AI Ethics and Fairness in Product Management

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

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

🗺 Navigating AI Impact Assessments with ISO 42005: Essential Areas for Compliance Leaders 🗺 In speaking with compliance, cybersecurity, and AI leaders around the world, one of the most common questions I have been getting of late is, “As we prepare for ISO 42001 certification, what blind spots should we be working to address?” Without hesitation, my response has been, and will continue to be, conducting and documenting a meaningful AI Impact assessment. Fortunately, though still in DRAFT status, ISO 42005 provides a structured framework for organizations to navigate that very concern effectively. As compliance executives, understanding and integrating the key components of this standard into your AI impact assessments is critical; below are the areas I feel are most essential for you to begin your journey. 1. Ethical Considerations and Bias Management:   - Address potential biases and ensure fairness across AI functionalities. Evaluate the design and operational parameters to mitigate unintended discriminatory outcomes. 2. Data Privacy and Security:   - Incorporate robust measures to protect sensitive data processed by AI systems. Assess the risks related to data breaches and establish protocols to secure personal and proprietary information. 3. Transparency and Explainability:   - Ensure that the workings of AI systems are understandable and transparent to stakeholders. This involves documenting the AI's decision-making processes and maintaining clear records that explain the logic and reasoning behind AI-driven decisions. 4. Operational Risks and Safeguards:   - Identify operational vulnerabilities that could affect the AI system’s performance. Implement necessary safeguards to ensure stability and reliability throughout the AI system's lifecycle. 5. Legal and Regulatory Compliance:   - Regularly update the impact assessments to reflect changing legal landscapes, especially concerning data protection laws and AI-specific regulations. 6. Stakeholder Impact:   - Consider the broader implications of AI implementation on all stakeholders, including customers, employees, and partners. Evaluate both potential benefits and harms to align AI strategies with organizational values and societal norms. By starting with these critical areas in your AI impact assessments as recommended by ISO 42005, you can steer your organization towards responsible AI use in a way that upholds ethical standards and complies with regulatory, and market, expectations. If you need help getting started, as always, please don't hesitate to let us know! A-LIGN #AICompliance #ISO42005 #EthicalAI #DataProtection #AItransparency #iso42001 #TheBusinessofCompliance #ComplianceAlignedtoYou

OpenAI introduced Custom Instructions which brings forth a significant advancement in tailoring AI responses to specific contexts. However, there is a noteworthy aspect that gives rise to certain concerns for me.... the balance between bias and neutrality in responses generated by the AI. While the ability to customize instructions is indeed powerful and allows for fine-tuning the AI's behavior, there is a potential pitfall. The risk of unintentional bias or the establishment of permanent biases within the AI's responses is a valid concern. The dynamic nature of language, coupled with the diversity of user inputs, can lead to the AI inadvertently adopting biases that may be embedded in the instructions. Maintaining a neutral stance is essential for an AI system like Chat, as it ensures fairness and equal treatment across all interactions. The challenge lies in striking a balance between customization and neutrality. If the AI is inadvertently influenced by biased instructions, it might lead to skewed responses that could unintentionally reinforce stereotypes or misconceptions. As someone deeply involved in network coordination and security with a background in programming, the importance of unbiased decision-making and the potential ramifications of biases in technical systems cannot be overlooked. It's crucial for AI to provide responses that are not influenced by any particular bias. Addressing this concern requires a thoughtful approach to crafting custom instructions. It's imperative to ensure that any instructions given to the AI are carefully designed to avoid promoting or reinforcing any kind of bias. As AI systems continue to evolve, it's vital to maintain a proactive stance in monitoring and addressing bias, making adjustments as needed to uphold the principles of fairness and neutrality. While Custom Instructions offer valuable customization capabilities, it's essential to remain cautious about the potential for biases to seep into AI responses. Your expertise in managing complex systems and your dedication to security underscore the importance of maintaining a neutral and unbiased approach, not only in your own work but also in the AI systems we develop and deploy. Chuck Brooks Bob Carver, CISM, CISSP, MS ✭ Murat Durmus Shawnee Delaney Alexandre BLANC Cyber Security Alexandre Borges Kevin Apolinario Carmen Marsh Gabrielle B. Angelique "Q" Napoleon Malak Trabelsi Loeb Dr. Martha Boeckenfeld Prof. Dr. Ingrid Vasiliu-Feltes Dr. Agatha K. Rokicki, D.B.A., B.S. Dan Williams Dorota Kozlowska

While AI models are emblematic of the data they're trained on, they also inadvertently mirror the perspectives and biases of their creators. This challenges the myth of AI neutrality and underscores the broader complexities of bias— which isn't merely a data issue but permeates every stage of model development. However, it's not just about acknowledging these biases but understanding their intricacies, especially in a society grappling with polarizing views. Instead of an impossible standard of neutrality, maybe our focus should shift to honesty and customization. By offering transparency about a model's inherent biases and allowing users to personalize their AI interactions, we might be able to strike a balance that serves diverse perspectives without amplifying misinformation. Yet, with every stride in AI customization, we tread on a double-edged sword. The same tools that can weed out unpleasantness can also be weaponized to reinforce misinformation, underscoring the ethical complexities we must navigate. As AI's role in society becomes more pronounced, so does the imperative for ethical diligence and transparency. #Transparency #Personalization #EthicalAI #AIandPrivacy

