Incorporating Ethics Into AI Training Programs

Guidance for a more Ethical AI 💡This guide, "Designing Ethical AI for Learners: Generative AI Playbook for K-12 Education" by Quill.org, offers education leaders insights gained from Quill.org's six years of experience building AI models for reading and writing tools used by over ten million students. 🚨This playbook is particularly relevant now as educational institutions address declining literacy and math scores exacerbated by the pandemic, where AI solutions hold promise but also risks if poorly designed. The guide explains Quill.org's approach to building AI-powered tools. While the provided snippets don't detail specific tools, they highlight the process of collecting student responses and having teachers provide feedback, identifying common patterns in effective coaching. #Bias: AI models are trained on data, which can contain and perpetuate existing societal biases, leading to unfair or discriminatory outcomes for certain student groups. #Accuracy and #Errors: AI can sometimes generate inaccurate information or "hallucinate" content, requiring careful fact-checking and validation. #Privacy and #Data #Security: AI systems often collect student data, raising concerns about how this data is stored, used, and protected. #OverReliance and #Reduced #Human #Interaction: Over-dependence on AI could diminish crucial teacher-student interactions and the development of critical thinking skills. #Ethical #Use and #Misinformation: Without proper safeguards, AI could be used unethically, including for cheating or spreading misinformation. 5 takeaway #Ethical #Considerations are #Paramount: Designing and implementing AI in education requires a strong focus on ethical principles like transparency, fairness, privacy, and accountability to protect students and promote equitable learning. #Human #Oversight is #Essential: AI should augment, not replace, human educators. Teachers' expertise in pedagogy, empathy, and the ability to foster critical thinking remain irreplaceable. #AI #Literacy is #Crucial: Educators and students need to develop AI literacy, understanding its capabilities, limitations, potential biases, and ethical implications to use it responsibly and effectively. #Context-#Specific #Design #Matters: Effective AI tools should be developed with a deep understanding of educational needs and learning processes, potentially through methods like analyzing teacher feedback patterns.  Continuous Evaluation and Adaptation are Necessary: The impact of AI in education should be continuously assessed for effectiveness, fairness, and unintended consequences, with ongoing adjustments and improvements. Via Philipp Schmidt Ethical AI for All Learners https://lnkd.in/e2YN2ytY Source https://lnkd.in/epqj4ucF

Fostering Responsible AI Use in Your Organization: A Blueprint for Ethical Innovation (here's a blueprint for responsible innovation) I always say your AI should be your ethical agent. In other words... You don't need to compromise ethics for innovation. Here's my (tried and tested) 7-step formula: 1. Establish Clear AI Ethics Guidelines ↳ Develop a comprehensive AI ethics policy ↳ Align it with your company values and industry standards ↳ Example: "Our AI must prioritize user privacy and data security" 2. Create an AI Ethics Committee ↳ Form a diverse team to oversee AI initiatives ↳ Include members from various departments and backgrounds ↳ Role: Review AI projects for ethical concerns and compliance 3. Implement Bias Detection and Mitigation ↳ Use tools to identify potential biases in AI systems ↳ Regularly audit AI outputs for fairness ↳ Action: Retrain models if biases are detected 4. Prioritize Transparency ↳ Clearly communicate how AI is used in your products/services ↳ Explain AI-driven decisions to affected stakeholders ↳ Principle: "No black box AI" - ensure explainability 5. Invest in AI Literacy Training ↳ Educate all employees on AI basics and ethical considerations ↳ Provide role-specific training on responsible AI use ↳ Goal: Create a culture of AI awareness and responsibility 6. Establish a Robust Data Governance Framework ↳ Implement strict data privacy and security measures ↳ Ensure compliance with regulations like GDPR, CCPA ↳ Practice: Regular data audits and access controls 7. Encourage Ethical Innovation ↳ Reward projects that demonstrate responsible AI use ↳ Include ethical considerations in AI project evaluations ↳ Motto: "Innovation with Integrity" Optimize your AI → Innovate responsibly

