Challenges in Aligning AI Ethics

On May 28, 2024, the Science, Innovation and Technology Select Committee, appointed by the UK House of Commons, published a report on the governance of AI, reviewing developments in AI governance and regulation since an earlier interim report in August 2023: https://lnkd.in/gX4nZrk9 The report underscores the necessity of fundamentally rethinking the approach to AI, particularly addressing the challenges posed by AI systems that operate as "black boxes" with opaque decision-making processes. It stresses the importance of robust testing of AI outputs to ensure accuracy and fairness when the internal workings of these systems are unclear. The report also highlights challenges in regulatory oversight, noting the difficulties faced by a newly established AI Safety Institute in accessing AI models for safety testing, as previously agreed upon by developers. It calls for future government action to enforce compliance and potentially name non-compliant developers. The document concludes by emphasizing the need for an urgent policy response to keep pace with AI's rapid development. It noted that optimal solutions for AI's challenges aren't always clear. In this context, the report identified "Twelve Challenges of AI Governance" and proposed initial solutions (see p. 89ff): 1. Bias Challenge: Addressing inherent biases in AI datasets and ensuring fair outcomes. 2. Privacy Challenge: Balancing privacy with the benefits of AI, particularly in sensitive areas like law enforcement. 3. Misrepresentation Challenge: Addressing the misuse of AI in creating deceptive content, including deepfakes. 4. Access to Data Challenge: Ensuring open and fair access to data necessary for AI development. 5. Access to Compute Challenge: Providing equitable access to computing resources for AI research and development. 6. Black Box Challenge: Accepting that some AI processes may remain unexplainable and focusing on validating their outputs. 7. Open-Source Challenge: Balancing open and proprietary approaches to AI development to encourage innovation while maintaining competitive markets. 8. Intellectual Property and Copyright Challenge: Developing a fair licensing framework for the use of copyrighted material in training AI. 9. Liability Challenge: Clarifying liability for harms caused by AI, ensuring accountability across the supply chain. 10. Employment Challenge: Preparing the workforce for the AI-driven economy through education and skill development. 11. International Coordination Challenge: Addressing the global nature of AI development and governance without necessarily striving for a unified global framework. 12. Existential Challenge: Considering the long-term existential risks posed by AI and focusing regulatory activity on immediate impacts while being prepared for future risks. Thank you, Chris Kraft, for posting - follow his incredibly helpful posts around AI Gov, and AI in the public sphere.

Guidance for a more Ethical AI 💡This guide, "Designing Ethical AI for Learners: Generative AI Playbook for K-12 Education" by Quill.org, offers education leaders insights gained from Quill.org's six years of experience building AI models for reading and writing tools used by over ten million students. 🚨This playbook is particularly relevant now as educational institutions address declining literacy and math scores exacerbated by the pandemic, where AI solutions hold promise but also risks if poorly designed. The guide explains Quill.org's approach to building AI-powered tools. While the provided snippets don't detail specific tools, they highlight the process of collecting student responses and having teachers provide feedback, identifying common patterns in effective coaching. #Bias: AI models are trained on data, which can contain and perpetuate existing societal biases, leading to unfair or discriminatory outcomes for certain student groups. #Accuracy and #Errors: AI can sometimes generate inaccurate information or "hallucinate" content, requiring careful fact-checking and validation. #Privacy and #Data #Security: AI systems often collect student data, raising concerns about how this data is stored, used, and protected. #OverReliance and #Reduced #Human #Interaction: Over-dependence on AI could diminish crucial teacher-student interactions and the development of critical thinking skills. #Ethical #Use and #Misinformation: Without proper safeguards, AI could be used unethically, including for cheating or spreading misinformation. 5 takeaway #Ethical #Considerations are #Paramount: Designing and implementing AI in education requires a strong focus on ethical principles like transparency, fairness, privacy, and accountability to protect students and promote equitable learning. #Human #Oversight is #Essential: AI should augment, not replace, human educators. Teachers' expertise in pedagogy, empathy, and the ability to foster critical thinking remain irreplaceable. #AI #Literacy is #Crucial: Educators and students need to develop AI literacy, understanding its capabilities, limitations, potential biases, and ethical implications to use it responsibly and effectively. #Context-#Specific #Design #Matters: Effective AI tools should be developed with a deep understanding of educational needs and learning processes, potentially through methods like analyzing teacher feedback patterns.  Continuous Evaluation and Adaptation are Necessary: The impact of AI in education should be continuously assessed for effectiveness, fairness, and unintended consequences, with ongoing adjustments and improvements. Via Philipp Schmidt Ethical AI for All Learners https://lnkd.in/e2YN2ytY Source https://lnkd.in/epqj4ucF

