Ethical Decision Making in Projects

The debate over #AI in libraries tends to be very black and white—either AI is seen as a revolutionary tool, or as a threat to our values and therefore should be banned. How should librarians approach the #EthicalDilemmas of AI in a more nuanced way? Yesterday, I had the opportunity to present "Beyond Black & White: Practical Ethics for Librarians" for the Rochester Regional Library Council (RRLC). 🔹 Key Takeaways: The Three Major Ethical Frameworks offer different ways to think about AI ethics: #Deontological Ethics considers whether actions are inherently right or wrong, regardless of the consequences. #Consequentialist Ethics evaluates decisions based on their outcomes, aiming to maximize benefits and minimize harm. #Virtue Ethics focuses on moral character and the qualities that guide ethical decision-making. These frameworks highlight that AI ethics isn’t black and white—decisions require navigating trade-offs and ethical tensions rather than taking extreme positions. I developed a 7-Step Ethical AI Decision-Making #Framework to provide a structured approach to balancing innovation with responsibility: 1️⃣ Identify the Ethical Dilemma – Clearly define the ethical issue and its implications. 2️⃣ Gather Information – Collect relevant facts, stakeholder perspectives, and policy considerations. 3️⃣ Apply the AI Ethics Checklist – Evaluate the situation based on core ethical principles. 4️⃣ Evaluate Options & Trade-offs – Assess different approaches and weigh their potential benefits and risks. 5️⃣ Make a Decision & Document It – Select the best course of action and ensure transparency by recording the rationale. 6️⃣ Implement & Monitor – Roll out the decision in a controlled manner, track its impact, and gather feedback. 7️⃣ Follow the AI Ethics Review Cycle – Continuously reassess and refine AI strategies to maintain ethical alignment. 💡 The discussion was lively, with attendees raising critical points about AI bias, vendor-driven AI implementations, and the challenge of integrating AI while protecting intellectual freedom. Libraries must engage in AI discussions now to ensure that AI aligns with our professional values while collaborating with vendors to encourage ethical AI development.

The biggest AI ethics mistake I've seen? Getting the timing wrong.🤦♀️   I've spent the last couple of years watching companies rush to implement AI without understanding the lifecycle.   Teams spend months building sophisticated models only to discover ethical issues during deployment that require complete redesigns.   Want to avoid this nightmare? One simple rule:   𝗘𝘁𝗵𝗶𝗰𝘀 𝗿𝗲𝘃𝗶𝗲𝘄 𝗶𝘀 𝗦𝗧𝗘𝗣 𝗧𝗪𝗢 in the AI lifecycle.   Look at the diagram below. Ethics review happens immediately after problem formulation (Step 1) and before any technical work begins (Steps 3-19).   Why so early?   Because once you start technical implementation, ethical issues get coded into your system's DNA. By Step 15 (deployment), fixing these problems becomes exponentially more expensive.   𝗪𝗵𝗼 𝘀𝗵𝗼𝘂𝗹𝗱 𝗱𝗼 𝘁𝗵𝗲 𝗿𝗲𝘃𝗶𝗲𝘄? Professional ethicists (not just your technical team). Representatives from affected communities. Stakeholders who'll use or be impacted by the system.   𝗪𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝘁𝗵𝗲𝘆 𝗲𝘅𝗮𝗺𝗶𝗻𝗲? Problem formulation (the challenge to be addressed leveraging AI); Data selection and representation; Potential impacts across communities; Security risks in preliminary design.   Examine your current AI projects today: is ethics review positioned at Step 2, or are you building on unstable ground?

