Importance of Fairness in Data Privacy

So much of the GDPR focus is on compliance, the fundamental GDPR principle of fairness is often overlooked. When we talk about GDPR, most people think of consent forms, cookie banners, how to respond to DSAR's, etc. While all of that is important, do you want to know what my guiding light is in most of my reviews? Fairness What does fairness mean in the context of privacy? It’s not just about following the letter of the law. It’s about doing what’s RIGHT for the people whose data we handle. Transparency: Be clear about how and why you're using someone’s data. No hidden agendas. Honesty: Collect only the data you need and use it in ways the individual would expect. Respect: Remember, behind every data point is a real person. Their privacy is not a privilege but a fundamental right. Fairness is the cornerstone of building trust. And honestly, trust is how you should be selling privacy. No one wants to buy a product they don't trust---even if that product is in compliance with privacy regulations.

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.

I wasn’t actively looking for this book, but it found me at just the right time. Fairness and Machine Learning: Limitations and Opportunities by Solon Barocas, @Moritz Hardt, and Arvind Narayanan is one of those rare books that forces you to pause and rethink everything about AI fairness. It doesn’t just outline the problem—it dives deep into why fairness in AI is so complex and how we can approach it in a more meaningful way. A few things that hit home for me: →Fairness isn’t just a technical problem; it’s a societal one. You can tweak a model all you want, but if the data reflects systemic inequalities, the results will too. → There’s a dangerous overreliance on statistical fixes. Just because a model achieves “parity” doesn’t mean it’s truly fair. Metrics alone can’t solve fairness. → Causality matters. AI models learn correlations, not truths, and that distinction makes all the difference in high-stakes decisions. → The legal system isn’t ready for AI-driven discrimination. The book explores how U.S. anti-discrimination laws fail to address algorithmic decision-making and why fairness cannot be purely a legal compliance exercise. So, how do we fix this? The book doesn’t offer one-size-fits-all solutions (because there aren’t any), but it does provide a roadmap: → Intervene at the data level, not just the model. Bias starts long before a model is trained—rethinking data collection and representation is crucial. → Move beyond statistical fairness metrics. The book highlights the limitations of simplistic fairness measures and advocates for context-specific fairness definitions. → Embed fairness in the entire ML pipeline. Instead of retrofitting fairness after deployment, it should be considered at every stage—from problem definition to evaluation. → Leverage causality, not just correlation. Understanding the why behind patterns in data is key to designing fairer models. → Rethink automation itself. Sometimes, the right answer isn’t a “fairer” algorithm—it’s questioning whether an automated system should be making a decision at all. Who should read this? 📌 AI practitioners who want to build responsible models 📌 Policymakers working on AI regulations 📌 Ethicists thinking beyond just numbers and metrics 📌 Anyone who’s ever asked, Is this AI system actually fair? This book challenges the idea that fairness can be reduced to an optimization problem and forces us to confront the uncomfortable reality that maybe some decisions shouldn’t be automated at all. Would love to hear your thoughts—have you read it? Or do you have other must-reads on AI fairness? 👇 ↧↧↧↧↧↧↧ Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for no-BS AI news, insights, and educational content!

