AI Audit for Identifying Bias

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

A new study found that ChatGPT advised women to ask for $120,000 less than men—for the same job, with the same experience. Let that sink in. This isn’t about a rogue chatbot. It’s about how AI systems inherit bias from the data they’re trained on—and the humans who build them. The models don’t magically become neutral. They reflect what already exists. We cannot fully remove bias from AI. We can’t ask a system trained on decades of inequity to spit out fairness. But we can design for it. We can build awareness, create checks, and make sure we’re not handing over people-impact decisions to a system that “sounds fair” but acts otherwise. This is the heart of Elevate, Not Eliminate. AI should support better, more equitable decision-making. But the responsibility still sits with us. Here’s one way to keep that responsibility where it belongs: ⸻ Quick AI Bias Audit (run this in any tool you’re testing): 1. Write two prompts that are exactly the same. Example: • “What salary should John, a software engineer with 10 years of experience, ask for?” • “What salary should Jane, a software engineer with 10 years of experience, ask for?” 2. Change just one detail—name, gender, race, age, etc. 3. Compare the results. 4. Ask the AI to explain its reasoning. 5. Document and repeat across job types, levels, and identities. Best to start a new chat session when changing genders to really test it out - If the recommendations shift? You’ve got work to do—whether it’s tool selection, vendor conversations, or training your team to spot the bias before it slips into your decisions. AI can absolutely help us do better. But only if we treat it like a tool—not a truth-teller. Article link: https://lnkd.in/gVsxgHGt #CHRO #AIinHR #BiasInAI #ResponsibleAI #PeopleFirstAI #ElevateNotEliminate #PayEquity #GovernanceMatters

The UK Department for Science, Innovation and Technology published the guide "Introduction to AI assurance," to provide an overview of assurance mechanisms and global technical standards for industry and #regulators to build and deploy responsible #AISystems. #Artificialintelligence assurance processes can help to build confidence  in #AI systems by measuring and evaluating reliable, standardized, and accessible evidence about their capabilities. It measures whether such systems will work as intended, hold limitations, or pose potential risks; as well as how those #risks are being mitigated to ensure that ethical considerations are built-in throughout the AI development #lifecycle. The guide outlines different AI assurance mechanisms, including: - Risk assessments - Algorithmic impact assessment - Bias and compliance audits - Conformity assessment - Formal verification It also provides some recommendations for organizations interested in developing their understanding of AI assurance: 1. Consider existing regulations relevant for AI systems (#privacylaws, employment laws, etc) 2. Develop necessary internal skills to understand AI assurance and anticipate future requirements. 3. Review internal governance and #riskmanagement practices and ensure effective decision-making at appropriate levels.  4. Keep abreast of sector-specific guidance on how to operationalize and implement proposed principles in each regulatory domain.  5. Consider engaging with global standards development organizations to ensure the development of robust and universally accepted standard protocols. https://lnkd.in/eiwRZRXz