Ensuring Fair Representation In AI Training Data

The guide "AI Fairness in Practice" by The Alan Turing Institute from 2023 covers the concept of fairness in AI/ML contexts. The fairness paper is part of the AI Ethics and Governance in Practice Program (link: https://lnkd.in/gvYRma_R). The paper dives deep into various types of fairness: DATA FAIRNESS includes: - representativeness of data samples, - collaboration for fit-for-purpose and sufficient data quantity, - maintaining source integrity and measurement accuracy, - scrutinizing timeliness, and - relevance, appropriateness, and domain knowledge in data selection and utilization. APPLICATION FAIRNESS involves considering equity at various stages of AI project development, including examining real-world contexts, addressing equity issues in targeted groups, and recognizing how AI model outputs may shape decision outcomes. MODEL DESIGN AND DEVELOPMENT FAIRNESS involves ensuring fairness at all stages of the AI project workflow by - scrutinizing potential biases in outcome variables and proxies during problem formulation, - conducting fairness-aware design in preprocessing and feature engineering, - paying attention to interpretability and performance across demographic groups in model selection and training, - addressing fairness concerns in model testing and validation, - implementing procedural fairness for consistent application of rules and procedures. METRIC-BASED FAIRNESS utilizes mathematical mechanisms to ensure fair distribution of outcomes and error rates among demographic groups, including: - Demographic/Statistical Parity: Equal benefits among groups. - Equalized Odds: Equal error rates across groups. - True Positive Rate Parity: Equal accuracy between population subgroups. - Positive Predictive Value Parity: Equal precision rates across groups. - Individual Fairness: Similar treatment for similar individuals. - Counterfactual Fairness: Consistency in decisions. The paper further covers SYSTEM IMPLEMENTATION FAIRNESS, incl. Decision-Automation Bias (Overreliance and Overcompliance), Automation-Distrust Bias, contextual considerations for impacted individuals, and ECOSYSTEM FAIRNESS. -- Appendix A (p 75) lists Algorithmic Fairness Techniques throughout the AI/ML Lifecycle, e.g.: - Preprocessing and Feature Engineering: Balancing dataset distributions across groups. - Model Selection and Training: Penalizing information shared between attributes and predictions. - Model Testing and Validation: Enforcing matching false positive/negative rates. - System Implementation: Allowing accuracy-fairness trade-offs. - Post-Implementation Monitoring: Preventing model reliance on sensitive attributes. -- The paper also includes templates for Bias Self-Assessment, Bias Risk Management, and a Fairness Position Statement. -- Link to authors/paper: https://lnkd.in/gczppH29 #AI #Bias #AIfairness

AI Is Misdiagnosing Millions—And No One's Talking Some patients are twice as likely to be misdiagnosed by AI. Why? The data that fuels it. In 2025, we’re seeing AI tools gain speed in healthcare. Faster triage. Faster decisions. Faster outcomes. But speed means nothing when it’s not fair. White patients are getting more accurate AI diagnoses. Black patients, Latino patients, Indigenous patients—less so. Why? Because systems are often trained on datasets that ignore demographic diversity. Because “representative” data is treated as an afterthought. Because fairness isn’t baked into the build—it’s patched in after launch. And for operations leaders pushing AI across the enterprise, this matters. Bias doesn’t just hurt ethics—it breaks performance. It leads to costly diagnostic errors. Regulatory exposure. Reputational risk. Fixing this starts with: • Training AI on inclusive, representative datasets • Stress-testing models across all populations • Demanding explainability from vendors—not just features • Making fairness a metric, not a footnote Healthcare transformation depends on trust. Without equity, there is no trust. Without trust, AI fails. If you're scaling AI in regulated environments, how are you building fairness into your rollout plans? CellStrat #CellBot #HealthcareAI