Data Trustworthiness and Bias Checks

My name is Alan and I have a LLM. I want to understand bias. Then mitigate it. Maybe even eliminate it. Here’s the reality: bias in AI isn’t just a technical flaw. It’s a reflection of the world your data comes from. There are different types: - Historical bias comes from the inequalities already present in society. If the past was unfair, your model will be too. - Sampling bias happens when your dataset doesn’t reflect the full population. Some voices get left out. - Label bias creeps in when human annotators bring their assumptions to the task. - Measurement bias arises when we use poor proxies for real-world traits, like using postcodes as a stand-in for income. - Feedback loop bias shows up when algorithms reinforce patterns they’ve already learned, especially in recommender systems or policing models. You won’t fix this with good intentions. You need process. 1. Explore your dataset Use tools like pandas-profiling, datasist, or WhyLabs to audit your data. Look at the distribution of features. Where are the gaps? Who’s overrepresented? Are protected groups like gender, race or age present and balanced? 2. Diagnose the bias Use fairness toolkits like Fairlearn, AIF360, or the What-If Tool to test how your model behaves across different groups. Common metrics include: - Demographic parity (same outcomes across groups) - Equalised odds (same true and false positive rates) - Predictive parity (equal accuracy) - Disparate impact ratio (used in employment law) There’s no one perfect measure. Fairness depends on the context and the stakes. 3. Apply mitigation strategies Pre-processing: Rebalance datasets, remove proxies, use reweighting or SMOTE. In-processing: Train with fairness constraints or use adversarial debiasing. Post-processing: Adjust decision thresholds to reduce group-level disparities. Each approach has pros and cons. You’ll often trade a little performance for a lot of fairness. 4. Validate and track Don’t just run once and forget. Track metrics over time. Retrain with care. Bias can creep back in with new data or changes to user behaviour. 5. Document your decisions Create a clear audit trail. Record what you tested, what you found, what you changed, and why. This becomes your defensible position. Regulators, auditors, and users will want to know what steps you took. Saying “we didn’t know” won’t be good enough. The legal landscape is catching up. The EU AI Act names bias mitigation as a mandatory control for high-risk systems like credit scoring, hiring, and facial recognition. And emerging global standards like ISO 23894 and IEEE 7003 are pushing for fairness assessments and bias impact documentation. So, can I eliminate bias completely? No. Not in a complex world with incomplete data. But I can reduce harm. I can bake fairness into design. And I can stay accountable. Because bias in AI isn’t theoretical. It affects lives. #AIBias #FairnessInAI #ResponsibleAI #AIandLaw #GovernanceMatters

𝗪𝗲 𝘁𝗿𝘂𝘀𝘁 𝗼𝘂𝗿 𝗠𝗜𝗦 𝗿𝗲𝗽𝗼𝗿𝘁𝘀. After all, they’re “𝘥𝘢𝘵𝘢-𝘥𝘳𝘪𝘷𝘦𝘯,” right? But here’s the uncomfortable truth: 𝗘𝘅𝗰𝗲𝗹 𝘀𝗵𝗲𝗲𝘁𝘀 𝗰𝗮𝗻 𝗯𝗲 𝗯𝗶𝗮𝘀𝗲𝗱... 𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝘀𝘂𝗯𝘁𝗹𝘆, 𝘀𝗼𝗺𝗲𝘁𝗶𝗺𝗲𝘀 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀𝗹𝘆. ➡️ 𝘚𝘦𝘭𝘦𝘤𝘵𝘪𝘷𝘦 𝘒𝘗𝘐s: Highlighting only the “feel-good” metrics while ignoring the ones that reveal cracks. ➡️ 𝘊𝘩𝘦𝘳𝘳𝘺-𝘱𝘪𝘤𝘬𝘦𝘥 𝘵𝘪𝘮𝘦𝘧𝘳𝘢𝘮𝘦𝘴: A report may look stellar if you show Q2, but not so much if you include Q1. ➡️ 𝘏𝘪𝘥𝘥𝘦𝘯 𝘧𝘪𝘭𝘵𝘦𝘳𝘴 𝘪𝘯 𝘗𝘪𝘷𝘰𝘵𝘛𝘢𝘣𝘭𝘦𝘴: A simple unchecked box can completely skew the story your data is telling. These aren’t just harmless quirks. They can 𝗱𝗶𝘀𝘁𝗼𝗿𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗲𝗮𝗹𝗶𝘁𝘆, leading to wrong calls. Whether it’s approving budgets, launching products, or restructuring teams. So, how do we keep reports honest? ✅ Cross-check KPIs against original objectives. ✅ Review multiple time horizons, not just the “best-looking” ones. ✅ Audit filters and slicers before presenting. ✅ Encourage a culture where bad news is not buried, but acted upon. Because at the end of the day, data doesn’t lie... but reporting can. The real question is: 𝘈𝘳𝘦 𝘸𝘦 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘤𝘰𝘯𝘧𝘳𝘰𝘯𝘵 𝘵𝘩𝘦 𝘸𝘩𝘰𝘭𝘦 𝘵𝘳𝘶𝘵𝘩? 👉 𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙚𝙣𝙨𝙪𝙧𝙚 𝙣𝙚𝙪𝙩𝙧𝙖𝙡𝙞𝙩𝙮 𝙖𝙣𝙙 𝙩𝙧𝙖𝙣𝙨𝙥𝙖𝙧𝙚𝙣𝙘𝙮 𝙞𝙣 𝙩𝙝𝙚 𝙧𝙚𝙥𝙤𝙧𝙩𝙨 𝙮𝙤𝙪 𝙬𝙤𝙧𝙠 𝙬𝙞𝙩𝙝? #DataDrivenDecisionMaking #DataAnalytics #ExcelReports #DataTransparency #MISReporting

