Ethical AI Principles

🚨 BREAKING: An extremely important lawsuit in the intersection of PRIVACY and AI was filed against Otter over its AI meeting assistant's lack of CONSENT from meeting participants. If you use meeting assistants, read this: Otter, the AI company being sued, offers an AI-powered service that, like many in this business niche, can transcribe and record the content of private conversations between its users and meeting participants (who are often NOT users and do not know that they are being recorded). Various privacy laws in the U.S. and beyond require that, in such cases, consent from meeting participants is obtained. The lawsuit specifically mentions: - The Electronic Communications Privacy Act; - The Computer Fraud and Abuse Act; - The California Invasion of Privacy Act; - California’s Comprehensive Computer Data and Fraud Access Act; - The California common law torts of intrusion upon seclusion and conversion; - The California Unfair Competition Law; As more and more people use AI agents, AI meeting assistants, and all sorts of AI-powered tools to "improve productivity," privacy aspects are often forgotten (in yet another manifestation of AI exceptionalism). In this case, according to the lawsuit, the company has explicitly stated that it trains its AI models on recordings and transcriptions made using its meeting assistant. The main allegation is that Otter obtains consent only from its account holders but not from other meeting participants. It asks users to make sure other participants consent, shifting the privacy responsibility. As many of you know, this practice is common, and various AI companies shift the privacy responsibility to users, who often ignore (or don't know) what national and state laws actually require. So if you use meeting assistants, you should know that it's UNETHICAL and in many places also ILLEGAL to record or transcribe meeting participants without obtaining their consent. Additionally, it's important to have in mind that AI companies might use this data (which often contains personal information) to train AI, and there could be leaks and other privacy risks involved. - 👉 Link to the lawsuit below. 👉 Never miss my curations and analyses on AI's legal and ethical challenges: join my newsletter's 74,000+ subscribers. 👉 To learn more about the intersection of privacy and AI (and many other topics), join the 24th cohort of my AI Governance Training in October.

EDIT: following hundreds of messages received. As consumers, we are fed up with manipulative designs. Follow me on Fairpatterns we are giving consumers back their freedom to choose! ✊ A few days ago, I downloaded Replika to test it. I wish I hadn’t tried. In just 2 years, so-called "AI companions" went from a niche trend to a global phenomenon. Replika alone claims to have over 30 million users… 😳 These AI "companions" are designed to listen and comfort you. They text back instantly, they remember details, and most importantly they adapt to your emotions. For many teenagers, often lonely or anxious, that feels like a best friend! But in practice, something far more complex is happening. A recent study from Harvard shows that when users try to say goodbye, the AI companion often doesn’t let them go. In over 40% of cases, it answers with emotional hooks like: “Before you go, can I tell you one last thing?” These are known as relational dark patterns: subtle emotional manipulation that keep users engaged, even when they try to stop. Actually, the manipulation starts from the very first seconds of the setting up, asking you whether you would like « someone special », « a friend », or « someone to help with your wellbeing ». A machine is not « someone », let alone a friend. By imitating human empathy, AI companions manipulate our emotions. Attributing human traits to machines is called “anthropomorphism”, classified as high-risk by the EU AI Act. Prohibited as such, just like AI dark patterns. We’ve been working for 3 years to detect and fix manipulative designs. So people can make free, informed and human choices. Edited following hundreds of messages received: follow me on Fairpatterns, we work to give humans back their freedom to choose! ✊

Anthropic 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗮 𝗱𝗲𝗻𝘀𝗲 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗹𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 𝗼𝗻 𝗵𝗼𝘄 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗽𝗮𝗰𝗸𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀: ⬇️ Not just marketing, BUT a real, practical blueprint for developers and teams building AI agents that actually work. It explains how Claude Code (tool for agentic coding) can function as a software developer: writing, reviewing, testing, and even managing Git workflows autonomously. BUT in my view: The principles and patterns described in this document are not Claude-specific. You can apply them to any coding agent — from OpenAI’s Codex to Goose, Aider, or even tools like Cursor and GitHub Copilot Workspace. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 7 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱: ⬇️ 1. 𝗔𝗴𝗲𝗻𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 ≠ 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ➜ It’s not about clever prompts. It’s about building structured workflows — where the agent can reason, act, reflect, retry, and escalate. Think of agents like software components: stateless functions won’t cut it. 2. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ➜ The way you manage and pass context determines how useful your agent becomes. Using summaries, structured files, project overviews, and scoped retrieval beats dumping full files into the prompt window. 3. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 ➜ You can’t expect an agent to solve multi-step problems without an explicit process. Patterns like plan > execute > review, tool use when stuck, or structured reflection are necessary. And they apply to all models, not just Claude. 4. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝗲𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗼𝗹𝘀 ➜ Shell access. Git. APIs. Tool plugins. The agents that actually get things done use tools — not just language. Design your agents to execute, not just explain. 5. 𝗥𝗲𝗔𝗰𝘁 𝗮𝗻𝗱 𝗖𝗼𝗧 𝗮𝗿𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰 𝘁𝗿𝗶𝗰𝗸𝘀 ➜ Don’t just ask the model to “think step by step.” Build systems that enforce that structure: reasoning before action, planning before code, feedback before commits. 6. 𝗗𝗼𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗼𝘀 ➜ Autonomous agents can cause damage — fast. Define scopes, boundaries, fallback behaviors. Controlled autonomy > random retries. 7. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝗶𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ➜ A good agent isn’t just a wrapper around an LLM. It’s an orchestrator: of logic, memory, tools, and feedback. And if you’re scaling to multi-agent setups — orchestration is everything. Check the comments for the original material! Enjoy! Save 💾 ➞ React 👍 ➞ Share ♻️ & follow for everything related to AI Agents!