On Monday, we explored bad AI ethics lessons. Today, we’re flipping them into real solutions. Earlier this week, we explored really bad ways to teach students ethical AI use (remember the “make ethics a paid elective” -- yikes 😬). Today, let’s flip those missteps into real strategies that empower students to engage with AI responsibly. 1. Make Ethics a Conversation, Not a One-Time Event Instead of a single lecture, embed discussions about AI ethics across subjects—debates in history, case studies in English, or real-world AI dilemmas in computer science. Ethics isn’t static, and neither should our teaching be. 2. Teach Students to Question AI, Not Just Use It Encourage students to challenge AI-generated outputs. Who created this data? What biases might be present? Just like media literacy, AI literacy requires skepticism and critical thinking. 3. Use Real-World Ethical Dilemmas Forget abstract policy documents—bring in relatable examples! Should AI write a college essay? Can an AI-generated image win an art contest? These discussions make ethics tangible and engaging. 4. Balance Risks and Opportunities Avoid fear by equipping students with frameworks for making responsible choices. Encourage open conversations about the risks and rewards of AI, so they feel empowered, not just policed. 5. Make Ethics a Right, Not a Privilege Ethical AI use is essential. Schools should provide all students access to meaningful AI ethics discussions, not limit them to a specialized curriculum corner. 6. A Bonus! Stacy Kratochvil's addition on Monday (which was a real one!): "Offer a 45 minute AI workshop during a “choice block” on a PD day and put it up against basketball in the gym, art with the ceramics teacher or free time to work in classrooms!" Flipped--Try to Make It the Main Event: Instead of competing with basketball and free time, schedule AI PD as a core session during the structured part of the day. When it’s positioned as essential—not optional—more teachers will engage, and the learning will stick. Your Turn: What’s working in your school regarding teaching students ethical AI use? Let’s share strategies. #BadBrainstormMonday #FlipItFriday #EthicalAI #Education #Leadership #GenerativeAI

AI literacy is now a required competency for the next generation of social workers. And it must go far beyond “how to use ChatGPT.” In social work education, we need to teach students to ask deeper questions: • How does this AI system actually work? • Who built it? For whom was it designed? Who was left out? • What data trained it—and what biases might be encoded? • How do we audit outputs for harm, accuracy, equity, and ethics? As generative AI becomes more common in case management, documentation, program evaluation, advocacy, and organizational decision-making, social work students must understand both the power and the limits of these tools. This is why I teach (and advocate for) a Social Work Education AI Integration Framework grounded in our profession’s ethics: 1. Develop Transparent AI Policies in the Syllabus Students deserve clarity about what’s allowed, what isn’t, and the ethical expectations for using AI. Transparency reduces fear, increases accountability, and builds trust. 2. Build AI Literacy & Competencies for Educators and Students From understanding training data and bias, to learning how to audit outputs, to integrating AI into research and practice. This is the new essential skillset for social work. 3. Empower Student Learning Through AI-Enhanced Assignments AI shouldn’t replace learning. It should strengthen critical thinking, practice skills, cultural humility, and reflective capacity. 4. Advocate for Technology Justice Social workers must ensure AI is safe, equitable, inclusive, accessible, and aligned with social justice values, not just market interests. This includes questioning profit motives, data extraction, and who benefits. At the center of it all must be the Ethical & Responsible AI Integration in the Classroom. Grounded human-centered practice, anti-oppressive frameworks, and the commitment to do no harm. When we teach AI through this lens, we aren’t just preparing students to use new tools. We are preparing them to lead, advocate, and shape the future of technology in service of communities. Social workers belong at the table where AI is being built. And our students deserve the skills to sit in those seats, confidently and critically. If you’re building curriculum, training faculty, or designing field guidance for AI literacy in social work, I’d love to connect. #SocialWork #SocialWorkEducation #MSWStudents #FieldEducation #SocialWorkers #AILiteracy #AIEthics #ResponsibleAI #AIandSocialWork #AIinEducation #EthicalAI #TechJustice #DigitalEquity #AntiOppressivePractice #EquityInEducation #FutureOfWork #HigherEducation #EdTech #InnovationInEducation #TheAISocialWorker