Is healthcare okay with exacerbating social inequalities? Or ignoring (known) mistakes for the sake of efficiency? Using AI in healthcare could advance healthcare, but are we risking safety? Health AI tools could be a silent epidemic, potentially affecting millions of patients worldwide. Bias can exacerbate social inequalities, and could influence who gets what treatment at what time. These tools, if left unchecked, could exacerbate existing health disparities and lead to misdiagnoses, inappropriate treatments, and worsened outcomes for certain groups. Here are 8 biases to be aware of, how to detect them, and what to do to mitigate them.  1) Selection bias:  Compare characteristics of included vs. excluded participants in AI-based screening. Use inclusive recruitment strategies and adjust selection criteria to ensure diverse representation. 2) Data bias: Analyze demographic distributions in training data compared to target population. Actively collect diverse, representative data and use techniques like stratified sampling or data augmentation. 3) Algorithmic bias: Evaluate model performance across different subgroups using fairness metrics. Implement fairness constraints in model design and use debiasing techniques during training. 4) Historical bias: Analyze historical trends in the data. Compare predictions to known historical disparities. Adjust historical data to correct for known biases. Incorporate domain knowledge to identify and address historical inequities. 5) Interpretation bias: Conduct audits of human-AI interactions. Analyze discrepancies between AI recommendations and human decisions. Provide bias awareness training for healthcare professionals. Implement decision support tools that highlight potential biases. Use explainable AI for increased transparency. 6) Racial bias: Compare model performance (accuracy and error rates) across different racial groups. Evaluate if model requires certain patients to be sicker to receive same level of care. Ensure diverse and representative training data. Implement fairness constraints in the algorithm. Engage with diverse stakeholders during AI lifecycle. 7) Gender bias: Assess model accuracy for male vs. female patients. Analyze if the model systematically under diagnoses or misclassified conditions in one gender. 8) Socioeconomic bias: Evaluate model performance across different socioeconomic status groups. Analyze if the model predicts health outcomes based on cost of care rather than actual health needs. Use diverse datasets including various socioeconomic groups. Implement fairness metrics accounting for disparities. Avoid using proxies for ehealth that may be influenced by status (e.g. healthcare costs). So, instead of blindly embracing AI in healthcare, we need to prioritize fairness and inclusivity in its development and implementation. What do you think about the steps your organization is taking to mitigate bias in Health AI tools?

