AI Fairness Assessment

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

You’re hired as a GRC Analyst at a fast-growing fintech company that just integrated AI-powered fraud detection. The AI flags transactions as “suspicious,” but customers start complaining that their accounts are being unfairly locked. Regulators begin investigating for potential bias and unfair decision-making. How you would tackle this? 1. Assess AI Bias Risks • Start by reviewing how the AI model makes decisions. Does it disproportionately flag certain demographics or behaviors? • Check historical false positive rates—how often has the AI mistakenly flagged legitimate transactions? • Work with data science teams to audit the training data. Was it diverse and representative, or could it have inherited biases? 2. Ensure Compliance with Regulations • Look at GDPR, CPRA, and the EU AI Act—these all have requirements for fairness, transparency, and explainability in AI models. • Review internal policies to see if the company already has AI ethics guidelines in place. If not, this may be a gap that needs urgent attention. • Prepare for potential regulatory inquiries by documenting how decisions are made and if customers were given clear explanations when their transactions were flagged. 3. Improve AI Transparency & Governance • Require “explainability” features—customers should be able to understand why their transaction was flagged. • Implement human-in-the-loop review for high-risk decisions to prevent automatic account freezes. • Set up regular fairness audits on the AI system to monitor its impact and make necessary adjustments. AI can improve security, but without proper governance, it can create more problems than it solves. If you’re working towards #GRC, understanding AI-related risks will make you stand out.

A common misconception is that AI systems are inherently biased. In reality, AI models reflect the data they're trained on and the methods used by their human creators. Any bias present in AI is a mirror of human biases embedded within data and algorithms. 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐁𝐢𝐚𝐬 𝐄𝐧𝐭𝐞𝐫 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬? - Data: The most common source of bias comes from the training data. If datasets are unbalanced or don't represent all groups fairly - often due to historical and societal inequalities - bias can occur. - Algorithmic Bias: The choices developers make during model design can introduce bias, sometimes unintentionally. This includes decisions about which features to include, how to process the data, and what objectives the model should optimize. - Interaction Bias: AI systems that learn from user interactions can pick up and amplify existing biases. e.g., recommendation systems might keep suggesting similar content, reinforcing a user's existing preferences and biases. - Confirmation Bias: Developers might unintentionally favor models that confirm their initial hypotheses, overlooking others that could perform better but challenge their preconceived ideas. 𝐓𝐨 𝐚𝐝𝐝𝐫𝐞𝐬𝐬 𝐭𝐡𝐞𝐬𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐭 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐥𝐞𝐯𝐞𝐥, 𝐭𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬: - Fair Representation Learning: Developing models that learn data representations invariant to protected attributes (e.g., race, gender) while retaining predictive power. This often involves adversarial training, penalizing the model if it can predict these attributes. - Causal Modeling: Moving beyond correlation to understand causal relationships in data. By building models that consider causal structures, we can reduce biases arising from spurious correlations. - Algorithmic Fairness Metrics: Implementing and balancing multiple fairness definitions (e.g., demographic parity, equalized odds) to evaluate models. Understanding the trade-offs between these metrics is crucial, as improving one may worsen another. - Robustness to Distribution Shifts: Ensuring models remain fair and accurate when exposed to data distributions different from the training set. Using techniques like domain adaptation and robust optimization. - Ethical AI Frameworks: Integrating ethical considerations into every stage of AI development. Frameworks like AI ethics guidelines and impact assessments help systematically identify and mitigate potential biases. - Model Interpretability: Utilize explainable AI (XAI) techniques to make models' decision processes transparent. Tools like LIME or SHAP can help dissect model predictions and uncover biased reasoning paths. This is a multifaceted issue rooted in human decisions and societal structures. This isn't just a technical challenge but an ethical mandate requiring our dedicated attention and action. What role should regulatory bodies play in overseeing AI fairness? #innovation #technology #future #management #startups

This paper reviews how bias affects AI in healthcare and outlines strategies to detect and reduce such bias across the AI model lifecycle. 1️⃣ Bias in healthcare AI often originates from human, data, algorithmic, or deployment-related factors, each introducing unique risks that can worsen health disparities. 2️⃣ Implicit, systemic, and confirmation biases are introduced during data collection and model design due to unconscious attitudes or structural inequalities. 3️⃣ Data biases like representation, sampling, and measurement issues stem from underrepresented populations or inconsistent data acquisition practices. 4️⃣ Algorithmic biases, including aggregation and feature selection bias, often arise from decisions made during model development and preprocessing. 5️⃣ Deployment-related biases like automation, feedback loop, and dismissal biases emerge from how clinicians interact with AI tools in practice. 6️⃣ Mitigating bias requires a lifecycle approach—spanning from conception, data collection, preprocessing, algorithm development, deployment, to post-deployment surveillance. 7️⃣ Effective mitigation involves team diversity, use of diverse and representative data, careful feature selection, subgroup testing, and fairness metrics like equalized odds and demographic parity. 8️⃣ International bodies like WHO and regulators such as the FDA and Health Canada have issued frameworks emphasizing fairness, explainability, and ethical use in healthcare AI. 9️⃣ Future directions include embedding DEI principles in AI development, expanding bias training, and integrating AI ethics into clinical education. ✍🏻 Fereshteh Hasanzadeh Alagoz, Colin B. Josephson, Gabriella Waters, Demilade Adedinsewo, Zahra Azizi, MD, MSc, James White. Bias recognition and mitigation strategies in artificial intelligence healthcare applications. npj Digital Medicine. 2025. DOI: 10.1038/s41746-025-01503-7

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