The Dark Ethics of Change: When Motivation Becomes Manipulation I recently heard about a financial transformation where leadership deliberately withheld information about workforce impacts until after key milestones were achieved. Their rationale? "We needed to maintain momentum." This got me thinking about the ethical boundaries we navigate as change practitioners. 🩷 The Ethical Tension at the Heart of Change Every transformation lives in the space between two realities: - We genuinely believe the change will benefit the organization long-term - We know there will be disruption, discomfort, and potential downsides for some How we navigate this tension defines the ethical character of our change practice. 🎭 When Influence Becomes Manipulation There's a spectrum of change tactics, from transparent influence to outright manipulation: Transparent Influence: - Full disclosure of known impacts - Clear articulation of both benefits and costs - Genuine invitation for input that can alter approach The Grey Zone: - Selective information sharing ("need to know" basis) - Strategic messaging that emphasizes positives - Creating artificial urgency - Using social proof to drive compliance Potential Manipulation: - Deliberately concealing negative impacts - Exaggerating consequences of not changing - Leveraging fear or employment insecurity - Dismissing legitimate concerns as "resistance" 🤫 The Power Imbalance We Don't Discuss As change leaders, we hold significant information asymmetry – we know more about the change than those impacted. This creates an ethical responsibility often overlooked in OCM methodologies. Change management isn't just about achieving outcomes; it's about how we achieve them. ❓ Questions Every Change Leader Should Ask Before your next transformation message or intervention, consider: 1. Would I be comfortable if our full change strategy was transparent to all? 2. Am I withholding information that would impact informed decision-making? 3. Does my messaging respect the agency and dignity of those affected? 4. Would I consider these tactics fair if applied to me or my family? 📋 Beyond Compliance to Ethical Change The most respected organizations are moving beyond "get it done at all costs" to change approaches that honor transparency, even when difficult: - Co-creating change approaches with those most affected - Establishing ethical boundaries in change plans - Creating psychological safety for surfacing genuine concerns - Measuring not just adoption but also the human impact The most successful technology transformations I've experienced began with leadership publicly acknowledging: "We don't have all the answers, and some of what we try won't work." Where does your change practice fall on the ethics spectrum? Have you witnessed tactics that crossed the line from influence to manipulation? #ChangeManagement #OrganizationalEthics #LeadershipEthics #ChangeLeadership #Transformation

AI is moving faster than our ethics. The question is — are we keeping up? Every day, I see businesses rushing to adopt AI — automating tasks, scaling insights, optimizing decisions. But very few are stopping to ask the most important question: “Is our AI aligned with our values?” Because let’s be real — AI isn’t just a technology revolution. It’s a leadership test. A test of trust, fairness, and human judgment in the age of machines. That’s why I believe the next generation of successful companies will not just be AI-driven, They’ll be ethically AI-driven. Here’s how leaders can make that happen — step by step: 1️⃣ Build diverse teams. AI learns from people. So the more perspectives in your team, the fewer blind spots in your models. 2️⃣ Conduct regular bias audits. Bias doesn’t disappear — it hides. Make audits part of your AI lifecycle to catch issues early. 3️⃣ Stay transparent. People trust what they understand. Create clear policies on data use and AI decisions — and communicate them often. 4️⃣ Train for ethics, not just tech. Make every employee understand why ethics matter in AI. Real-world case studies make it stick. 5️⃣ Keep humans in the loop. AI should assist, not replace, human judgment — especially in critical or emotional decisions. 6️⃣ Consult the experts. Work with ethicists, regulators, and academia to stay aligned with evolving best practices. 7️⃣ Establish governance. Form an AI Ethics Committee to ensure all projects align with your company’s values. Because at the end of the day, ethical AI isn’t a checklist. It’s a culture. It’s how you ensure that what your company builds doesn’t just make profits — it makes progress. As a leader, ask yourself: If your AI decisions were public tomorrow, would you still be proud of them today? That’s the true test of leadership in the age of AI.

Project Managers: It’s time to face a hard truth. For too long, we’ve been taught that neutrality is the gold standard — to “stay objective,” to “focus on deliverables,” and to “steer clear of politics.” But neutrality isn’t protection. It’s complicity. In my latest piece, I make a powerful case: In a world grappling with climate collapse, inequality, and systemic injustice, project managers can’t afford to hide behind neutrality anymore. Every project we deliver leaves a legacy — the real question is, are we shaping that legacy with intention and ethical clarity, or simply reinforcing the status quo? Leadership today demands more than efficiency; it demands courage. It demands that we move beyond objectivity myths and step fully into ethical, regenerative leadership. This isn’t about politicizing projects — it’s about owning the real impact of our work. About redefining success to include dignity, justice, and sustainability. About being active agents of positive change. If you believe the future deserves better — if you’re ready to lead with purpose — this is a must-read. 👉 https://lnkd.in/gtkZgxf6 #ProjectManagement #EthicalLeadership #Sustainability #RegenerativeLeadership #ClimateAction #GPM