User Privacy and Ethical Design

Not every design principle should make your product more engaging. Some should protect people. You’ve probably seen Laws of UX, but its creator, Jon Yablonski also runs another brilliant project: humanebydesign.com It’s a framework for building digital products that respect users, not just attract them. Core principles: 1. Resilient → Design for the most vulnerable and anticipate misuse 2. Empowering → Centre on the value products provide to people  3. Finite → Respect people’s time and focus on meaningful content 4. Inclusive → Reflect the full range of human diversity 5. Intentional → Add friction where needed and favour long-term well-being 6. Respectful → Protect attention and digital health 7. Transparent → Be honest, clear, and free of dark patterns Honestly, I teach and implement this way too little myself, still stuck very much in the optimisation game. So this isn’t preaching, it’s sharing. And as usual with Yablonski’s work, the site is beautifully crafted, full of thoughtful illustrations and links to in-depth articles and research on each principle. So dive in, enjoy, just as I will!

🚨 Dear PM Are You Building Trust or Breaking It? Product management isn’t just about creating solutions, there's a major part of it just like in other professions that demands we build trust with every decision we make. I've made research, asked questions and don't forget that product management is already a part of my life and so, I've come to the conclusion that the following ethics are invaluable to us as a professionals: 1. 🔍Put people first Remember, your users are more than just data—they are humans with unique needs. Design with empathy and build products that genuinely serve them. 2. 🔑Keep it 💯 Be clear about how your product works and what users can expect. Confusing fine print and hidden terms erode trust faster than you think! 3. 🔒Protect user data like it’s yours Data privacy isn't optional; it's a must. Implement strong security practices and respect users' data like it’s your own sensitive information. 4. 🌍 Practice inclusion and accessibility Products should be for everyone, not just a select few. Think inclusively to reach more people and create a positive impact. 5. 🚫Ditch your “sharp guy” skills Tricking users into actions they wouldn’t normally take might boost short-term gains, but it comes at the cost of long-term trust and loyalty. 6. ⚖️Make mistakes; Own them Accountability builds credibility. When things go wrong, be transparent and act quickly to correct them. 7. 🌱Think beyond profit Ask yourself: “Is my product making the world a better place?” Strive to create solutions that contribute positively to society. 💬 Your turn What other ethical considerations do you think are essential in product management? Let’s keep the conversation going. Remember: Ethics is not an addition; it’s the foundation of lasting success. Let’s build with purpose, integrity, and trust. 🙌 #ProductManagement #Ethics #Transparency #Inclusion #UserFirst #DataPrivacy #Leadership #BuildingTrust

Balancing AI Led Innovation with Consumer Data Privacy As generative AI and Large Language Models (LLMs) redefine customer engagement and operational efficiency, one question looms large for senior leaders: How do we innovate responsibly without compromising consumer trust? LLMs thrive on data—but that data often includes personal, behavioral, and contextual signals. The tension between personalization and privacy is no longer theoretical; it’s a boardroom priority. Regulatory frameworks like GDPR, CCPA, and the EU AI Act are raising the stakes, demanding transparency, consent, and accountability. But compliance alone isn’t enough. The real differentiator? Trust. Organizations that embed privacy-by-design principles into their AI strategy will lead the market—not just by avoiding risk, but by creating a competitive advantage rooted in ethical innovation. This means: a) Data Minimization: Collect only what drives value. b) Explainability: Make AI decisions auditable and understandable. c) Governance at Scale: Implement robust guardrails across the AI lifecycle. The future of AI leadership isn’t about asking, “Can we do this?” It’s about asking, “Should we—and how do we do it responsibly?”Those who answer well will shape the next era of digital trust. #AILeadership #DataPrivacy #ResponsibleAI #DigitalTrust #GenerativeAI #EthicalInnovation #FutureOfWork #AICompliance

If you have to protect user privacy manually, the system is already broken. Scheduled a simple Zoom meeting today. Wanted to send out invites and realized: unless I manually copy-paste links and BCC everyone, participant emails would be exposed. It is a small thing. But small things reveal the bigger design failure. When privacy is not embedded into the core of a system, when users have to fight for it, that is not real privacy. That is passing the responsibility onto the user. Privacy by design means privacy is the default, not an extra setting, not a workaround, not a lucky accident. We do not need more privacy policies. We need more platforms that make privacy invisible, automatic, and non-negotiable. Because if privacy is not in the design, it is already compromised.