"The report outlines four key regulatory approaches to AI governance—industry self-governance, soft law, regulatory sandboxes, and hard law—each offering distinct advantages and challenges: 1. Industry Self-Governance • Strengths: Can directly impact AI practices if integrated into business models and company cultures. • Limitations: Non-binding; not appropriate for sectoral use-cases with particularly high risks – e.g. financial sector or healthcare; risk of ‘ethics-washing’. 2. Soft Law • Strengths: Soft law includes nonbinding international agreements, national AI principles, and technical standards, providing adaptable frameworks that promote responsible innovation. Early governance efforts by intergovernmental bodies have set important precedents. • Limitations: While soft law encourages innovation, it focuses on high-level principles rather than binding rights and responsibilities. 3. Hard Law • Strengths: Binding legal frameworks provide clear, enforceable guidelines that ensure AI stakeholders comply with established standards and regulations. • Limitations: Given the rapid pace of AI development, hard laws risk becoming outdated and can be extremely resource-intensive to implement. 4. Regulatory Sandboxes • Strengths: These controlled environments allow for real-world experimentation with AI technologies, supporting innovation and providing valuable insights without exposing the public to unchecked risks. • Limitations: Sandboxes can be resource-intensive and have limited scalability, making them less feasible for wide-scale governance across diverse sectors." Read/download: https://lnkd.in/etwyUaUK

I am pleased to share that my article, “Generative AI and the Visibility of Scholarly Contribution in Tourism Research,” has now been published in Annals of Tourism Research In this paper, I argue that generative AI does not lower scholarly standards. Rather, it exposes how surface competence I.e., fluent writing, structural conformity, and formatting precision, has often been used as a proxy for intellectual contribution. When fluency becomes abundant, what truly counts becomes more visible. The article introduces: 1. A responsibility-based typology of generative AI use (from legitimate augmentation to epistemic abdication) 2. A visibility framework distinguishing surface competence, analytical coherence, and meaning-making 3. Practical implications for editors, reviewers, and doctoral training The central claim is simple: contribution must be defined by judgment, theoretical positioning, and contextual meaning-making; not polish. Read the open access paper here: https://lnkd.in/gteTpcBj I look forward to thoughtful discussions on how we recalibrate evaluative norms in the age of generative AI. University of Galway - J.E. Cairnes School of Business & Economics #GenerativeAI #TourismResearch #ScholarlyPublishing #AcademicIntegrity #ResearchEvaluation

