Understanding AI Ethics for Tomorrow

𝗧𝗵𝗲 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜: 𝗪𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗼𝗮𝗿𝗱 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 "𝘞𝘦 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘱𝘢𝘶𝘴𝘦 𝘵𝘩𝘪𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘪𝘮𝘮𝘦𝘥𝘪𝘢𝘵𝘦𝘭𝘺." Our ethics review identified a potentially disastrous blind spot 48 hours before a major AI launch. The system had been developed with technical excellence but without addressing critical ethical dimensions that created material business risk. After a decade guiding AI implementations and serving on technology oversight committees, I've observed that ethical considerations remain the most systematically underestimated dimension of enterprise AI strategy — and increasingly, the most consequential from a governance perspective. 𝗧𝗵𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 Boards traditionally approach technology oversight through risk and compliance frameworks. But AI ethics transcends these models, creating unprecedented governance challenges at the intersection of business strategy, societal impact, and competitive advantage. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Beyond explainability, boards must ensure mechanisms exist to identify and address bias, establish appropriate human oversight, and maintain meaningful control over algorithmic decision systems. One healthcare organization established a quarterly "algorithmic audit" reviewed by the board's technology committee, revealing critical intervention points preventing regulatory exposure. 𝗗𝗮𝘁𝗮 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻𝘁𝘆: As AI systems become more complex, data governance becomes inseparable from ethical governance. Leading boards establish clear principles around data provenance, consent frameworks, and value distribution that go beyond compliance to create a sustainable competitive advantage. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗜𝗺𝗽𝗮𝗰𝘁 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Sophisticated boards require systematically analyzing how AI systems affect all stakeholders—employees, customers, communities, and shareholders. This holistic view prevents costly blind spots and creates opportunities for market differentiation. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆-𝗘𝘁𝗵𝗶𝗰𝘀 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Organizations that treat ethics as separate from strategy inevitably underperform. When one financial services firm integrated ethical considerations directly into its AI development process, it not only mitigated risks but discovered entirely new market opportunities its competitors missed. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.

"On Nov 6, the UK Department for Science, Innovation and Technology (DSIT) published a first draft version of its AI Management Essentials (AIME) self-assessment tool to support organizations in implementing responsible AI management practices. The consultation for AIME is open until Jan 29, 2025. Recognizing the challenge many businesses face in navigating the complex landscape of AI standards, DSIT created AIME to distill essential principles from key international frameworks, including ISO/IEC 42001, the NIST Risk Management Framework, and the EU AI Act. AIME provides a framework to: - Evaluate current practices by identifying areas that meet baseline expectations and pinpointing gaps. - Prioritize improvements by highlighting actions needed to align with widely accepted standards and principles. - Understand maturity levels by offering insights into how an organization's AI management systems compare to best practices. AIME's structure includes: - A self-assessment questionnaire - Sectional ratings to evaluate AI management health - Action points and improvement recommendations The tool is voluntary and doesn’t lead to certification. Rather, it builds a baseline for 3 areas of responsible AI governance - internal processes, risk management, and communication. It is intended for individuals familiar with organizational governance, such as CTOs or AI Ethics Officers. Example questions: 1) Internal Processes Do you maintain a complete record of all AI systems used and developed by your organization? Does your AI policy identify clear roles and responsibilities for AI management? 2) Fairness Do you have definitions of fairness for AI systems that impact individuals? Do you have mechanisms for detecting unfair outcomes? 3) Impact Assessment Do you have an impact assessment process to evaluate the effects of AI systems on individual rights, society and the environment? Do you communicate the potential impacts of your AI systems to users or customers? 4) Risk Management Do you conduct risk assessments for all AI systems used? Do you monitor your AI systems for errors and failures? Do you use risk assessment results to prioritize risk treatment actions? 5) Data Management Do you document the provenance and collection processes of data used for AI development? 6) Bias Mitigation Do you take steps to mitigate foreseeable harmful biases in AI training data? 7) Data Protection Do you implement security measures to protect data used or generated by AI systems? Do you routinely complete Data Protection Impact Assessments (DPIAs)? 8) Communication Do you have reporting mechanisms for employees and users to report AI system issues? Do you provide technical documentation to relevant stakeholders? This is a great initiative to consolidating responsible AI practices, and offering organizations a practical, globally interoperable tool to manage AI!" Very practical! Thanks to Katharina Koerner for summary, and for sharing!

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.