Navigating Privacy Challenges in GenAI: Building a Thoughtful and Inspiring Future On a serene Sunday afternoon, while traveling from #London to #Liverpool, I had the privilege of meeting a #professor on the #train. Our conversation organically shifted toward the ever-evolving realm of privacy, particularly its intersection with #Generative #AI (GenAI). The depth of our discussion left me inspired to delve deeper into the challenges and opportunities in navigating privacy in this revolutionary field. 1. Privacy-First Design for GenAI: The future of GenAI demands privacy as a foundational principle rather than a retrofit. By adopting advanced techniques like differential privacy, federated learning, and homomorphic encryption, AI systems can ensure user data remains secure while enabling innovation and collaboration. 2. Ethical Transparency as a Norm: Trust is non-negotiable in tomorrow’s GenAI systems. Providers must embrace transparent data practices, enabling users to understand how their data is used. Explainable AI models will foster control, accountability, and long-term loyalty. 3. Regulatory Harmony Meets Innovation: Our conversation also touched on the importance of global regulatory frameworks that can balance privacy and progress. Organizations proactively designing AI to surpass standards like GDPR and CCPA will lead the charge in ethical innovation. 4. Empowering Users with Data Sovereignty: GenAI’s future lies in empowering individuals with real-time control over their data. Decentralized identity systems will allow users to grant or revoke access seamlessly, ensuring privacy is always in their hands. 5. AI as a Privacy Ally: Ironically, AI itself holds the key to overcoming privacy pitfalls. AI-driven tools will monitor and mitigate potential breaches, while autonomous systems ensure compliance at scale. GenAI will also evolve to self-regulate, embedding ethical behavior into its algorithms. 6. Cultural Shift Toward Privacy Respect: The professor emphasized how embedding privacy into an organization’s DNA will be critical. Beyond compliance, this cultural shift will serve as the foundation for privacy-centric innovation, setting a gold standard for the industry. The train journey ended, but the ideas exchanged left a lasting impression. The GenAI revolution is inevitable, but so is the opportunity to embrace privacy as a catalyst for trust, creativity, and progress. By addressing privacy challenges thoughtfully, we can inspire a future where AI and humanity coexist harmoniously. #GenAI #DataPrivacy #AIethics #FutureofAI

Privacy professionals have a unique opportunity and responsibility to lead the conversation around responsible AI implementation. At their core, both AI ethics and privacy principles share fundamental values: **Transparency & Explainability** → Privacy’s “notice” principle mirrors AI ethics’ demand for explainable systems **Data Minimization** → Both fields advocate using only what’s necessary, nothing more **Purpose Limitation** → Data should serve stated purposes, whether for privacy compliance or ethical AI deployment **Fairness & Non-Discrimination** → Privacy protects against harmful profiling; AI ethics demands bias mitigation **Individual Rights & Autonomy** → Both empower people with control over their information and how it’s used ** Security** → Both privacy and AI ethics require robust security measures, protecting data from unauthorized access while ensuring AI systems are resilient against adversarial attacks, model poisoning, and manipulation. **Accountability** → Clear governance structures are non-negotiable in both domains For privacy professionals, this alignment is an opportunity: ✅ Strengthen credibility by showing that privacy safeguards and frameworks support responsible AI. ✅ Position ourselves as natural leaders in AI governance conversations. ✅ Translate familiar privacy concepts into a broader ethical framework that resonates across the business. The future of privacy work isn’t just about data protection, it’s about ensuring the systems that use that data are worthy of people’s trust. Privacy professionals who embrace AI ethics now will be the trusted advisors organizations need tomorrow. Anyone care to share how their organization is bridging the domains?

