Principles of Human-Centered AI Development

Despite all the talks... I don’t think AI is being built ethically - or at least not ethically enough! Last week, I had lunch in San Francisco with my ex-Salesforce colleague and friend Paula Goldman, who taught me everything I know about the matter. When it comes to Enterprise AI, Paula not only focuses on what's possible - she spells out also what's responsible, making sure the latter always wins ! Here's what Paula taught me over time: 👉AI needs guardrails, not just guidelines. 👉Humans must remain at the center — not sidelined by automation. 👉Governance isn’t bureaucracy—it’s the backbone of trust. 👉Transparency isn’t a buzzword—it’s a design principle. 👉And ultimately, AI should serve human well-being, not just shareholder return The choices we make today will shape AI’s impact on society tomorrow. So we need to ensure we design AI to be just, humane, and to truly serves people. How do we do that? 1. Eliminate bias and model fairness AI can mirror and magnify our societal flaws. Trained on historical data, models can adopt biased patterns, leading to harmful outcomes. Remember Amazon’s now-abandoned hiring algorithm that penalized female applicants? Or the COMPAS system that disproportionately flagged Black individuals as high-risk in sentencing? These are the issues we need to swiftly address and remove. Organisations such as the Algorithmic Justice League - who is driving change, exposing bias and demanding accountability - give me hope. 2. Prioritise privacy We need to remember that data is not just data: behind every dataset is a real person data. Real people with real lives. Techniques like federated learning and differential privacy show we can innovate without compromising individual rights. This has to be a focal point for us as it’s super important that individuals feel safe when using AI. 3. Enable transparency & accountability When AI decides who gets a loan, a job, or a life-saving diagnosis, we need to understand how it reached that conclusion. Explainable AI is ending that “black box” era. Startups like CalypsoAI stress-test systems, while tools such as AI Fairness 360 evaluate bias before models go live. 4. Last but not least - a topic that has come back repeatedly in my conversation with Paula - ensure trust can be mutual This might sound crazy, but as we develop AI and the technology edges towards AGI, AI needs to be able to trust us just as much as we need to be able to trust AI. Trust us in the sense that what we’re feeding it is just, ethical and unbiased. And not to bleed in our own perspectives, biases and opinions. There’s much work to do, however, there are promising signs. From AI Now Institute’s policy work to Black in AI’s advocacy for inclusion, concrete initiatives are pushing AI in the right direction when it comes to ensuring that it’s ethical. The choices we make now will shape how well AI fairly serves society. What’s your thoughts on the above?

New paper out! A case study: Duolingo’s AI ethics approach and implementation. This is a rare example of real-world, detailed AI ethics implementation. ➤ Context: * There are so many AI ethics frameworks out there. Most of them are high level, abstract, and far from implementation. * That’s why I wanted to co-author this paper. * It showcases how an organization can write practical AI ethics principles and then implement them. * The case study is Duolingo English Test My fabulous co-authors are Jill Burstein, who led the paper, and Alina von Davier, Geoff LaFlair, and Kevin Yancey, all parts of Duolingo’s English Test team. ➤ The AI ethics principles: 1. Validity and reliability 2. Fairness 3. Privacy 4. Transparency and accountability ➤  The implementation The paper demonstrates how these principles are implemented using several examples: * A six-step process for writing exam questions, illustrating the validity and reliability and fairness standards * A process for detecting plagiarism that demonstrates the privacy principle * Quality assurance and documentation processes that demonstrate the accountability and transparency principle ➤ You can read a summary of the paper in the link in the comments ➤ Get in touch if you’d like to have a paper like this about your own company! #responsibleai #aiethics

Good guidance from the U.S. Department of Education to developers of education technology; focus on shared responsibility, managing risks, and bias mitigation. 🛡️ One think I really like about this document is the use-case specific guidance and examples (clearly there were industry contributors that helped facilitate that). 🎓 Key Guidance for Developers of AI in Education -------------------------------------------------- 🔍 Build Trust: Collaborate with educators, students, and stakeholders to ensure fairness, transparency, and privacy in AI systems. 🛡️ Manage Risks: Identify and mitigate risks like algorithmic bias, data privacy issues, and potential harm to underserved communities. 📊 Show Evidence: Use evidence-based practices to prove your system's impact, including testing for equitable outcomes across diverse groups. ⚖️ Advance Equity: Address discrimination risks, ensure accessibility, and comply with civil rights laws. 🔒 Ensure Safety: Protect data, prevent harmful content, and uphold civil liberties. 💡 Promote Transparency: Communicate clearly about how AI works, its limitations, and its risks. 🤝 Embed Ethics: Incorporate human-centered design and accountability throughout development, ensuring educators and students are part of the process. BABL AI has done a lot of work in the edtech space, and I can see an opportunity for us to provide assurance that some of these guidelines are being followed by companies. #edtech #AIinEducation #aiassurance Khoa Lam, Jeffery Recker, Bryan Ilg, Jovana Davidovic, Ali Hasan, Borhane Blili-Hamelin, PhD, Navrina Singh, GoGuardian, Khan Academy, TeachFX, EDSAFE AI Alliance, Patrick Sullivan

What’s the real secret to unlocking AI’s potential? Making it work for the people behind it. At RingCentral, our human-centric approach has been key to building trust, fostering innovation, and driving long-term success. But that only happens when AI is designed to empower, not overshadow, people's unique talents and perspectives. These three principles guide our people-first AI strategy: 1. Transparency: Build trust by showing your team how AI works for them—whether it’s automating repetitive tasks or enabling faster, smarter decisions. 2. Collaboration: Get your team involved early. Identify their biggest challenges and co-create solutions. AI that addresses real needs creates real impact. 3. Empowerment: Use AI to enhance—not replace—human skills and ingenuity. By putting people first, we’re creating AI solutions that don’t just drive business outcomes—they support teams, inspire growth, and enable organizations to thrive in a rapidly evolving world. As we look to 2025 and beyond, let’s lead with empathy and innovation.

I️ met the brilliant Michelle Culver about a year ago and I'm just continuously stunned by the work she and the The Rithm Project are doing to ensure we're being hyper-conscious about leveraging AI for pro-social behavior. These 5 principles are such a perfectly simple way to understand whether AI is going to make us happier or more isolated, lonely, and less functional. The future of AI isn't about replacing human connection—it's about enhancing it. Instead of AI that tries to seem human, we get transparent artificiality—systems that are honest about what they are. Instead of keeping us comfortable in echo chambers, we get productive friction—and this principle resonates deeply with me right now. Lately, I've been seeing more "AI laziness"—people throwing something into Claude, then hitting send without really analyzing if it's the product they think is strong. Our young people need their critical thinking skills to be sharp so they can leverage AI to create draft one, two, and twenty. But not just throw it in and press send. The principles around cultural affirmation and harm mitigation particularly strike me too. The best AI tools don't just solve problems—they respect our values while protecting against unintended consequences, and they strengthen our human bonds rather than replace them. What strikes me most: this isn't just about making AI "nicer." It's about designing technology that makes us more thoughtful, connected, and capable. The question isn't whether AI will change how we interact—it's whether that change will make us more human or less so. These principles suggest we can choose "more human." That's the kind of future worth building.