Importance of Ethical AI for Building Trust

In the rapidly advancing world of AI, the responsibility to build ethical and trusted products lies heavily on the shoulders of AI Product Leaders. Inspired by Radhika Dutt 's "Radical Product Thinking," this article argues for the adoption of a Hippocratic Oath for AI Product Management—a commitment to prioritize user well-being, transparency, and long-term value over short-term gains. This approach is essential for balancing the often competing demands of profit and purpose, ensuring that AI products not only innovate but also protect and enhance human life. During a consulting engagement with an AI Robotic Toy Companion company, I was challenged to create a practical solution ("walk the talk") that embodies Responsible AI. When I reviewed the warranty statement for the toy, I was inspired to go further by creating a Human Warranty statement and an allied Hippocratic Oath for the AI Toy Companion product, as well as for the AI-powered Mental Health Management app I am developing. These principles ensure that the AI Systems we build are not only functional but also safe, ethical, and centered on human welfare. The proposed Human Warranty Declaration, coupled with a Hippocratic Oath for AI Product Leaders, offers a framework for fostering trust, mitigating risks, and setting new industry standards for responsible AI development. By embracing these commitments, AI Product Leaders can ensure that their innovations truly serve humanity's best interests while positioning themselves as leaders in ethical AI. This is more than just a moral imperative—it's a strategic advantage in an age where trust in technology is paramount. #AIProductManagement #ResponsibleAI #EthicalAI #HippocraticOath #HumanWarranty #RadicalProductThinking #AIProductLeaders #AIInnovation #AILeadership 

🔍 Ethics in AI for Healthcare: The Foundation for Trust & Impact As AI transforms healthcare, from diagnostics to clinical decision-making, ethics must be at the center of every advancement. Without strong ethical grounding, we risk compromising patient care, trust, and long-term success. 💡 Why ethics matter in healthcare AI: ✅ Patient Safety & Trust: AI must be validated and monitored to prevent harm and ensure clinician and patient confidence. ✅ Data Privacy: Healthcare data is highly sensitive, ethical AI demands robust privacy protections and responsible data use. ✅ Bias & Fairness: Algorithms must be stress-tested to avoid reinforcing disparities or leading to unequal care outcomes. ✅ Transparency: Clinicians and patients deserve to understand why AI makes the decisions it does. ✅ Accountability: Clear lines of responsibility are essential when AI systems are used in real-world care. ✅ Collaboration Over Competition: Ethical AI thrives in open ecosystems, not in siloed, self-serving environments. 🚫 Let’s not allow hype or misaligned incentives to compromise what matters most. As one physician put it: “You can’t tout ethics if you work with organizations that exploit behind the scenes.” 🤝 The future of healthcare AI belongs to those who lead with integrity, transparency, and a shared mission to do what’s right, for patients, for clinicians, and for the system as a whole. #AIinHealthcare #EthicalAI #HealthTech

Should we really trust AI to manage our most sensitive healthcare data? It might sound cautious, but here’s why this question is critical: As AI becomes more involved in patient care, the potential risks—especially around privacy and bias—are growing. The stakes are incredibly high when it comes to safeguarding patient data and ensuring fair treatment. The reality? • Patient Privacy Risks – AI systems handle massive amounts of sensitive information. Without rigorous privacy measures, there’s a real risk of compromising patient trust. • Algorithmic Bias – With 80% of healthcare datasets lacking diversity, AI systems may unintentionally reinforce health disparities, leading to skewed outcomes for certain groups. • Diversity in Development – Engaging a range of perspectives ensures AI solutions reflect the needs of all populations, not just a select few. So, what’s the way forward? → Governance & Oversight – Regulatory frameworks must enforce ethical standards in healthcare AI. → Transparent Consent – Patients deserve to know how their data is used and stored. → Inclusive Data Practices – AI needs diverse, representative data to minimize bias and maximize fairness. The takeaway? AI in healthcare offers massive potential, but only if we draw ethical lines that protect privacy and promote inclusivity. Where do you think the line should be drawn? Let’s talk. 👇

✳ Bridging Ethics and Operations in AI Systems✳ Governance for AI systems needs to balance operational goals with ethical considerations. #ISO5339 and #ISO24368 provide practical tools for embedding ethics into the development and management of AI systems. ➡Connecting ISO5339 to Ethical Operations  ISO5339 offers detailed guidance for integrating ethical principles into AI workflows. It focuses on creating systems that are responsive to the people and communities they affect. 1. Engaging Stakeholders  Stakeholders impacted by AI systems often bring perspectives that developers may overlook. ISO5339 emphasizes working with users, affected communities, and industry partners to uncover potential risks and ensure systems are designed with real-world impact in mind. 2. Ensuring Transparency  AI systems must be explainable to maintain trust. ISO5339 recommends designing systems that can communicate how decisions are made in a way that non-technical users can understand. This is especially critical in areas where decisions directly affect lives, such as healthcare or hiring. 3. Evaluating Bias  Bias in AI systems often arises from incomplete data or unintended algorithmic behaviors. ISO5339 supports ongoing evaluations to identify and address these issues during development and deployment, reducing the likelihood of harm. ➡Expanding on Ethics with ISO24368  ISO24368 provides a broader view of the societal and ethical challenges of AI, offering additional guidance for long-term accountability and fairness. ✅Fairness: AI systems can unintentionally reinforce existing inequalities. ISO24368 emphasizes assessing decisions to prevent discriminatory impacts and to align outcomes with social expectations.  ✅Transparency: Systems that operate without clarity risk losing user trust. ISO24368 highlights the importance of creating processes where decision-making paths are fully traceable and understandable.  ✅Human Accountability: Decisions made by AI should remain subject to human review. ISO24368 stresses the need for mechanisms that allow organizations to take responsibility for outcomes and override decisions when necessary. ➡Applying These Standards in Practice  Ethical considerations cannot be separated from operational processes. ISO24368 encourages organizations to incorporate ethical reviews and risk assessments at each stage of the AI lifecycle. ISO5339 focuses on embedding these principles during system design, ensuring that ethics is part of both the foundation and the long-term management of AI systems. ➡Lessons from #EthicalMachines  In "Ethical Machines", Reid Blackman, Ph.D. highlights the importance of making ethics practical. He argues for actionable frameworks that ensure AI systems are designed to meet societal expectations and business goals. Blackman’s focus on stakeholder input, decision transparency, and accountability closely aligns with the goals of ISO5339 and ISO24368, providing a clear way forward for organizations.