Tech’s next ethical facade seems to be AI Ethics, according to a study by Stanford Institute for Human-Centered Artificial Intelligence (HAI) . I see a lot of commonalities between this hot field and its previously popular cousin - DEIB (Diversity, Equity, Inclusion, & Belonging). Companies “talk the talk” of principle and policy without fully “walking the walk” of their implementation. The study (I'll post it in the comments) delves into the challenges of integrating AI ethics within tech companies, revealing a disconnect between policies and actual practices. Dubbed as 'ethics washing,' the paper highlights how workers tasked with ethical integration are Ethics Entrepreneurs who are pioneering change amidst lack of organizational support. ➕ Business vs. Values: The relentless drive for product launches and winning the race of AI hype puts AI ethics on backburner. AI Ethics workers struggle against an industry ethos that prioritizes product launches over ethics and product metrics over morals. ➕ Navigating Organizational Change: Frequent team reorganizations hinder the continuity and knowledge sharing on AI Ethics to be internalized. ➕ The Risk of Advocacy: The individuals championing ethical change, often from marginalized backgrounds, bear personal risks, suggesting a need for deeper organizational reform and robust support for ethics in tech. In my view, all 3 sound very familiar with the DEIB space. AI ethics workers and DEIB champions are not just participants in their fields; they are agents of change, striving to embed deeply held values into the fabric of their organizations. The difference is the enormous business disruption by AI - however, there is learning from the DEIB space as well as Data privacy space that can help salvage this field before we create irreversible risks as a society. Thoughts shared here are my own perspective only and my advisory Belong & Lead. Thanks Sanna Ali, PhD Riitta Katila Andrew Smart Angèle Christin for the paper and Kevin Klyman for the excellent recommendation to this paper. #artificialintelligence #humanresources #futureofwork #future #leadership #culture #peopleanalytics #aiethics #responsibleai #irresponsibleai #diversityequityinclusionandbelonging

I've recently worked with organizations genuinely trying to evolve, leaders open to AI but often unsure how to proceed responsibly. What I’ve learned is simple: it’s not ambition that creates risk, it’s the absence of aligned frameworks to guide it. I was reading a report from the Future of Life Institute (FLI) last week which revealed even the top AI labs - OpenAI, Anthropic, DeepSeek AI etc - those building artificial general intelligence, have major gaps in safety, governance, and long-term planning. That isn’t cause for panic. It’s a prompt for reflection. If those at the frontier are still learning how to govern what they build, then the rest of us have a profound opportunity: to pause, ask better questions, and design with greater clarity from the outset. In this article, I unpack what this report actually signals, not just for labs, but for businesses, leadership teams, and transformation projects across sectors. I also share a practical readiness model I use with clients to ensure what we build is powerful, sustainable, safe, and aligned with human intention. There’s no need to fear AI. But we do need to lead it with structure, integrity, and long-range thinking. Big thanks to voices like Luiza Jarovsky, PhD for elevating AI safety and Sarah Hastings-Woodhouse for the vital governance dialogues, they remind us that this is both urgent and collaborative. #ArtificialIntelligence #AGI #ResponsibleAI #AILeadership #TechGovernance #AIReadiness #EthicalInnovation #EnterpriseAI #FutureOfWork #AIXccelerate

Why do 60% of organizations with AI ethics statements still struggle with bias and transparency issues? The answer lies in how we approach responsible AI. Most companies retrofit ethics onto existing systems instead of embedding responsibility from day one. This creates the exact disconnect we're seeing everywhere. I've been exploring a framework that treats responsible AI as an operational capability, not a compliance checkbox. It starts with AI-specific codes of ethics, builds cross-functional governance teams, and requires continuous monitoring rather than periodic reviews. The research shows organizations that establish robust governance early see 40% fewer ethical issues and faster regulatory approval. But here's what surprised me most - responsible AI actually accelerates innovation when done right because it builds the trust necessary for broader adoption. What are some of the biggest AI ethical obstacles you're trying to solve for? I will tell you what I hear in the comments.

