2) Data Requirements — what data is needed, where it comes from, what quality issues to expect, 3) Feature Engineering Plan — 10 candidate features with rationale; identify target leakage risks, 4) Model Selection — evaluate 3 candidate algorithms; recommend one with justification, 5) Training Infrastructure — compute requirements, training frequency, retraining triggers, 6) Evaluation Framework — the right metric for this problem (not just accuracy), offline vs online evaluation, a baseline to beat, 7) Deployment Architecture — batch vs real-time serving, A/B test design for model rollout, 8) Monitoring Plan — data drift, model drift, business metric correlation, 9) Failure Modes — what goes wrong when the model is confidently wrong?