How and to what extent can ethical theories guide the design of AI systems? This is the question I'd like to tackle in this week's #sundAIreads. The reading I chose for this is "Ethics of AI: Toward a Design for Values Approach" by Stefan Buijsman, Michael Klenk, and jeroen van den hoven from the Delft University of Technology. It's a chapter in The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence, which is available open access here: https://lnkd.in/dmP7hBnJ. The authors argue that familiar ethical theories such as virtue ethics ("what character traits should I cultivate?"), deontology ("which moral principles should I follow?"), and consequentialism ("what actions maximize wellbeing?") are necessary, but insufficient to guide the responsible development and deployment of #AI systems. Instead the authors advocate for a #design approach to AI ethics, which entails identifying relevant values, embedding them in AI systems, and continuously evaluating whether and to what extent these efforts were successful. Of course, this is easier said than done. Why? Because: 1️⃣ Values come with trade-offs, e.g., #privacy versus #security or #usability. 2️⃣ Values can change, both in terms of what they mean and how important they are to people, e.g., #sustainability. 3️⃣ AI systems are socio-technical systems, i.e., AI ethics is "just as much about the people interacting with AI and the institutions and norms in which AI is employed." These challenges can be addressed by: ✅ Making trade-offs between values explicit and either trying to resolve them or at least documenting the reasoning behind why one value was chosen over the other. ✅ Designing for "adaptability, flexibility and robustness" to account for changing values over time. ✅ Considering the environment in which AI systems will be deployed, including not only the people who will use AI systems, but also those affected by their use. I first encountered the values-by-design literature during my postgraduate studies with Helen Nissenbaum at the NYU Steinhardt Department of Media, Culture, and Communication and have been a huge fan ever since. For an even more hands-on approach to translating ethical values into technical design, I recommend checking out Dr. Niina Zuber, Severin Kacianka, Alexander Pretschner, and Julian Nida-Rümelin's Ethics in Agile Software Development (EDAP) project at the Bayerisches Forschungsinstitut für Digitale Transformation (bidt) (https://lnkd.in/dNiBUxBF) and Dr Lachlan Urquhart's Moral-IT Deck (https://lnkd.in/d9J2WQNi).

I’ve been curious about flipping Bloom’s Taxonomy—today, I saw it in action, and the results were incredible. In Mr. Stangler’s 8th-grade career class, we introduced AI and Ethics in a way that put students in charge. After modeling a lesson on AI and Academic Integrity (covering effective prompting and proper citation), students were given an AI ethics topic to research using AI and teach their peers in a quick-fire activity. Topics covered: 🔹 AI & Academic Integrity 🔹 AI & Deepfakes 🔹 AI & Mental Health 🔹 AI & Bias 🔹 AI & Privacy With permission to use AI as a research tool (as long as they cited their sources), students took the challenge and ran with it. The speed rounds in the auditorium made learning fast-paced and interactive. And while this was just an introduction to AI ethics, I was blown away by how much they learned in a single class period—far more than I could have covered alone. The exit ticket feedback confirmed it—students appreciated the peer-teaching approach, and it was clear that teaching the topic gave them a much deeper understanding of AI’s ethical implications. It was so much fun working with Mr. Stangler and his students, and it has been the highlight of a very challenging year. How are you empowering students to take ownership of their learning—whether with AI, ethics, or beyond?

🧭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.

AI Ethics Before Choice: Beyond Procedural Ethics in AI Literacy AI literacy occupies a central place in institutional planning partly because it feels administratively manageable. Whatever the program, each version includes something presented as “AI ethics,” though the term usually designates a set of reductive procedures, a hygiene curriculum aimed at assuring technical correctness rather than cultivating interpretive judgment. Ethics is framed as a sequence of discrete choices made by autonomous users. That premise falters as soon as AI mediates inquiry. Student work unfolds inside environments shaped by infrastructures, datasets, institutional agreements, and platform incentives, each of which orients knowledge long before any assignment begins. These forces rarely appear in official literacy frameworks even though they shape what students come to regard as possible. María Puig de la Bellacasa offers a way to study this terrain. Her account describes a speculative ethics grounded in ongoing relations, where obligations arise from potentials embedded in evolving worlds rather than from discrete acts of choice. She uses “care” to describe the processes through which these worlds take form and through which demands emerge prior to intention. I read care as an epistemological practice that traces how perception is shaped by conditions operating before conscious engagement. Care highlights the unnoticed architectures—technical, institutional, ecological—that delimit inquiry and orient what students interpret as credible. In practice, this reframes familiar scenes. An image generator reveals how training data produces visual conventions that confer authority. Writing feedback systems lean toward stylistic habits sedimented in their corpora. Privacy guidelines point back to district contracts that narrow what appears acceptable. Verification tasks expose how datasets privilege particular lines of evidence. Prompt adjustments expose interfaces that steer reasoning along certain paths. Each example shows how inquiry arises within relations that organize what students think they are choosing. Current AI literacy programs limit themselves by casting students as procedure-followers instead of situated knowers. Their inquiries arise from environments that structure what feels possible, yet the curriculum rarely addresses these environments because they resist proceduralization. A richer pedagogy would treat care as a method for studying how obligations materialize through relations among infrastructures, institutions, ecologies, and interpretive habits. AI ethics becomes a study of how infrastructures condition perception, shaping what appears available to thought before any ethical posture can be imagined. AI literacy could evolve into an education in world-making rather than a sequence of cautions, preparing students for environments that shift through their participation and giving them ways to respond with insight rather than quiet compliance.