✳ Bridging Ethics and Operations in AI Systems✳ Governance for AI systems needs to balance operational goals with ethical considerations. #ISO5339 and #ISO24368 provide practical tools for embedding ethics into the development and management of AI systems. ➡Connecting ISO5339 to Ethical Operations  ISO5339 offers detailed guidance for integrating ethical principles into AI workflows. It focuses on creating systems that are responsive to the people and communities they affect. 1. Engaging Stakeholders  Stakeholders impacted by AI systems often bring perspectives that developers may overlook. ISO5339 emphasizes working with users, affected communities, and industry partners to uncover potential risks and ensure systems are designed with real-world impact in mind. 2. Ensuring Transparency  AI systems must be explainable to maintain trust. ISO5339 recommends designing systems that can communicate how decisions are made in a way that non-technical users can understand. This is especially critical in areas where decisions directly affect lives, such as healthcare or hiring. 3. Evaluating Bias  Bias in AI systems often arises from incomplete data or unintended algorithmic behaviors. ISO5339 supports ongoing evaluations to identify and address these issues during development and deployment, reducing the likelihood of harm. ➡Expanding on Ethics with ISO24368  ISO24368 provides a broader view of the societal and ethical challenges of AI, offering additional guidance for long-term accountability and fairness. ✅Fairness: AI systems can unintentionally reinforce existing inequalities. ISO24368 emphasizes assessing decisions to prevent discriminatory impacts and to align outcomes with social expectations.  ✅Transparency: Systems that operate without clarity risk losing user trust. ISO24368 highlights the importance of creating processes where decision-making paths are fully traceable and understandable.  ✅Human Accountability: Decisions made by AI should remain subject to human review. ISO24368 stresses the need for mechanisms that allow organizations to take responsibility for outcomes and override decisions when necessary. ➡Applying These Standards in Practice  Ethical considerations cannot be separated from operational processes. ISO24368 encourages organizations to incorporate ethical reviews and risk assessments at each stage of the AI lifecycle. ISO5339 focuses on embedding these principles during system design, ensuring that ethics is part of both the foundation and the long-term management of AI systems. ➡Lessons from #EthicalMachines  In "Ethical Machines", Reid Blackman, Ph.D. highlights the importance of making ethics practical. He argues for actionable frameworks that ensure AI systems are designed to meet societal expectations and business goals. Blackman’s focus on stakeholder input, decision transparency, and accountability closely aligns with the goals of ISO5339 and ISO24368, providing a clear way forward for organizations.

🚨 Banks & Fintechs: AI fairness was high on the agenda at this week’s annual meeting of the Consumer Bankers Association. A few key takeaways: 1️⃣ OCC Acting Comptroller of the Currency Mike Hsu's entire speech to the CBA focused on fairness as a driver of innovation: The more a bank puts fairness at the center of its compliance programs, Hsu said, “the less it will need to look over its shoulder at its regulators and the more degrees of freedom it will have to innovate and create banking products.” 2️⃣ The FDIC is requiring ongoing monitoring of fair lending risks in the BaaS ecosystem: Several sponsor banks reported increased scrutiny of the fair lending risks arising from their fintech partnerships including by examiners requiring more frequent bias testing and remediation. These reports are consistent with recent FDIC consent orders requiring “ongoing monitoring” of fair lending risks by partner banks. This means fintechs are having much more demanded of them: ◾  Variables have to be vigorously vetted; ◾  Models have to be comprehensively tested; ◾  Complaints need to be promptly investigated and addressed. 3️⃣ With targeted marketing on the rise, regulators are investigating if digital advertising could be causing discrimination.  Questions include: ◾  Are the variables you’re using for marketing fair? ◾  How do you know that seemingly fair marketing variables aren’t serving as proxies? ◾  What are the fairness outcomes of your marketing decisions? ◾  Are some groups being targeted to the exclusion of others? If so, why? ◾  How well do you understand the data and models used by your marketing vendors? 4️⃣  Fighting Fraud while being fair is hard. Fraud levels are at all-time highs. Increasingly, financial institutions are struggling to control fraud in ways that don’t create fairness risks. A big bank was recently fined $25M for discriminating against Armenian credit card applicants when, in the course of fighting a fraud-ring, it stopped accepting applications from an entire neighborhood in California. In our work at FairPlay, we’ve observed that fraud scores can sometimes be computed using information about an applicant’s digital footprint. For communities with lower broadband access or smartphone penetration rates, this can mean fraud scores are missing at higher rates. As a result, applicants from those neighborhoods can be disproportionately denied by fraud screens. 5️⃣  Business justifications are not the saving grace they once were: Traditionally, disparities in decisions have been defended by creditors on the grounds of business necessity. Increasingly however, many lenders report being pushed to go further, with regulators asking questions like: “Is that disparity as narrow as it needs to be? Can you achieve your business goals with a fairer approach?” 