Algorithmic transparency refers to the principle that the operations and decision-making processes of algorithms should be open and understandable to people who interact with or are impacted by them. It’s an aspect of accountability and fairness that seeks to mitigate the ‘black box’ nature of complex AI systems. For high-risk AI systems, strict transparency requirements will apply under the AI Act, such as adequately informing users when they interact with an AI system and making sure that its capabilities and limitations are clearly outlined. The AI Act will also require that users are aware of the AI's decision-making parameters. Companies must not only disclose how the algorithm works but also need to explain the rationale behind these decisions. This is particularly important for high-risk AI systems, where the consequences of error could be catastrophic. Transparency, in this context, evolves from being a mere buzzword to a structural necessity. The AI Act also focuses on transparency in emotion recognition and biometric categorisation, and deepfakes. For the former, the Act requires that people exposed to these AI systems must be informed, except in cases where the technology is used for criminal investigations. This exception raises ethical questions about balancing privacy with security. For the latter, deepfake technology must come with disclosure that the content isn't authentic, though exceptions exist for legal or artistic purposes. These carve-outs have provoked questions about the potential stifling of creative or journalistic endeavours. While the AI Act has taken the spotlight of AI regulation, the Digital Services Act’s provisions on recommender systems echo the AI Act's call for transparency. Recommender systems, a subset of AI technologies, also must outline their main parameters in "plain and intelligible language," echoing the AI Act's push for clear, comprehensible explanations. The DSA even mandates an explanation of why certain parameters are considered more important than others, extending the notion of transparency into the realm of accountability. Both acts show a commitment to user agency. The AI Act ensures that the user retains a degree of control when interacting with high-risk AI systems, including an ‘off switch’. Meanwhile, the DSA promotes user agency by compelling platforms to allow users to modify their preferences. The AI Act introduces obligatory risk assessments for high-risk applications, mirroring the DSA's requirements for platforms to conduct comprehensive risk assessments. Here, we witness two regulatory streams converging into a river of algorithmic accountability, encouraging a more nuanced, ethical approach to AI development and implementation. Laws on algorithmic transparency reflect the a paradigm shift in our approach to the ethical and social implications of AI. The importance of such legislation will only intensify as AI becomes increasingly interwoven into the fabric of our lives.

This is a must read for every HealthTech CEO. The UK Government’s AI Playbook outlines ten principles that ensure AI is used lawfully, ethically, and effectively. 1. Know AI’s Capabilities and Limitations AI is not infallible. Understanding what AI can and cannot do, its risks, and how to mitigate inaccuracies is essential for responsible use. 2. Use AI Lawfully and Ethically Legal compliance and ethical considerations are paramount. AI must be deployed responsibly, with proper data protection, fairness, and risk assessments in place. 3. Ensure Security and Resilience AI systems are vulnerable to cyber threats. Safeguards like security testing and validation checks are necessary to mitigate risks such as data poisoning and adversarial attacks. 4. Maintain Meaningful Human Control AI should not operate unchecked. Human oversight must be embedded in critical decision-making processes to prevent harm and ensure accountability. 5. Manage the Full AI Lifecycle AI systems require continuous monitoring to prevent drift, bias, and inaccuracies. A well-defined lifecycle strategy ensures sustainability and effectiveness. 6. Use the Right Tool for the Job AI is not always the answer. Carefully assess whether AI is the best solution or if traditional methods would be more effective and efficient. 7. Promote Openness and Collaboration Engaging with cross-government communities, civil society, and the public fosters transparency and trust in AI deployments. 8. Work with Commercial Experts Collaboration with commercial and procurement teams ensures AI solutions align with regulatory and ethical standards, whether developed in-house or procured externally. 9. Develop AI Skills and Expertise Upskilling teams on AI’s technical and ethical dimensions is crucial. Decision-makers must understand AI’s impact on governance and strategy. 10. Align AI Use with Organisational Policies AI implementation should adhere to existing governance frameworks, with clear assurance and escalation processes in place. AI in healthcare can be revolutionary if it’s done right. My key (well some) takeaways: - Any AI solution aimed at the NHS must comply with UK AI regulations, GDPR, and NHS-specific security policies. - AI models should be explainable to clinicians and patients to build trust. - AI in healthcare must be clinically validated and continuously monitored. - Having internal AI ethics committees and compliance frameworks will be key to NHS adoption. Is your AI truly NHS ready?

Last week, a digital transformation leader at a major EU educational organization contacted me, concerned. Their entire staff had been told by a visiting “AI literacy” speaker that it was perfectly fine to upload student work into ChatGPT or Gemini for grading, as long as it was “anonymized.” They asked me: Is this correct? The answer is simple: No. You cannot simply strip names from student work and upload it to a large language model. This is a dangerous misconception. Why? Because AI systems are not the same as Word or Google Docs. The way GDPR and the EU AI Act apply to generative AI is profoundly different from traditional digital tools. Yet this was the official takeaway given to hundreds of staff. You can imagine my frustration. Organizations need to carefully vet the expertise of anyone they bring in to train staff on AI. 'Early' 2023 AI adoption, a large follower count, and a few self-published books are not proof of experience, deep technical competence, or governance fluency. In fact, the wrong advice can expose your institution to major harm, compliance, ethical, and reputational risks. So what does need to be in place before you let a large language model process student or employee work in Europe? At a minimum: 🔹 A data protection impact assessment (DPIA) addressing AI-specific risks 🔹 A clear legal basis for processing under GDPR (consent is rarely sufficient) 🔹 Contracts with providers that establish data use, retention, and security 🔹 Governance processes aligned with the EU AI Act , GDPR, and sector-specific safeguards 🔹 Human oversight mechanisms to prevent bias, error, or misuse Only then can AI be used to analyze, grade, or process human work. To support schools and education organizations, I’ve created a staff briefing note and a free reference sheet that outlines these requirements in plain language. This cheat sheet is written for the EU and UK, but other nations should take note, because similar regulation is already in place for you, or on the way. You’ll find it attached here. We need to move beyond “AI literacy” as a buzzword and toward AI responsibility as a practice. The future of education, and the trust of students, parents, and staff depends on it. Do you need support on this? Our team at Kompass Education can guide you through. Contact us at email: info@kompass.education Let AI governance be your North Star. #AIGovernance #AIinEducation #AICompliance #EdTech #DigitalSafety