𝗔𝗜 𝗲𝘁𝗵𝗶𝗰𝘀 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗮𝗿𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿𝘀 — 𝗻𝗼𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀. That’s the impression you get when employees are unaware. Creating an AI ethics policy is just the start — not the end. The real challenge lies in ensuring it’s fully understood and adopted by everyone in your organization. Because: a lack of employee engagement means that your AI ethics policy remains just a piece of paper — without consequence. Few leaders realize that AI ethics: - requires more than spotting bias in data - concerns more than just your AI team - needs more than a document on some SharePoint page For your AI ethics policy to be effective, it needs to reach the intended audience and guide their behavior. Accomplishing that can be as simple 4 steps: - formal committee defines the policy - working group communicates it & gets feedback - model review process ensures adherence - formal training supports all employees Unlike 6-7 years ago, most companies have an AI ethics policy in place. 𝗕𝘂𝘁 𝗵𝗼𝘄 𝗰𝗮𝗻 𝘁𝗵𝗮𝘁 𝗽𝗼𝗹𝗶𝗰𝘆 𝗯𝗲𝗰𝗼𝗺𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗻𝗮𝘁𝘂𝗿𝗲? I’m excited to welcome Elizabeth Adams, one of the leading experts on this topic, on “What’s the 𝘉𝘜𝘡𝘡?” this week. 𝘓𝘦𝘢𝘳𝘯 𝘸𝘪𝘵𝘩 𝘶𝘴 𝘭𝘪𝘷𝘦 𝘰𝘯 𝘛𝘶𝘦𝘴𝘥𝘢𝘺: https://lnkd.in/d8XaDHPd #ArtificialIntelligence #Ethics #GenerativeAI #IntelligenceBriefing

A study recently featured by the European Pharmaceutical Review and published in the Asian Journal of Advanced Research and Reports, delved into how regulatory compliance, ethical consciousness, professional training, direct AI engagement, and the efficacy of tool application and data integrity interrelate. The results reveal a clear positive link between strict regulatory compliance and the effectiveness of technology implementation. However, ethical concerns persist, especially regarding the security of data that intelligent systems depend on. There are still challenges facing life science and healthcare organizations which must be worked through before AI technology is mature enough for use in critical business decisions that may impact patient health. As the European Pharmaceutical Review notes, the study emphasized the potential risks associated with intentional data manipulation or the impact of AI biases in algorithms on patient health. Such concerns cast a shadow over the reliability and impartiality of decisions influenced by AI. AI systems function based on the quality of the data they’re fed and the instructions they’re given. With appropriate and accountable human oversight, AI-based technology can supplement expertise while mitigating the risks. Rooting out any chance of flawed or biased data sources is an imperative move toward ensuring a safer, more innovative future for everyone. Recent regulation signed in the EU will both help and slow down the effective progress of AI. A balance between regulation and risk is critical. #Clarivate #Pharma #Healthcare #Innovation #LifeScience #AI

🤝 Embracing Ethical Constraints in Deploying LLMs 🌏🚀 As the power and potential of LLMs continue to grow, we, the AI community, must acknowledge and address the ethical challenges associated with their deployment. Today, I want to highlight the importance of ethical constraints in leveraging these powerful AI tools responsibly. 📚 The Power of Language Models: Large language models have demonstrated their ability to revolutionize natural language understanding and generation across various applications. They enable us to automate tasks, enhance customer experiences, and provide innovative solutions to complex problems. ⚖️ Embracing Ethical Constraints: As we use these powerful models, we must be mindful of the potential ethical implications they may bring. Ethical constraints are not meant to stifle innovation but to ensure that we integrate AI responsibly, protect user privacy, and mitigate unintended biases. 👉 Privacy and Data Protection: Data privacy should be at the forefront of our minds when deploying large language models. We must ensure that sensitive data is handled securely and consent is obtained from users before using their data for AI applications. Striking the right balance between data utilization and user privacy is essential to build trust with user in the long run. 🎭 Addressing Bias and Fairness: Large language models are trained on vast amounts of data from the internet, which can inadvertently perpetuate biases present in the data. It's crucial to continuously monitor and mitigate bias during model training and deployment to provide fair and equitable user experiences. 🤖 Transparency and Explainability: Understanding how AI models arrive at their decisions is essential for building user trust and ensuring accountability. Efforts must be made to enhance the transparency and explainability of these models to enable users to comprehend and contest the outcomes of AI-driven systems. 🚀 Collaborative Responsibility: Solving ethical challenges in deploying large language models requires collective efforts. Collaboration between researchers, developers, policymakers, and end-users is crucial to establish best practices, guidelines, and regulatory frameworks that promote responsible AI deployment. 🔬 Constant Evaluation and Improvement: The field of AI is ever-evolving, and so are ethical concerns. We must actively evaluate and improve our AI systems to align with ethical standards and societal values. Together, let's foster an ethical AI ecosystem that maximizes the benefits of large language models while minimizing potential harm. By embracing ethical constraints, we can positively impact and create a future where AI serves as a force for good. #AIForGood #EthicalAI What do you think about ethical constraints in deploying large language models? #EthicsInAI #AIResponsibility #ResponsibleAI #LanguageModels #AICommunity

