Solutions for Implementing Fair AI Practices

🧭Governing AI Ethics with ISO42001🧭 Many organizations treat AI ethics as a branding exercise, a list of principles with no operational enforcement. As Reid Blackman, Ph.D. argues in "Ethical Machines", without governance structures, ethical commitments are empty promises. For those who prefer to create something different, #ISO42001 provides a practical framework to ensure AI ethics is embedded in real-world decision-making. ➡️Building Ethical AI with ISO42001 1. Define AI Ethics as a Business Priority ISO42001 requires organizations to formalize AI governance (Clause 5.2). This means: 🔸Establishing an AI policy linked to business strategy and compliance. 🔸Assigning clear leadership roles for AI oversight (Clause A.3.2). 🔸Aligning AI governance with existing security and risk frameworks (Clause A.2.3). 👉Without defined governance structures, AI ethics remains a concept, not a practice. 2. Conduct AI Risk & Impact Assessments Ethical failures often stem from hidden risks: bias in training data, misaligned incentives, unintended consequences. ISO42001 mandates: 🔸AI Risk Assessments (#ISO23894, Clause 6.1.2): Identifying bias, drift, and security vulnerabilities. 🔸AI Impact Assessments (#ISO42005, Clause 6.1.4): Evaluating AI’s societal impact before deployment. 👉Ignoring these assessments leaves your organization reacting to ethical failures instead of preventing them. 3. Integrate Ethics Throughout the AI Lifecycle ISO42001 embeds ethics at every stage of AI development: 🔸Design: Define fairness, security, and explainability objectives (Clause A.6.1.2). 🔸Development: Apply bias mitigation and explainability tools (Clause A.7.4). 🔸Deployment: Establish oversight, audit trails, and human intervention mechanisms (Clause A.9.2). 👉Ethical AI is not a last-minute check, it must be integrated/operationalized from the start. 4. Enforce AI Accountability & Human Oversight AI failures occur when accountability is unclear. ISO42001 requires: 🔸Defined responsibility for AI decisions (Clause A.9.2). 🔸Incident response plans for AI failures (Clause A.10.4). 🔸Audit trails to ensure AI transparency (Clause A.5.5). 👉Your governance must answer: Who monitors bias? Who approves AI decisions? Without clear accountability, ethical risks will become systemic failures. 5. Continuously Audit & Improve AI Ethics Governance AI risks evolve. Static governance models fail. ISO42001 mandates: 🔸Internal AI audits to evaluate compliance (Clause 9.2). 🔸Management reviews to refine governance practices (Clause 10.1). 👉AI ethics isn’t a magic bullet, but a continuous process of risk assessment, policy updates, and oversight. ➡️ AI Ethics Requires Real Governance AI ethics only works if it’s enforceable. Use ISO42001 to: ✅Turn ethical principles into actionable governance. ✅Proactively assess AI risks instead of reacting to failures. ✅Ensure AI decisions are explainable, accountable, and human-centered.

"On Nov 6, the UK Department for Science, Innovation and Technology (DSIT) published a first draft version of its AI Management Essentials (AIME) self-assessment tool to support organizations in implementing responsible AI management practices. The consultation for AIME is open until Jan 29, 2025. Recognizing the challenge many businesses face in navigating the complex landscape of AI standards, DSIT created AIME to distill essential principles from key international frameworks, including ISO/IEC 42001, the NIST Risk Management Framework, and the EU AI Act. AIME provides a framework to: - Evaluate current practices by identifying areas that meet baseline expectations and pinpointing gaps. - Prioritize improvements by highlighting actions needed to align with widely accepted standards and principles. - Understand maturity levels by offering insights into how an organization's AI management systems compare to best practices. AIME's structure includes: - A self-assessment questionnaire - Sectional ratings to evaluate AI management health - Action points and improvement recommendations The tool is voluntary and doesn’t lead to certification. Rather, it builds a baseline for 3 areas of responsible AI governance - internal processes, risk management, and communication. It is intended for individuals familiar with organizational governance, such as CTOs or AI Ethics Officers. Example questions: 1) Internal Processes Do you maintain a complete record of all AI systems used and developed by your organization? Does your AI policy identify clear roles and responsibilities for AI management? 2) Fairness Do you have definitions of fairness for AI systems that impact individuals? Do you have mechanisms for detecting unfair outcomes? 3) Impact Assessment Do you have an impact assessment process to evaluate the effects of AI systems on individual rights, society and the environment? Do you communicate the potential impacts of your AI systems to users or customers? 4) Risk Management Do you conduct risk assessments for all AI systems used? Do you monitor your AI systems for errors and failures? Do you use risk assessment results to prioritize risk treatment actions? 5) Data Management Do you document the provenance and collection processes of data used for AI development? 6) Bias Mitigation Do you take steps to mitigate foreseeable harmful biases in AI training data? 7) Data Protection Do you implement security measures to protect data used or generated by AI systems? Do you routinely complete Data Protection Impact Assessments (DPIAs)? 8) Communication Do you have reporting mechanisms for employees and users to report AI system issues? Do you provide technical documentation to relevant stakeholders? This is a great initiative to consolidating responsible AI practices, and offering organizations a practical, globally interoperable tool to manage AI!" Very practical! Thanks to Katharina Koerner for summary, and for sharing!

