How data ethics build and break trust

Why are you ignoring a crucial factor for trust in your AI tool? By overlooking crucial ethical considerations, you risk undermining the very trust that drives adoption and effective use of your AI tools. Ethics in AI innovation ensures that technologies align with human rights, avoid harm, and promote equitable care. Building trust with patients and healthcare practitioners alike. Here are 12 important factors to consider when working towards trust in your tool. Transparency: Clearly communicating how AI systems operate, including data sources and decision-making processes. Accountability: Establish clear lines of responsibility for AI-driven outcomes. Bias Mitigation: Actively identifying and correcting biases in training data and algorithms. Equity & Fairness: Ensure AI tools are accessible and effective across diverse populations. Privacy & Data Security: Safeguard patient data through encryption, access controls, and anonymization. Human Autonomy: Preserve patients’ rights to make informed decisions without AI coercion. Safety & Reliability: Validate AI performance in real-world clinical settings. And test AI tools in diverse environments before deployment. Explainability: Design AI outputs that clinicians can interpret and verify. Informed Consent: Disclose AI’s role in care to patients and obtain explicit permission. Human Oversight: Prevent bias and errors by maintaining clinician authority to override AI recommendations. Regulatory Compliance: Adhere to evolving legal standards for (AI in) healthcare. Continuous Monitoring: Regularly audit AI systems post-deployment for performance drift or new biases. Address evolving risks and sustain long-term safety. What are you doing to increase trust in your AI tools?

AI killed trust in 37% of companies last year. Not because the technology failed. Because leaders did. I watched a $4B healthcare company deploy AI that made perfect business sense. Saved $12M annually. Improved efficiency 3x. Six months later, some of their best people had quit. The AI worked flawlessly. The trust? Gone. Here's what actually happened: Monday morning all-hands: "We're implementing AI to augment our workforce." What employees heard: "We're replacing you." No context. No conversation. No consideration for the humans who'd built the company. Leadership promised "full transparency" about AI's role. Then held closed-door meetings about "workforce optimization." Then wondered why rumors spread faster than facts. What Trust Actually Looks Like in the AI Era: 1. Start With (The Human) Why → Not "AI will save us money." → But "AI will handle repetitive tasks so you can do work that matters." → Show people their future, not their replacement. 2. Co-Create The Change The companies succeeding with AI? They involved employees from day one. → Engineers helped design AI workflows → Customer service shaped AI responses   → Sales teams defined AI boundaries 3. Address The Fear Directly → "Will AI take my job?"  → Stop dodging. Answer honestly: "AI will change your job. Here's exactly how. Here's what we're doing to ensure you thrive in that change." 4. Invest in Humans First → One client spent $3M on AI, $3M on employee development. → Result: 94% adoption rate, zero key talent loss. → The math is simple: Trust is cheaper than turnover. Trust Multipliers That Work: Radical Transparency → Share AI decision criteria → Show which tasks AI handles → Publish success AND failure metrics Skills Guarantee → "We'll invest in your growth" → Paid learning time → Clear career pathways with AI Human-First Policies → No AI decisions about people without human review → Employees can challenge AI recommendations → Ethics committee with actual employee representation The Trust Killers to Avoid: → Surprise AI deployments → "Trust us" without evidence → Talking efficiency while planning layoffs → Treating AI resistance as ignorance The Counter-Intuitive Truth: Companies that prioritize trust over technology are winning with AI. Here's what I've learned from watching multiple AI transformations: Trust isn't the soft stuff. It's the hard requirement. You can have perfect AI with zero trust = failure. You can have basic AI with high trust = transformation. AI without trust isn't transformation. It's just expensive automation that your best people will leave behind. The choice is yours: Build trust first, or rebuild your team later. What's your biggest AI trust challenge? Share below 👇 ♻️ Repost if your someone in your network needs this message (thank you!) Follow Carolyn Healey for more AI transformation insights.