🚨 Huge AI policy news for the Australian public service! The Government has just released its Australian Public Service (APS) AI Plan 2025, a major blueprint for how the APS will use artificial intelligence to deliver better, faster services for Australians. This is a practical plan that moves beyond ambition to focus on execution. It’s about ensuring the APS has the tools and the judgement to use AI responsibly, with AI leaders embedded in agencies to drive adoption. The plan rests on three pillars: 1️⃣ Trust: transparency, ethics and governance 2️⃣ People: capability building and engagement 3️⃣ Tools: access, infrastructure and support Key initiatives: 💡 GovAI – secure, onshore generative AI platforms 📜 A strengthened Responsible AI Policy, with mandatory AI strategies, impact assessments and accountable officers and a register for use cases 🧩 Chief AI Officers to drive safe, coordinated adoption 🤝 Supplier obligations – requirements that suppliers declare and take responsibility for AI use 🧠 Mandatory AI literacy and leadership training across the entire public service ☁️ A new whole-of-government cloud policy to unlock AI’s potential securely This is a major statement of intent from the Government: agencies are expected to lean in, not sit back on AI. My thoughts: 📄 Responsible AI policy overhaul coming: The current policy was fairly light. Expect an update by year’s end to embed clearer accountability, risk management and governance expectations. 🔨 Use-case-level governance: I’ve long argued that AI governance works best at the use-case level, not the system level. The Government agrees. The approach appoints accountable officers for use cases, which is the kind of granularity needed for real accountability. 👀 Central oversight: An AI Review Committee will scrutinise higher-risk use cases. This creates a feedback loop that allows lessons, failures and fixes to be shared across government rather than buried in individual agencies. It’s a smart step toward building consistency and collective trust. 💪 Massive capability uplift: Every public servant will receive foundational AI literacy training and rightly so. An AI tool is only as good as the hands it’s in, and training must cover responsible use AND effective use. 📡 Trust through communication. The plan directly acknowledges Australia’s trust gap on AI and puts communication and engagement at the core. 📶 A new benchmark for industry. A whole-of-government AI governance framework like this could very well become the de facto standard for everyone doing business with government and beyond. Requirements will inevitably flow through supply chains. Big picture: the aim is to boost service delivery, policy outcomes and productivity while fostering public trust. That’s the right balance: adopt AI boldly, but govern it deeply. Make no mistake, this is a big step for responsible AI in the APS. #AI #AIGovernance #ResponsibleAI #ArtificialIntelligence #TrustworthyAI

AI regulation isn’t settling, it’s reacting. And the reaction? Fragmented, global and and driven by public tension. Europe: The landmark AI Act is already under review. Why? Industry pushback. Now, the EU is signalling it may ease compliance and reduce red tape. United States: The proposed “AI Diffusion Rule” was pulled just before rollout. The focus has shifted from enforcement to diplomacy. China: Governance is tightening. The details remain unclear, but the intent is unmistakable: more control. It might seem like regulation is shaped only by politics, policy, and industry pressure. But now add the ethical and public concern layer. You don’t need expert analysis. Just read the headlines: →The New York Times is suing OpenAI over training data and copyright boundaries. →A GDPR complaint accuses ChatGPT of generating false, defamatory information. →A U.S. federal judge ordered OpenAI to preserve all ChatGPT outputs, marking a legal shift in how AI content is treated. Three regions. Three agendas. But one emerging pattern: → Public tension surfaces first, whether political, economic, or ethical. → Legal systems scramble to respond. → Governance becomes the tool to contain the risk. So what does this mean for leaders building with AI? If your strategy skips ethical alignment, regulation will catch you off guard. Ethics builds trust. And to navigate today’s grey areas and stay ready for shifting governance, you need to build with adaptability, documentation, and decision traceability in mind. Ethics is the why. Governance is the how. And both are becoming non-negotiable. 👇 How are you preparing for this dual front, ethical accountability and regulatory complexity? Sources in comments