"this position paper challenges the outdated narrative that ethics slows innovation. Instead, it proves that ethical AI is smarter AI—more profitable, scalable, and future-ready. AI ethics is a strategic advantage—one that can boost ROI, build public trust, and future-proof innovation. Key takeaways include: 1. Ethical AI = High ROI: Organizations that adopt AI ethics audits report double the return compared to those that don’t. 2. The Ethics Return Engine (ERE): A proposed framework to measure the financial, human, and strategic value of ethics. 3. Real-world proof: Mastercard’s scalable AI governance and Boeing’s ethical failures show why governance matters. 4. The cost of inaction is rising: With global regulation (EU AI Act, etc.) tightening, ethical inaction is now a risk. 5. Ethics unlocks innovation: The myth that governance limits creativity is busted. Ethical frameworks enable scale. Whether you're a policymaker, C-suite executive, data scientist, or investor—this paper is your blueprint to aligning purpose and profit in the age of intelligent machines. Read the full paper: https://lnkd.in/eKesXBc6 Co-authored by Marisa Zalabak, Balaji Dhamodharan, Bill Lesieur, Olga Magnusson, Shannon Kennedy, Sundar Krishnan and The Digital Economist.

74% of business executives trust AI advice more than their colleagues, friends, or even family. Yes, you read that right. AI has officially become the most trusted voice in the room, according to recent research by SAP. That’s not just a tech trend — that’s a human trust shift. And we should be paying attention. What can we learn from this? 🔹 AI is no longer a sidekick. It’s a decision-maker, an advisor, and in some cases… the new gut instinct. 🔹 But trust in AI is only good if the AI is worth trusting. Blind trust in black-box systems is as dangerous as blind trust in bad leaders. So here’s what we should do next: ✅ Question the AI you trust Would you take strategic advice from someone you’ve never questioned? Then don’t do it with AI. Check its data, test its reasoning, and simulate failure. Trust must be earned — even by algorithms. ✅ Make AI explain itself Trust grows with transparency. Build “trust dashboards” that show confidence scores, data sources, and risk levels. No more “just because it said so.” ✅ Use AI to enhance leadership, not replace it Smart executives will use AI as a mirror — for self-awareness, productivity, communication. Imagine an AI coach that preps your meetings, flags bias in decisions, or tracks leadership tone. That’s where we’re headed. ✅ Rebuild human trust, too This stat isn’t just about AI. It’s a signal that many execs don’t feel heard, supported, or challenged by those around them. Let’s fix that. 💬 And finally — trust in AI should look a lot like trust in people: Consistency, Transparency, Context, Integrity, and Feedback. If your AI doesn’t act like a good teammate, it doesn’t deserve to be trusted like one. What do you think? 👇 Are we trusting AI too much… or not enough? #SAPAmbassador #AI #Leadership #Trust #DigitalTransformation #AgenticAI #FutureOfWork #ArtificialIntelligence #EnterpriseAI #AIethics #DecisionMaking

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.

Prediction for 2025: orgs that apply an Ethical AI framework, communicate it, and stick to it, will win with employees and consumers.   At Top Employers Institute, we work with 2,300+ global multinational organizations through their continuous journey to truly be *Top Employers* based on the people-practices they employ. Our research team compiled data from several studies we've recently completed to form the Ethical AI Report.   Here are 5 key takeaways to keep in mind as you look to use AI at work in 2025: 1) Balance Speed and Responsibility: Ethical use of AI can help drive business success while *also* respecting employees / society, so a holistic approach needs to align AI with business strategy *and* org culture. 2) Note Opportunities and Challenges: While AI offers innovation, new business models, and improved customer experiences, org leaders must address concerns like job displacement and employee distrust: *48% of employees don’t welcome AI in the workplace. *Only 55% are confident their organization will implement AI responsibly. *61% of Gen Z believe AI will positively impact their career (the other 39% are unsure) 3) HR & Talent Teams play a Crucial Role: HR should be at the forefront of AI strategy, ensuring ethical implementation while bridging the gap between technology and human-centric work design. Here’s the Top Employers Institute Ethical AI Framework: *Human-centric: prioritize employee well-being and meaningful work (we know 93% of Top Employers utilize employee-centric work design) *Evidence-backed: use data to validate AI effectiveness. * Employ a long-term lens: consider the future impact of AI on work and society. 4) Apply Practical Steps for HR: Advocate for ethical AI and involve diverse stakeholders. Equip HR teams with AI knowledge and skills, and promote inclusion to upskill all employees for the future of work. 5) Don’t Forget Broader Societal Impact: Collaborate with other orgs / governments for ethical AI standards. Focus on upskilling society to adapt to AI-driven changes: i.e. The AI-Enabled ICT Workforce Consortium aims to upskill 95 million people over the next 10 years. Has your employer shared an ethical AI framework? And have they encouraged you to use AI at work? Comment below and I’ll direct message you the Ethcial AI Framework Report from Top Employers Institute. #BigIdeas2025

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.

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?