The Future Isn’t Data-Driven, It’s Ethics-Driven. Everyone’s racing to become “data-driven.” But here’s the real question: What happens when we drive with no brakes? Recently, we’ve seen what that looks like: ↳ Predictive policing tools targeting minority neighborhoods. ↳ Healthcare algorithms denying access based on flawed historical data. ↳ Hiring software that filters out women and minority candidates. These aren’t just glitches. They’re the consequence of ignoring ethics. ↦ Data without ethics is a ticking time bomb. Being first to adopt AI doesn’t mean much if you can’t earn public trust. And trust is the new metric of success. The organizations winning today are doing more than innovating. They’re embedding ethical frameworks into every data decision. ⇨ They prioritize transparency. ⇨ They build diverse teams to avoid blind spots. ⇨ They welcome regulation - because they’re already setting the bar. If you're leading in data or AI, here’s your roadmap: Transparency: Make your data practices visible. Accountability: Define who’s responsible when things go wrong. Inclusion: Build teams that reflect the communities you serve. It’s no longer enough to just collect and analyze data. We need leaders who question the impact. Who chooses values over velocity. Who asks, “Just because we can, should we?” The next wave of innovation won’t just be data-driven. It will be ethics-driven. And the future belongs to those who get this right. How are you embedding ethics into your work? Let’s learn from each other in the comments.

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering

Imagine if someone took your diary. Not to expose you, but to study you. Quietly. And without telling you. That’s what just happened in Denmark: Three and a half million hospital records, including psychiatric notes, were handed over to an AI research project without the patients ever being informed. Legally, it’s allowed. Ethically, however, it’s problematic. We're not talking about neutral data points here. These records contain moments of fear, illness, and vulnerability. They are words spoken to a doctor in trust. Of course, you can pseudonymize them. You can follow the law. However, you cannot strip away the duty to treat people as ends in themselves. Consent is not a formality. It's about dignity. I believe the greatest risk here is the undermining of trust. Once trust in the health system is gone, the consequences will be measured by the silence of those who no longer seek help. #AIethics #DataPrivacy #TechPhilosopher – – – 🧭 I write about AI, ethics, and why trust and dignity must be at the core of technology. Follow me here for more: Sune Selsbæk-Reitz

I froze for a minute when a client asked me “How do I know my data is safe with you?” Not because I didn’t have an answer But because I knew words alone wouldn’t be enough. After all, trust isn’t built with promises. It’s built with systems. Instead of just saying, “Don’t worry, your data is safe,” I did something different. I showed them: 👉 NDAs that legally protected their information 👉 Strict access controls (only essential team members could ) 👉 Encrypted storage and regular security audits 👉 A proactive approach—addressing risks before they became problems Then, I flipped the script. I told them- “You’re not just trusting me, you’re trusting the systems I’ve built to protect you” That changed everything. → Clients didn’t just feel comfortable—they became loyal. → Referrals skyrocketed because trust isn’t something people keep to themselves. → My business became more credible. And the biggest lesson? 👉 Security isn’t just a checkbox. It’s an experience. Most businesses treat data protection as a technical issue. But it’s an emotional one. When clients feel their information is safe, they don’t just stay. They become your biggest advocates. PS: How do you build trust with your clients?

Are you familiar with the term “𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜?” What does it mean to you? Let me share an example of what it 𝙞𝙨𝙣’𝙩. Trust is the foundation of every good relationship, to include the relationship between businesses and their customers. As companies increasingly integrate AI into customer interactions, they have a choice to use AI to 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝘁𝗿𝘂𝘀𝘁 𝗼𝗿 𝗲𝗿𝗼𝗱𝗲 𝗶𝘁. Most of the women I know dread car shopping, and I’m no exception. Luckily, I have a son willing to send me links to my potential next car. After deleting 𝙝𝙞𝙨 dream sports cars from the top, it was a pretty good list, so I was ready to send a few questions to dealers. After inquiring about one certain vehicle, "Jessica Jones," texted with an offer to provide more details and schedule a visit. A short time later, "Joseph" texted from a different mobile number with a similar offer. He was associated with the same dealer as “Jessica.” Curious, I asked Jessica if she and Joseph worked together. Her reply text was slightly off, but I live in an area where many people speak English as their second language. The next text didn’t answer my question, but repeated another version of the sentence “Let me know if you need help.” So, I asked “Jessica” directly: "𝘼𝙧𝙚 𝙮𝙤𝙪 𝙖 𝙥𝙚𝙧𝙨𝙤𝙣 𝙤𝙧 𝙖 𝙗𝙤𝙩?" “Jessica” assured me she was a 𝗿𝗲𝗮𝗹 𝗽𝗲𝗿𝘀𝗼𝗻 here to assist me. Immediately after, I received another text clarifying that “Jessica” was actually the dealership's AI scheduling bot and Joseph was a person. The problem here isn’t AI. It’s 𝗱𝗲𝗰𝗲𝗽𝘁𝗶𝗼𝗻. When companies deliberately program AI to sound human and even deny being a bot, they aren’t building trust—they’re breaking it. And as AI-powered interactions become more common in everything from customer service to companionship, businesses and the boards providing oversight need to be asking a critical question: 𝘼𝙧𝙚 𝙮𝙤𝙪 𝙪𝙨𝙞𝙣𝙜 𝘼𝙄 𝙩𝙤 𝙚𝙣𝙝𝙖𝙣𝙘𝙚 𝙧𝙚𝙡𝙖𝙩𝙞𝙤𝙣𝙨𝙝𝙞𝙥𝙨, 𝙤𝙧 𝙖𝙧𝙚 𝙮𝙤𝙪 𝙢𝙞𝙨𝙡𝙚𝙖𝙙𝙞𝙣𝙜 𝙩𝙝𝙚 𝙫𝙚𝙧𝙮 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧𝙨 𝙮𝙤𝙪 𝙬𝙖𝙣𝙩 𝙩𝙤 𝙨𝙚𝙧𝙫𝙚? AI, when used ethically, can be an incredible tool for improving efficiency, responsiveness, and customer experience. But honesty should never be sacrificed in the process. People don’t mind AI—they mind being deliberately 𝙛𝙤𝙤𝙡𝙚𝙙 by it. Am I wrong? #AI #EthicalAI #ResponsibleAI #Trust #CustomerExperience #ArtificialIntelligence #BoardLeadership #CorporateGovernance #Oversight #Technology #DigitalTransformation