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

The Decision Tree for Responsible AI is a guide developed by AAAS (American Association for the Advancement of Science) to help put ethical principles into practice when creating and using AI, and aid users and their organizations in making informed choices regarding the development or deployment of AI solutions. The DT is meant to be versatile, but may not cover every unique situation and might not always have clear yes/no answers. It's advised to continually consult the chart throughout the AI solution's development and deployment, considering the changing nature of projects. Engaging stakeholders inclusively is vital to this framework. Before using the tree, determine who is best suited to answer the questions based on their expertise. To do this, the decision tree is referring to Partnership on AI's white paper “Making AI Inclusive” (see: https://lnkd.in/gEeDhe4q) on stakeholder engagement to make sure that the right people are included and get a seat on the table: 1. All participation is a form of labor that should be recognized 2. Stakeholder engagement must address inherent power asymmetries 3. Inclusion and participation can be integrated across all stages of the development lifecycle 4. Inclusion and participation must be integrated to the application of other responsible AI principles The decision tree was developed against the backdrop of the NIST AI Risk Management Framework (AI RMF 1.0) and its definition of 7 principles of trustworthy AI: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with their harmful biases managed. See: https://lnkd.in/gHp5iE7x Apart from the decision tree itself, it is worth having a look at the additional resources at the end of the paper: - 4 overall guiding principles for evaluating AI in the context of human rights (Informed Consent, Beneficence, Nonmaleficence, Justice). - Examples of groups that are commonly subject to disproportionate impacts. - Common ways that AI can lead to harm (Over-reliance on safety features, inadequate fail-safes, over-reliance on automation, distortion of reality or gaslighting, reduced self-esteem/reputation damage, addiction/attention hijacking, identity theft, misattribution, economic exploitation, devaluation of expertise, dehumanization, public shaming, loss of liberty, loss of privacy, environmental impact, erosion of social & democratic structures). See for more from Microsoft: https://lnkd.in/gCVK9kNe - Examples of guidance for regular post-deployment monitoring and auditing of AI systems. #decisiontree #RAI

The other day Dr. Joy Buolamwini shared an update with an example of ChatGPT that helps with parental leave. She posed some ethical questions to evaluate the model, but used the term "AI Ethical Pipeline." I was not familiar with the term and was curious. My first step was to do a quick google search. It didn't turn up much useful information but it did share this paper (that's where I snagged the screen capture). The paper was lengthy, written by academics exploring this concept in a manufacturing context. A Responsible AI Framework: Pipeline Contextualisation Eduardo Vyhmeister · Gabriel Castane · P.‑O. Östberg · Simon Thevenin https://lnkd.in/g9W24XWU When my eyes started to glaze over, I decided to use Claude.AI as my personal tutor to help guide some self-learning. I've been working on ethical and responsible use frameworks, but a pipeline helps operationalize the policy. It has a big focus on risk management - to identify, assess, and mitigate ethical risks related to AI systems such as unfair bias, privacy, security, safety, and transparency. So, while a policy might be developed on the front end, the process of ethical AI is an ongoing one of assessing risk management - especially for those developing applications. AI ethics is not a pot-roast that you set and forget! The pipeline has specific steps including defining the technical scope, data usage, human interaction, and values to incorporate. The testing assesses potential risks or harms to identify and mitigate them. The pipeline also incorporates regulatory requirements so it has to be flexible to adapt to evolving regulations.The pipeline also establishes monitoring processes to continually assess ethics risks and make improvements over time. The goal is to bake ethical considerations into the full lifecycle - development, deployment, and operation - of AI systems. It provides a structured way to operationalize ethical principles and values (perhaps spelled out in an ethical use policy) and to make ethics integral to building, deploying, and managing trustworthy AI. The European Commission's Ethics Guidelines for Trustworthy AI propose a process with an assessment list, implementation measures, and monitoring through a "trustworthiness pipeline." Other techniques include: Algorithmic Assessment and Workflow injection. So, yes big companies developing the tech are doing this. But when we (nonprofits) build with those tools, are we thinking about a version of the ethical pipeline as well? My biggest concern is that the work might stop at writing the ethical use policy without having that pipeline. #aiethics #ai #ainonprofits