Part 1: The Algorithm Decided and Now You’re in Court Problem → Purpose → Solution Problem: It’s 2030. Your district just got sued by a student-led AI Ethics Council. The claims? No transparency in algorithmic grading Student data used to train third-party models without consent No way to appeal decisions made by machines Purpose: To avoid repeating these mistakes or ending up in court, the goal is to design policies with communities, not around them. Because trust, transparency, and protection shouldn’t come after the fact. They should be built into the blueprint from the start. Solution: To build trust and avoid breakdowns, we need more than reactive policies — we need real systems of care and clarity: ✅ Make grading explainable. If a machine is involved, students and families deserve to know how and why decisions were made. ✅ Protect student data like it matters, because it does. That means clear boundaries, written consent, and no quiet handoffs to vendors. ✅ Build in human pause points. AI should support teachers, not silently override them. Humans stay in the loop... always. ✅ Include students and communities early. If AI touches learning, equity, or identity, those impacted need a seat at the table, not just a summary after the fact. This kind of system doesn’t build itself. It takes purpose, planning, and yes, support. #FutureOfEducation #EthicalAI #BrightMindsAI #BuiltWithPurpose #StudentData #AIEthics #AIinSchools #FutureReady #EducationalLeadership #Teachers

The California AG issues a useful legal advisory notice on complying with existing and new laws in the state when developing and using AI systems. Here are my thoughts. 👇 📢 𝐅𝐚𝐯𝐨𝐫𝐢𝐭𝐞 𝐐𝐮𝐨𝐭𝐞 ---- “Consumers must have visibility into when and how AI systems are used to impact their lives and whether and how their information is being used to develop and train systems. Developers and entities that use AI, including businesses, nonprofits, and government, must ensure that AI systems are tested and validated, and that they are audited as appropriate to ensure that their use is safe, ethical, and lawful, and reduces, rather than replicates or exaggerates, human error and biases.” There are a lot of great details in this, but here are my takeaways regarding what developers of AI systems in California should do: ⬜ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲: Clearly disclose when AI is involved in decisions affecting consumers and explain how data is used, especially for training models. ⬜ 𝐓𝐞𝐬𝐭 & 𝐀𝐮𝐝𝐢𝐭 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Regularly validate AI for fairness, accuracy, and compliance with civil rights, consumer protection, and privacy laws. ⬜ 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐁𝐢𝐚𝐬 𝐑𝐢𝐬𝐤𝐬: Implement thorough bias testing to ensure AI does not perpetuate discrimination in areas like hiring, lending, and housing. ⬜ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Establish policies and oversight frameworks to mitigate risks and document compliance with California’s regulatory requirements. ⬜ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐇𝐢𝐠𝐡-𝐑𝐢𝐬𝐤 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: Pay special attention to AI used in employment, healthcare, credit scoring, education, and advertising to minimize legal exposure and harm. 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐞𝐞𝐭𝐢𝐧𝐠 𝐥𝐞𝐠𝐚𝐥 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬—it’s about building trust in AI systems. California’s proactive stance on AI regulation underscores the need for robust assurance practices to align AI systems with ethical and legal standards... at least this is my take as an AI assurance practitioner :) #ai #aiaudit #compliance Khoa Lam, Borhane Blili-Hamelin, PhD, Jeffery Recker, Bryan Ilg, Navrina Singh, Patrick Sullivan, Dr. Cari Miller

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

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!

New paper out! A case study: Duolingo’s AI ethics approach and implementation. This is a rare example of real-world, detailed AI ethics implementation. ➤ Context: * There are so many AI ethics frameworks out there. Most of them are high level, abstract, and far from implementation. * That’s why I wanted to co-author this paper. * It showcases how an organization can write practical AI ethics principles and then implement them. * The case study is Duolingo English Test My fabulous co-authors are Jill Burstein, who led the paper, and Alina von Davier, Geoff LaFlair, and Kevin Yancey, all parts of Duolingo’s English Test team. ➤ The AI ethics principles: 1. Validity and reliability 2. Fairness 3. Privacy 4. Transparency and accountability ➤  The implementation The paper demonstrates how these principles are implemented using several examples: * A six-step process for writing exam questions, illustrating the validity and reliability and fairness standards * A process for detecting plagiarism that demonstrates the privacy principle * Quality assurance and documentation processes that demonstrate the accountability and transparency principle ➤ You can read a summary of the paper in the link in the comments ➤ Get in touch if you’d like to have a paper like this about your own company! #responsibleai #aiethics