𝗪𝗲 𝗰𝗮𝗻’𝘁 𝘁𝗿𝘂𝘀𝘁 𝗔𝗜 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝘂𝗻𝘁𝗶𝗹 𝗶𝘁’𝘀 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻. We’re pushing AI deeper into patient care, but are we building it on ethical ground? . . . HIPAA & CCPA offer a start, but they weren’t built for algorithms that learn, adapt, and decide. 𝗪𝗛𝗘𝗥𝗘 𝗔𝗜 𝗙𝗔𝗟𝗟𝗦 𝗦𝗛𝗢𝗥𝗧 → Bias in training = unequal care → Black-box models = no clinical trust → Consent models = outdated for AI complexity → No clear liability when AI goes wrong 𝗪𝗛𝗔𝗧’𝗦 𝗡𝗘𝗘𝗗𝗘𝗗? → Explainability: Patients & doctors deserve to understand → Privacy: De-identification + consent that adapts over time → Accountability: Clear guardrails, roles & recourse → Fairness: Diverse data, bias audits, human oversight 𝗪𝗛𝗢 𝗣𝗟𝗔𝗬𝗦 𝗔 𝗣𝗔𝗥𝗧? → 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: Build for equity, not just accuracy → 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀: Keep humans in the loop → 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘀: Move faster than the tech If trust breaks, adoption fails. If ethics lead, AI actually delivers. 𝗬𝗢𝗨𝗥 𝗧𝗔𝗞𝗘? → What ethical issue in AI keeps you up at night? → What’s the most overlooked ethical risk in AI today? 👇 Let’s build AI that earns trust, not just headlines. #EthicalAI #HIPAA #CCPA #AIinHealthcare #HealthData #PatientPrivacy #TechlingHealthcare #ResponsibleAI

Your data knows more about you than you think and it's not just about privacy We all know our phones track us. But few of us realise what that data really says—and what companies can do with it. In this episode of Lancefield on the Line, I speak with Professor Sandra Matz, psychologist, data scientist and author of Mindmasters, about the surprising power and peril of our digital footprints. This is one of the most stimulating and disturbing conversations I’ve had on the show. Here are the top five takeaways: 1. Digital footprints go beyond what you post; they include everything from your GPS data to how often your phone runs out of battery. 2. Machine learning can infer your personality, values, and mental health from these "behavioural residues", often more accurately than people close to you. 3. There are significant benefits, such as early detection of emotional distress or AI-powered mental health support when used ethically. 4. Federated learning is a game-changer, allowing AI to help you without harvesting your data. Trust becomes built-in, not blind. 5. The Evil Steve Test is essential for leaders; if your data practices were used by someone with bad intentions, would they still feel ethical? This conversation will make you think differently about your phone, your organisation, and yourself. Tune in and explore how to lead and live with greater ethical awareness in the age of data.