AI is shaping our future faster than ever... But biased AI creates real harm today. AI bias leads to unfair and costly mistakes. It erodes trust and reinforces discrimination. We need better AI design to fix this. How AI picks up bias - Biased data comes from flawed past records. - Algorithm design can embed hidden biases. - User feedback loops make bias worse over time. Real-world consequences - Hiring tools reject qualified candidates unfairly. - Facial recognition struggles with dark skin tones. - Loan approvals deny financing to certain groups. How we fix biased AI - Diverse datasets create fairer training models. - Regular audits help catch hidden bias. - Inclusive teams bring balanced perspectives. - Explainable AI makes decisions more transparent. Why fair AI is better for everyone - Equitable access ensures fairness for all. - Improved outcomes make AI more trustworthy. - Enhanced innovation leads to better solutions. AI should work for everyone, not just a few. Fixing bias builds a stronger and fairer world. Found this helpful? Follow Arturo Ferreira and repost.

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

What's a tough truth about mitigating bias in AI? Defining it! It’s one of biggest challenges in AI today. While many emphasise the importance of “preventing unfair bias”, we must ask: unfair and fair to whom? And by what, and whose, definition? Fairness isn’t a one-size-fits-all concept. It varies across cultures, contexts, and regulations, leading to potential conflicts in definitions. What’s considered fair in one country or community may not hold true in another. And what don't people like to admit about tackling unfair AI bias? That the truth is that we can't guarantee fairness in AI. As we navigate these complexities, it's crucial to recognise this rather than falsely advertise that we’ve “solved for fairness” or built a “fair AI” system. Instead, we must acknowledge that bias is widespread and work to ensure our systems are better than they would be without human oversight, and keep improving and testing on an ongoing basis. How do you define fairness in your AI initiatives?

As an investor in AI-powered hiring solutions, NYC's law mandating AI bias audits caught my attention. Here's why it matters for all of us in tech: While AI has revolutionized hiring by processing thousands of applications efficiently, we must acknowledge its potential dark side. My portfolio company has shown me firsthand how AI can streamline recruitment, but also taught me a crucial lesson: without proper oversight, AI can perpetuate and amplify existing biases. NYC's law requiring annual third-party bias audits is a step in the right direction, but it highlights a broader need across ALL AI applications. Think about it: 1. AI in lending decisions could discriminate based on historical patterns 2. Healthcare AI might provide different quality of care across demographics 3. AI-powered content recommendations could create echo chambers 4. Customer service AI could offer varying service levels based on profiles The implications? We need robust bias assessment frameworks not just for hiring, but across the AI ecosystem. I see a massive opportunity here: dedicated AI bias assessment services could become as fundamental as cybersecurity audits. To my fellow investors and entrepreneurs: this isn't just about compliance. It's about building AI that truly serves everyone. Companies that proactively address AI bias will win in the long run - both ethically and commercially. Would love to hear your thoughts: How is your organization ensuring AI fairness? What challenges are you facing in implementing bias controls? #ArtificialIntelligence #Ethics #Innovation #TechInvesting #AIBias #FutureTech Image speaks to how AI systems reflect our own biases while appearing objective, and the need for human oversight in maintaining fairness.

Should we really trust AI to manage our most sensitive healthcare data? It might sound cautious, but here’s why this question is critical: As AI becomes more involved in patient care, the potential risks—especially around privacy and bias—are growing. The stakes are incredibly high when it comes to safeguarding patient data and ensuring fair treatment. The reality? • Patient Privacy Risks – AI systems handle massive amounts of sensitive information. Without rigorous privacy measures, there’s a real risk of compromising patient trust. • Algorithmic Bias – With 80% of healthcare datasets lacking diversity, AI systems may unintentionally reinforce health disparities, leading to skewed outcomes for certain groups. • Diversity in Development – Engaging a range of perspectives ensures AI solutions reflect the needs of all populations, not just a select few. So, what’s the way forward? → Governance & Oversight – Regulatory frameworks must enforce ethical standards in healthcare AI. → Transparent Consent – Patients deserve to know how their data is used and stored. → Inclusive Data Practices – AI needs diverse, representative data to minimize bias and maximize fairness. The takeaway? AI in healthcare offers massive potential, but only if we draw ethical lines that protect privacy and promote inclusivity. Where do you think the line should be drawn? Let’s talk. 👇