🚀 Bias in AI Models: Addressing the Challenges Imagine AI systems making critical decisions about job applications, loan approvals, or legal judgments. If these systems are biased, it can lead to unfair outcomes and discrimination. Understanding and addressing bias in AI models is crucial for creating fair and equitable technology. 🌟 **Relatable Example**: Think about an AI-based hiring tool that disproportionately favors certain demographics over others. Such biases can perpetuate inequality and undermine trust in AI. Here’s how we can address bias in AI models: 🔬 **Bias Detection**: Regularly test AI models for biases during development and after deployment. Use tools and methodologies designed to uncover hidden biases. #BiasDetection ⚖️ **Fair Training Data**: Ensure that training data is diverse and representative of all groups to minimize biases. This includes balancing data and avoiding over-representation of any group. #FairData 🛠️ **Algorithmic Fairness**: Implement fairness-aware algorithms and techniques to reduce biases in AI models. This involves adjusting models to treat all individuals and groups equitably. #FairAlgorithms 🔄 **Continuous Monitoring**: Continuously monitor AI systems for bias, especially as new data is introduced. Regular audits and updates help maintain fairness over time. #AIMonitoring 👨💻 **Inclusive Design**: Involve diverse teams in AI development to bring multiple perspectives and reduce the likelihood of biased outcomes. Inclusivity in design leads to more balanced AI systems. #InclusiveDesign ❓ **Have you encountered biased AI models in your work? What steps do you think are essential to address these biases? Share your experiences and insights in the comments below!** 👉 **Interested in the latest discussions on AI and bias? Follow my LinkedIn profile for more updates and insights: [Durga Gadiraju](https://lnkd.in/gfUvNG7). Let’s explore this crucial issue together!** #BiasInAI #AI #FairAI #TechEthics #FutureTech #AIModels #InclusiveAI #ResponsibleAI

Bias in AI = Ad fairness? Understanding AI bias is crucial for ethical advertising. AI can perpetuate biases from training data, impacting ad fairness. I've written an article for Forbes Technology Council "Understanding And Mitigating AI Bias In Advertising" (link in comments), synopsis: Key Strategies: (a) Transparent Data Use: Ensure clear data practices. (b) Diverse Datasets: Represent all demographic groups. (c) Regular Audits: Conduct independent audits to detect bias. (d) Bias Mitigation Algorithms: Use algorithms to ensure fairness. Frameworks & Guidelines: (a) Fairness-Aware Tools: Incorporate fairness constraints  (TensorFlow Fairness Indicators from Google and IBM’s AI Fairness 360) (b) Ethical AI Guidelines: Establish governance and transparency. (c) Consumer Feedback Systems: Adjust strategies in real-time. Follow Evgeny Popov for updates. #ai #advertising #ethicalai #bias #adtech #innovation

In Nov 2021, a huge wave of Gen AI hit the market with the launch of ChatGPT. However, there is something significant that often gets ignored: As Gen AI became the talk of the town, businesses began to adapt it for growth. At Quadrant Technologies, we have worked on a myriad of Gen AI projects with some incredible organizations. But soon, we realized its dark side that not many talk about : 👉 Threats of Generative AI Technology reflects society. The threats of GenAI include biases, influence, lack of transparency, hallucination, ethics, and much more. These threats can impact people’s decisions, experiences, and lives. 👉 The Solution: RESPONSIBLE AI As it has been said, with great power comes great responsibility. To reduce the effects of all these threats, Responsible AI comes into the picture. It is more than a buzzword. It ensures that AI will be used for the greater good of humanity and not as a threat. Many ways have now emerged to ensure responsible AI. One of these is the OECD AI Principles, offered by the Organization for Economic Co-operation and Development. At Quadrant Technologies, we helped organizations use this framework to mitigate the risks of GenAI. Here is that 6-component framework: 1/ Fairness: AI systems should treat all individuals equally. For this, businesses should recognize potential biases and work towards preventing them. 2/ Transparency: AI-powered apps have the power to influence our decisions. Therefore, companies should be transparent about how the AI models are trained. 3/ Inclusiveness: AI technology should address the needs of diverse individuals and groups. Organizations must ensure that their AI systems follow inclusivity. 4/ Accountability: Organizations must take responsibility for any negative impacts caused by their AI systems, proactively identifying and mitigating risks. 5/ Reliability & Safety: AI systems should be built and tested to ensure they operate safely and effectively, minimizing harm and accidents through thorough testing and risk assessment. 6/ Privacy & Security: AI models should be designed to respect users' privacy and secure their data. This means preventing models from improperly accessing or misusing personal information, ensuring data protection from the AI's perspective. Here are the ways tech organizations can embed this framework into their culture: 📍 Train and educate: Teach teams about ethical AI principles and bias risks. 📍Detect AI bias before scaling: Test for biases at every stage of scaling. 📍Community management: Engage with affected communities for feedback to ensure fairness and inclusivity. ------------ AI is here to stay. Ensuring that we develop and use it responsibly is the only way to leverage it for the betterment of society. What's your perspective? #genai #aisytems #threat