How do we maximize value and minimize risks with Generative AI like ChatGPT? In the age of AI, where Generative AI and large language models like ChatGPT play an increasingly significant role, it's crucial to understand their limitations for optimal interaction. These models can inadvertently provide plausible yet inaccurate answers, lacking real-world experience and up-to-date information. The introduction of the PROMPT Framework equips users with the tools for more transparent, robust conversations with AI. The Framework emphasizes the significance of asking for explanations, sources, and logical reasoning to ensure the validity and reliability of the AI’s responses. Key Steps in the PROMPT Framework include: 1. Ask Explainable Prompts. Push the model to provide sources and reasoning behind its response. 2. Have Multi-Step, Logical Conversations. This involves starting broad and then getting more specific, linking each question naturally. 3. Troubleshoot Insufficient Responses. Rephrase, simplify, or provide additional context to ensure clearer, more accurate answers. 4. Be Precise with Prompts. Craft focused, precise queries to avoid ambiguity and enhance accuracy. 5. Maintain Ethical Prompts. Uphold truth, fairness, and respect for all, encoding these qualities into prompts. 6. Personalize for User Needs. Tailor prompts based on the user’s knowledge level, circumstances, and goals. 7. Craft Culturally Sensitive Prompts. Ensure diversity and inclusivity to avoid perpetuating societal biases. The PROMPT Framework guides users in harnessing AI’s power responsibly for enhanced learning and information literacy. It ensures that humans remain a crucial element in AI interactions, fostering transparency and ethical AI use. Want to learn more about effectively using AI for informed decision-making? #generativeAI #ethicalAI #turningdataintowisdom #dataliteracy #dataliteracyinpractice #qlik #datainformed #futureskills https://lnkd.in/ekQXERCS

AI isn’t just about algorithms. It’s about responsibility. Here's how to Navigate AI Ethics in 3 Crucial Steps: 1. Data Transparency ↳ Be clear about how you collect and use data. ↳ Build trust through openness. 2. Bias Prevention ↳ Actively work to eliminate biases in AI. ↳ Diverse perspectives lead to fairer AI. 3. Continuous Monitoring ↳ AI isn’t set-and-forget. It evolves. ↳ Regularly assess the ethical impact of your AI. Ethical AI isn’t something to take lightly.  ↳ it’s a necessity moving forward It's about caring for people. Just as much as we care about progress. P.S. How do you ensure your AI practices are ethical?

AI regulatory frameworks are cropping up across regions, but it's not enough. So far, we've seen: - EU’s Artificial Intelligence Act: Setting a global precedent, the EU's draft AI Act focuses on security, transparency, and accountability. - U.S. AI Executive Order by Biden Administration: Shares strategies for AI, emphasizing safety, privacy, equity, and innovation. - Japan's Social Principles of Human-Centric AI: Japan emphasizes flexibility and societal impact in their AI approach. - ISO's Global Blueprint: ISO/IEC 23053:2022/AWI Amd 1 aims to standardize AI systems using machine learning worldwide. - IAPP's Governance Center: Leading in training professionals for intricate AI regulation and policy management. But these are just the beginning, a starting point for all of us. Ethical AI usage goes beyond regulations; it's about integrating ethical considerations into every stage of AI development and deployment. Here’s how YOU, as an in-house counsel, can ensure ethical AI usage in your company, specifically when it comes to product development: - Always disclose how AI systems make decisions. This clarity helps build trust and accountability - Regularly audit AI systems for biases. Diverse data and perspectives are essential to reduce unintentional bias - Stay informed about emerging ethical concerns and adjust practices accordingly - Involve a range of stakeholders, including those who might be impacted by AI, in decision-making processes - Invest in training for teams. Understanding ethical implications should be as fundamental as technical skills The collective global efforts in AI regulation, like those from the US, EU, Japan, ISO, and IAPP, lay the foundation. However, it's our daily commitment to ethical AI practices that will truly harness its potential while ensuring that AI serves humanity, not the other way around. #AIRegulations #AIUse #AIEthics #SpotDraftRewind

CEOs are asking for business outcomes to be achieved with enterprise GenAI. Their teams rush to implement tools that can do more harm than good. Or compliance teams are blocking GenAI roll out. How can we strike the right balance between innovation and compliance? AIMultiple looked into early implementations for clues. The below principles can be used to evaluate GenAI tools before roll-out: ✅ Consistent Enterprise customers need predictability and enterprises deliver that. This sets them apart from immature businesses. ✅ Controlled Building with evolving 3rd party APIs is building on sand. Enterprises need to own at least parts of the tech stack. ✅ Explainable Enterprise users need to know the data that drive decisions. RAG can support this. ✅ Reliable Through human-in-the-loop or guardrails, expensive mistakes need to be avoided. ✅ Secure Depending on the attack surface, securing a model can be a trivial or complex but it needs to be considered. ✅ Ethically trained An LLM built on unethical data is a bomb waiting to explode. Enterprises need to understand the training data. ✅ Fair Bias in training data can impact model effectiveness. ✅ Licensed LLM licensing is complex but important. You don't want to rely on Llama-2 in a product that will have 700M active users next year. ✅ Sustainable Business leaders should be aware of the full cost of generative AI and identify ways to minimize its ecological and financial costs. Sources: More background on the principles: https://lnkd.in/eamiSzj9 LLM API changes: https://lnkd.in/dm7Kg_ig Llama 2 license: https://lnkd.in/dXnzMasv *** Follow me for latest in B2B tech Ring the 🔔 on my profile for notifications #enterpriseai #generativeai #ethicalai