🔥 Hot off the press 🔥 My latest article: 🚀 The Ethics Disconnect: Why AI Needs a New Approach 🚀 As a 2022 graduate of the Harvard Kennedy School specializing in AI ethics, I’ve come to recognize the profound and urgent challenges we face in this dynamic field. The rapid advancement of AI technologies has left our ethical frameworks struggling to keep up, creating significant gaps that demand immediate attention. In my latest article, I delve into the crucial areas where AI ethics needs to evolve: ✅ Theoretical Underpinnings vs. Practical Applications: How do we translate robust ethical theories into actionable guidelines for real-world scenarios? ✅ Regulation and Governance: Why are our current policies reactive rather than proactive, and how can we change this? ✅ Ethical AI by Design: Moving from compliance to embedding ethics at every stage of AI development. ✅ Inclusivity and Diversity: Ensuring that AI systems reflect diverse perspectives and mitigate biases. ✅ Accountability and Responsibility: Establishing clear accountability frameworks for AI actions. ✅ Public Awareness and Engagement: Bridging the gap in public understanding and involvement in AI ethics. These discussions are not just theoretical; they are about shaping a future where AI technologies are a force for good, aligned with societal values and norms. I invite you to join the conversation. Share your insights and experiences on the ethical implications of AI, and let’s work together to bridge these critical gaps. What steps do you believe are most crucial for aligning AI development with ethical principles and ensuring accountability? Let’s inspire a future where AI serves the common good and upholds the values we hold dear. 🌟 #AIethics #EthicalAI #AIdevelopment #DiversityInTech #AIregulation #InclusiveAI #AIaccountability #FutureOfAI #AIgovernance #PublicEngagementAI #HarvardKennedySchool #TechForGood #ethics

ChatGPT, can you keep a secret? As AI becomes more integrated into legal practice, challenges inevitably arise about their impact on our bedrock of legal ethics: confidentiality. While most lawyers just think of AI as ChatGPT, AI encompasses the use of computer systems and algorithms capable of performing tasks that previously required human intelligence - a Google search and spellcheck are as much AI as an appellate brief drafted by ChatGPT. Traditionally, maintaining client confidentiality involved a combo of professional discretion, secure document handling, and clear communication protocols. Law firms relied on physical security measures (like locked filing cabinets), professional codes of conduct, and vetting of employees who have access to sensitive information. In the context of AI, these traditional methods of maintaining confidentiality face new challenges. AI systems, which often require access to large datasets, including sensitive client information, pose unique risks. When integrating AI, selecting the right tools is paramount. Lawyers must ensure that any AI technology employed adheres to the highest standards of confidentiality and security. Here are some big picture guidelines: - Vet AI vendors - Understand the tool’s capabilities and limits - Set Clear Boundaries for data - Audit, audit, audit! Lawyers must strike a delicate balance between leveraging the advantages of AI and upholding the ethical standards of the profession. This balance requires a deep understanding of both the potential and limitations of AI technologies, coupled with a steadfast commitment to the core principles of legal ethics, particularly client confidentiality. Adopting best practices in selecting and using AI tools is critical. This includes conducting due diligence on AI vendors, understanding the workings of AI technologies, and setting clear boundaries for data use. Additionally, ongoing education and awareness about AI's role in legal ethics are vital for adapting to this evolving landscape. How are you using AI and protecting client confidentiality? #law #legalai —— Want to know more? Shoot me a DM and follow #TheLawFirmGC Ring my 🔔 for better practice, less stress.