Building A Framework For Ethical Decision-Making

Layoffs feel unethical, but they’re not inherently wrong. It’s the behavior behind the decision that matters. I learned this early in my career. Layoffs are business decisions. They’re about numbers, markets, and strategy. What makes them unethical is when leadership lies, hides, or treats people like disposable parts. When you can’t look someone in the eye and tell the truth, that’s when you’ve crossed the line. That’s why I teach the ETHICS framework to leaders and HR folks. It’s not academic. It’s survival. It kept me grounded when the pressure was high and the choices were ugly. Evaluate. Get the facts. Who’s impacted? What’s the real story behind the spreadsheet? Don’t accept half-truths. Think. Sit with the consequences. Who gets hurt? Who gets protected? What’s the ripple effect six months from now? Honor values. Integrity isn’t a slide deck. It’s how you behave when nobody’s watching. Does this decision reflect what you say you stand for? Identify options. There are always more than leaders admit. Better severance. Clearer communication. A chance to redeploy someone into a different role. Get creative. Choose. Make the call with clarity, not cowardice. People can smell fear. They can also smell respect. Scrutinize. After it’s done, don’t bury it. What worked? What was awful? What will you refuse to repeat? Layoffs are a business failure for sure. We can and should make them fair, transparent, and respectful. That’s ethical leadership. So next time you’re in the room for a hard decision, don’t wing it. Don’t hide. Use the ETHICS framework. Stand in your values. People will forget the press release, but they’ll never forget how you made them feel when their job disappeared. https://lnkd.in/e2amCVM6

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.

🧭Governing AI Ethics with ISO42001🧭 Many organizations treat AI ethics as a branding exercise, a list of principles with no operational enforcement. As Reid Blackman, Ph.D. argues in "Ethical Machines", without governance structures, ethical commitments are empty promises. For those who prefer to create something different, #ISO42001 provides a practical framework to ensure AI ethics is embedded in real-world decision-making. ➡️Building Ethical AI with ISO42001 1. Define AI Ethics as a Business Priority ISO42001 requires organizations to formalize AI governance (Clause 5.2). This means: 🔸Establishing an AI policy linked to business strategy and compliance. 🔸Assigning clear leadership roles for AI oversight (Clause A.3.2). 🔸Aligning AI governance with existing security and risk frameworks (Clause A.2.3). 👉Without defined governance structures, AI ethics remains a concept, not a practice. 2. Conduct AI Risk & Impact Assessments Ethical failures often stem from hidden risks: bias in training data, misaligned incentives, unintended consequences. ISO42001 mandates: 🔸AI Risk Assessments (#ISO23894, Clause 6.1.2): Identifying bias, drift, and security vulnerabilities. 🔸AI Impact Assessments (#ISO42005, Clause 6.1.4): Evaluating AI’s societal impact before deployment. 👉Ignoring these assessments leaves your organization reacting to ethical failures instead of preventing them. 3. Integrate Ethics Throughout the AI Lifecycle ISO42001 embeds ethics at every stage of AI development: 🔸Design: Define fairness, security, and explainability objectives (Clause A.6.1.2). 🔸Development: Apply bias mitigation and explainability tools (Clause A.7.4). 🔸Deployment: Establish oversight, audit trails, and human intervention mechanisms (Clause A.9.2). 👉Ethical AI is not a last-minute check, it must be integrated/operationalized from the start. 4. Enforce AI Accountability & Human Oversight AI failures occur when accountability is unclear. ISO42001 requires: 🔸Defined responsibility for AI decisions (Clause A.9.2). 🔸Incident response plans for AI failures (Clause A.10.4). 🔸Audit trails to ensure AI transparency (Clause A.5.5). 👉Your governance must answer: Who monitors bias? Who approves AI decisions? Without clear accountability, ethical risks will become systemic failures. 5. Continuously Audit & Improve AI Ethics Governance AI risks evolve. Static governance models fail. ISO42001 mandates: 🔸Internal AI audits to evaluate compliance (Clause 9.2). 🔸Management reviews to refine governance practices (Clause 10.1). 👉AI ethics isn’t a magic bullet, but a continuous process of risk assessment, policy updates, and oversight. ➡️ AI Ethics Requires Real Governance AI ethics only works if it’s enforceable. Use ISO42001 to: ✅Turn ethical principles into actionable governance. ✅Proactively assess AI risks instead of reacting to failures. ✅Ensure AI decisions are explainable, accountable, and human-centered.

Everyone’s at the AI parade… but when the confetti clears, who’s left to clean up the mess? We cheer the automation. We celebrate the productivity. But when it's time to talk ethics, responsibility, and the actual impact on people - the crowd thins out. The music fades. And silence speaks volumes. We all love what we can do with AI - automate mundane tasks, optimize workflows, power personalization, generate content, make ourselves super productive. But here’s the thing: Everyone wants to use AI - whether they are doing it the right way is questionable. Very few understand how to take the responsibility of using the content without questioning, reasoning. When the conversations shifts from automation to ethics From performance to accountability From outputs to outcomes  Things get quiet. The real work isn’t in using AI.  It’s in making sure that information is correct. It serves people and not just processes. It’s in asking hard questions, and staying in the room when answers become uncomfortable. 🎯 The Responsible AI Leader's Roadmap (5 Steps to Implement in Your Org) Step 1: Start with the "Why" - Document your AI objectives - Map them to human needs, not just process efficiency - Get stakeholder alignment on success metrics Step 2: Build Your Ethics Framework - Create clear guidelines for AI use - Define accountability measures - Establish regular review cycles Step 3: Prioritize Trust & Transparency - Communicate openly about AI capabilities - Document decision-making processes - Make outcomes traceable and explainable Step 4: Train Your Teams - Educate on both capabilities AND limitations - Build awareness of ethical considerations - Create clear escalation paths Step 5: Monitor & Adjust - Continuously - Track impact on people, not just performance - Regular ethics audits - Course-correct based on feedback Remember: Technology moves fast. Ethics should move faster. We don’t need more cheerleaders for AI. We need stewards. We need leaders who understand that trust is the real product—and it’s earned every day. The future of AI won’t be defined by how advanced the tech is… But by how human we choose to remain. P.S. What's one thing about the future of AI that keeps you up at night? Drop it below. 👇 ♻️ Repost to keep this conversation going—we don’t just need smarter tech, we need wiser humans. ➕ Follow me (Ranjana Sharma) for more insights on leading with AI and integrity.