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

🚨 New Publication Alert 🚨 Proud to share our team’s latest work in Journal of Medical Internet Research: “Detecting, Characterizing, and Mitigating Implicit and Explicit Biases in Health Care Datasets With Subgroup Learnability” As AI becomes deeply embedded in health care, one of the greatest challenges is ensuring that algorithms truly serve all patients. While most approaches focus on tweaking models after training, our study introduces AEquity led by Faris G. — a simple, data-centric metric that tackles the problem at its root: the data itself. 🔑 Why it matters: Data-first fairness: Instead of waiting until deployment, AEquity helps detect bias early in the pipeline. Broad applicability: Works across architectures — from ResNets to Transformers to gradient-boosted trees. Proven impact: In chest X-rays, health care cost prediction, and NHANES mortality data, AEquity-guided interventions cut performance gaps by up to 96%. Scalable & efficient: Requires <10% of data to identify where imbalances occur and guides smarter data collection. This is about building trustworthy AI systems that regulators, clinicians, and patients can rely on. The ability to characterize and mitigate bias at the dataset level has the potential to become as standard as sample size calculation in clinical research. Excited to see how AEquity can be applied across clinical domains, and grateful to my co-authors. Ashwin Sawant Lora L. Carol Horowitz, MD, MPH Lili Chan Ira Hofer, MD Karandeep Singh Lynne D. Richardson MD Emmanuel Mensah Alexander Charney Jianying Hu and David Reich Full paper here: https://lnkd.in/gzk8K2kH Press Release Here: https://lnkd.in/gVTqFHvE #AI #MachineLearning #HealthcareInnovation #DataScience #ClinicalAI #MedicalResearch

You’re working in People Ops at a mid-size tech company. You just rolled out a new AI-based performance review platform. It uses sentiment analysis, peer feedback, and productivity scores to help managers assess employee performance more “objectively.” But things take a turn. An employee files a complaint claiming the AI-generated feedback was biased and possibly discriminatory. They say the model flagged their performance inaccurately and they’re concerned it may be tied to race or gender. Your legal team is now involved, and leadership wants your help ensuring this doesn’t spiral. What’s your next move? First things guess, you’d freeze any further use of the AI review tool until an internal risk evaluation is done. Document the complaint, notify legal and your AI governance contact, and request logs or metadata from the tool to trace how the score was generated. Then, review the procurement and onboarding process of that AI tool. Was there a bias assessment done before rollout? Was HR trained on interpreting its outputs? If not, that’s a major gap in both governance and operational risk. Next, conduct a bias audit — either internally or with a third-party — to validate whether the tool is producing disparate impacts across protected groups. At the same time, inform your DPO (if applicable) to check if any personal or sensitive data was used beyond its intended scope. Lastly, you’d update internal policy: new tools affecting employment decisions must go through risk reviews, model documentation must be clear, and a human must always make final decisions with audit trails showing how they arrived there. #GRC

Data Readiness Isn’t Just About Tech, It’s About Trust Let’s get honest about something many organizations ignore: AI isn’t a tech project. It’s a trust project. If your data isn’t ready. If it’s biased, incomplete, or hidden behind silos. Your AI won’t just fail technically. It will fail socially. I’ve seen it happen: → Tools built without proper data checks end up excluding entire communities. → Leaders invest in automation that backfires because the data was outdated. → Public trust erodes when AI systems make unfair or unexplained decisions. Data readiness isn’t just about clean spreadsheets. It’s about protecting people and protecting your organization from preventable risks. Here’s what real data readiness looks like: - Data that's representative and verified - Ethics reviewed before deployment - Cross-functional teams aligned on use and accountability - Documentation that anyone can understand, not just the data team Before you build, pause and ask: Is our data trustworthy enough to scale this responsibly? Because without readiness, AI creates faster mistakes, not better solutions. Follow to learn more about Data Readiness for AI.