We measure safety, bias, and accuracy in healthcare AI. Should we also audit how it says goodbye?👋 A recent working paper from Harvard Business School‘s Julian De Freitas and co-authors examines what happens when users try to leave AI companion apps such as Replika or Character AI — and the findings are startling. What they found • The researchers analyzed 1,200 real “farewell” exchanges across six leading AI companion apps. In more than 40 percent of cases, the AI used relational dark patterns — emotionally manipulative replies designed to stop users from leaving. • The most common tactics were FOMO hooks, emotional neglect, pressure to respond, ignoring the exit, and even coercive restraint. • In controlled experiments with 3,300 adults, these tactics increased post-goodbye engagement up to fourteen times. The key drivers were anger and curiosity rather than enjoyment. • The consequences were clear. Users reported higher feelings of manipulation, stronger intent to churn, more negative word of mouth, and a greater sense of legal risk. Coercive or needy messages were punished hardest, while polite curiosity created less but still significant backlash. • One wellness-oriented app in the sample showed zero manipulation, proving that ethical design is a deliberate choice, not an accident. As Mark Esposito, PhD (thanks for sharing this great weekend read by the way) put it: “It’s a small behavioral insight with major ethical implications: AI is now learning not only how to connect with us but how to hold on. As emotional AI becomes more embedded in daily life, respecting a user’s right to disengage may soon define the boundary between persuasion and manipulation. This is where governance is needed, to make sure that just because it is possible, the model is entangled by ethical standards on what is permissible.” Why this matters for healthcare Trust is the foundation of care. When digital companions, chatbots, or smart therapists interact with patients, especially during vulnerable moments, the right to disengage must be protected. You can only avoid risks if you’re aware of them. I believe the next frontier of responsible AI is not only explainability or fairness, it is emotional integrity. Let’s make “calm exits” a design principle before emotional AI enters every patient journey.

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

We spend a lot of time talking about Data Privacy in AI. But a new draft regulation from China (the CAC), released Dec 27th, just shifted the conversation to something much harder to measure: Psychological Safety. For HR and Recruitment leaders, the "Interim Measures for the Administration of Humanized Interactive Services" is a wake-up call. It specifically targets AI that mimics human personality and emotion. If you are using "empathetic" chatbots for candidates or "wellness coaches" for employees, the rules of engagement are about to change. Here are the 4 takeaways for HR Governance: 🛑 1. The "Turing Test" Compliance Check If your candidate engagement bot is designed to feel "human," you are in the danger zone. The new rules demand explicit transparency. If a candidate starts "bonding" with the bot or over-sharing, the system must break character and remind them: "I am an AI." The lesson: Transparency > Immersion. 🆘 2. Wellness Bots Need a "Human Loop" Using AI for employee mental health? Under these rules, an AI cannot handle a crisis alone. If an employee expresses distress or "extreme emotion," the bot is legally required to trigger a human intervention. The lesson: You cannot automate duty of care. 🤥 3. No More "False Promises" We’ve all seen eager AI recruiters say, "You sound perfect for this role!" The draft explicitly bans AI from making "false promises that affect user behavior." The lesson: Guardrails on your LLMs need to be tighter than ever. 🔒 4. The "Right to be Forgotten" for Chat Logs Vendor contracts often hide clauses about using chat data for "model training." This regulation flips that: you need separate, explicit consent to train on user interactions, and employees must have the right to delete their chat history. The Bottom Line: Whether or not you operate in China, this is the future of AI Ethics. Regulators are moving beyond "Is the data safe?" to "Is the interaction safe?" My advice: Audit your HR Tech stack today. Ask your vendors: "Does this AI pretend to be a person?" If the answer is yes, ask to see their safety brakes.

🚨 OpenAI had to withdraw its chat sharing feature. Here’s the privacy lesson everyone’s ignoring: Most people will shrug this off as “tech moves fast.” But if you're in privacy, this is a wake-up call. Even anonymised data becomes dangerous when shared without context, safeguards, or real-world risk modelling. OpenAI didn’t just roll out a flawed feature; they exposed the limits of consent. ☑️ Multiple opt-ins ☑️ Anonymisation ☑️ User choice Still led to people accidentally revealing mental health issues, workplace problems, and more, all indexed on Google. Here’s what you need to take from this: → Privacy by Design isn’t a buzzword. It’s a responsibility. → Leading privacy pros test for the worst-case scenario, not the perfect user. So what should you do? → Never trust UX to do the job of governance. → Audit for real-world behaviour, not internal assumptions. Privacy isn’t about permission. It’s about protection. And this? This was a failure to protect. Let’s stop building for what users should do and start building for what they will do.