I’m obsessed with one truth: 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 is AI’s make-or-break. And it's not that simple to get right ⬇️ ⬇️ ⬇️ Gartner estimates an average organization pays $12.9M in annual losses due to low data quality. AI and Data Engineers know the stakes. Bad data wastes time, breaks trust, and kills potential. Thinking through and implementing a Data Quality Framework helps turn chaos into precision. Here’s why it’s non-negotiable and how to design one. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗿𝗶𝘃𝗲𝘀 𝗔𝗜 AI’s potential hinges on data integrity. Substandard data leads to flawed predictions, biased models, and eroded trust. ⚡️ Inaccurate data undermines AI, like a healthcare model misdiagnosing due to incomplete records.   ⚡️ Engineers lose their time with short-term fixes instead of driving innovation.   ⚡️ Missing or duplicated data fuels bias, damaging credibility and outcomes. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 A data quality framework ensures your data is AI-ready by defining standards, enforcing rigor, and sustaining reliability. Without it, you’re risking your money and time. Core dimensions:   💡 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Uniform data across systems, like standardized formats.   💡 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Data reflecting reality, like verified addresses.   💡 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Data adhering to rules, like positive quantities.   💡 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗻𝗲𝘀𝘀: No missing fields, like full transaction records.   💡 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀: Current data for real-time applications.   💡 𝗨𝗻𝗶𝗾𝘂𝗲𝗻𝗲𝘀𝘀: No duplicates to distort insights. It's not just a theoretical concept in a vacuum. It's a practical solution you can implement. For example, Databricks Data Quality Framework (link in the comments, kudos to the team Denny Lee Jules Damji Rahul Potharaju), for example, leverages these dimensions, using Delta Live Tables for automated checks (e.g., detecting null values) and Lakehouse Monitoring for real-time metrics. But any robust framework (custom or tool-based) must align with these principles to succeed. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲, 𝗕𝘂𝘁 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗜𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Automation accelerates, but human oversight ensures excellence. Tools can flag issues like missing fields or duplicates in real time, saving countless hours. Yet, automation alone isn’t enough—human input and oversight are critical. A framework without human accountability risks blind spots. 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ✅ Set standards, identify key dimensions for your AI (e.g., completeness for analytics). Define rules, like “no null customer IDs.”   ✅ Automate enforcement, embed checks in pipelines using tools.   ✅ Monitor continuously, track metrics like error rates with dashboards. Databricks’ Lakehouse Monitoring is one option, adapt to your stack.   ✅ Lead with oversight, assign a team to review metrics, refine rules, and ensure human judgment. #DataQuality #AI #DataEngineering #AIEngineering

Avoiding the Data Landmines Sabotaging Your AI Strategy AI strategies can quickly fail when organizations overlook the "hidden" data issues that undermine their effectiveness. These data landmines, like inconsistent datasets and unseen biases, can quietly sabotage AI’s potential, leading to poor decisions and lost opportunities. Recommendation 1: Perform Regular Data Audits: Implement periodic reviews of your data pipelines to identify hidden inconsistencies or bias that could affect AI performance. This allows you to address issues before they impact decision-making. Recommendation 2: Invest in Data Quality Automation Tools: Automate data cleansing and validation processes to ensure your datasets are consistently accurate and up-to-date. Real-time data quality checks help mitigate the risk of silent data errors in AI models.

Working with companies on their AI strategy across different industries, I get to see lots of common patterns… Data quality and availability: AI needs consistent, reliable inputs. In healthcare, for example, patient records often live in multiple EHR systems that don’t speak to each other—lab results here, imaging scans there, medication histories somewhere else. A predictive model can’t learn if half its data is missing or mislabeled. Similarly, in manufacturing, sensor data may come from old PLCs with proprietary formats, making it nearly impossible to combine vibration readings, temperature logs, and maintenance histories into one dataset. Without a unified data platform and a clear governance process, teams spend weeks just cleaning and standardizing fields—time they could have spent building models that actually work. Talent gap: Skilled AI professionals are hard to hire. When you can’t find enough in-house experts, decide: build or buy? Many organizations skip the learning curve by licensing vertical AI solutions—fraud detection engines for finance, demand-forecasting tools for retail—rather than trying to do it all themselves. Legacy systems: Modern AI tools often can’t plug into decade-old infrastructure. Whether it’s a mainframe transaction log or a factory’s outdated control system, integration work can eat up months. By the time the AI finally connects, its assumptions are already stale. Ethical concerns and bias: If your training data reflects past imbalances—biased loan approvals or skewed hiring pools—AI can repeat those mistakes at scale. In any sector where decisions affect real lives, unchecked bias means unfair outcomes, reputational damage, and possible legal headaches. Privacy and security: AI often needs sensitive information—patient scans, credit-card histories, or purchase patterns—to work. If that data isn’t anonymized and encrypted, a single breach can lead to fines, lawsuits, and lost trust. Industries under strict regulations must lock down access and track every data touchpoint. Scalability: A model that works in a small prototype often crashes under real-world demand. Recommendations that run smoothly for a few hundred users can grind to a halt during peak traffic. Route-optimization logic that handles a handful of delivery trucks can buckle when dozens more come online. Without cloud infrastructure that auto-scales, automated retraining, and clear MLOps pipelines, pilots stay pilots—and never deliver real impact. Ignore these six, and your AI spend becomes a lesson in frustration. Nail them—clean data, the right build-vs-buy decision, seamless integration, bias checks, airtight security, and a plan for scale—and you’ll turn AI from a buzzword into a repeatable growth engine.