🔖 Defining AI Ethics and Applying ISO Standards with Actionable KPIs🔖 ➡ What Is AI Ethics?  #AIethics applies moral principles to guide the design, development, and management of artificial intelligence systems. These principles aim to ensure fairness, accountability, transparency, and respect for societal values. However, applying ethics in a measurable and actionable way can be exceptionally challenging. Leveraging ISO standards such as #ISO12791, #ISO5339, #ISO38507, and #ISO37301, organizations can create structured approaches to embed ethical principles into AI systems while measuring their effectiveness. ➡ Practical and Empirical Approaches Using ISO Standards Operationalizing AI ethics requires translating abstract principles into tangible Key Performance Indicators (#KPIs). Below is a proposed framework aligning ethical goals with ISO standards to provide measurable results. ➡Steps to Operationalize Ethics with ISO Standards ✅ 1. Define Ethical Priorities  Use ISO5339 to identify stakeholder-aligned ethical goals and ISO38507 to map these goals to governance responsibilities. ✅ 2. Establish Measurable KPIs  Translate principles like #fairness and #transparency into KPIs such as bias remediation rates or user satisfaction with system #explainability. ISO12791 offers tools to identify and address ethical gaps empirically. ✅ 3. Implement Ethical Risk Management  Apply compliance risk frameworks from ISO37301/ISO23894 and lifecycle bias checks from ISO12791 to ensure ethical risks are mitigated before deployment. ✅ 4. Monitor and Adapt Continuously  Use ISO38507 to establish governance structures for lifecycle monitoring, ensuring systems remain aligned with ethical objectives and evolving societal norms. ❗ For those interested, several organizations are dedicated to promoting ethical practices in artificial intelligence. Notable among them are: -Association of AI Ethicists: Dedicated to promoting the professional development and independence of digital, data, and AI ethicists globally. - AI Now Institute: A research institute examining the social implications of artificial intelligence. - The Algorithmic Justice League: A collective aiming to highlight algorithmic bias and promote equitable and accountable AI systems. - Ethical AI Alliance: A non-profit alliance of leading tech companies, academic institutions, and advocacy groups committed to ethical AI development. - Partnership on AI: Organization focusing on AI and media integrity, labor and the economy, fairness, transparency, accountability, inclusive research and design, and safety-critical AI. And please don't forget our established leaders in AI Ethics like Rupa Singh, Enrico Panai, Reid Blackman, Ph.D., Dr. Joy Buolamwini, and many others...please comment those AI Ethicists who should be acknowledged below. A-LIGN #TheBusinessofCompliance #ComplianceAlignedtoYou

What Makes AI Truly Ethical—Beyond Just the Training Data 🤖⚖️ When we talk about “ethical AI,” the spotlight often lands on one issue: Don’t steal artists’ work. Don’t scrape data without consent. And yes—that matters. A lot. But ethical AI is so much bigger than where the data comes from. Here are the other pillars that don’t get enough airtime: Bias + Fairness Does the model treat everyone equally—or does it reinforce harmful stereotypes? Ethics means building systems that serve everyone, not just the majority. Transparency Can users understand how the AI works? What data it was trained on? What its limits are? If not, trust erodes fast. Privacy Is the AI leaking sensitive information? Hallucinating personal details? Ethical AI respects boundaries, both digital and human. Accountability When AI makes a harmful decision—who’s responsible? Models don’t operate in a vacuum. People and companies must own the outcomes. Safety + Misuse Prevention Is your AI being used to spread misinformation, impersonate voices, or create deepfakes? Building guardrails is as important as building capabilities. Environmental Impact Training huge models isn’t cheap—or clean. Ethical AI considers carbon cost and seeks efficiency, not just scale. Accessibility Is your AI tool only available to big corporations? Or does it empower small businesses, creators, and communities too? Ethics isn’t a checkbox. It’s a design principle. A business strategy. A leadership test. It’s about building technology that lifts people up—not just revenue. What do you think is the most overlooked part of ethical AI? #EthicalAI #ResponsibleAI #AIethics #TechForGood #BiasInAI #DataPrivacy #AIaccountability #FutureOfTech #SustainableAI #TransparencyInAI

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

If you’re teaching about AI without talking about algorithmic justice, you’re not preparing your students for the real world. Chapter 1 of Mitigating Bias in Machine Learning, written by Nina da Hora & Silvandro Pedrozo, MSc, goes deeper than the surface level ethics conversation, by offering a usable foundation for educators and developers alike. Inside the authors offer strategies to build machine learning models that don’t just “perform,” but perform responsibly. This Chapter Covers: • Why tech neutrality is a myth that needs to go • What algorithmic fairness actually means (and why definitions vary) • Where injustice creeps into the ML pipeline — from data collection to deployment • How to use fairness metrics and mitigation methods to shift models from harm to help This for the instructor who’s tired of outdated syllabi, or anyone teaching AI who’s been quietly wondering: "Am I covering this in a way that actually matters?" Upon completion of this chapter, the student should be able to • Understand different definitions of algorithmic fairness • Understand the importance of ethics in artificial intelligence • Learn about the main causes of injustice in machine learning • Learn the different sources of harm in a machine learning life cycle If you're building curriculum — or just curious what responsible ML teaching actually looks like — Mitigating Bias in Machine Learning is a great place to start. I'll leave the 🔗 to the book in comments. Drop your questions/syllabus updates below, I'd love to hear how the book is landing with you and your students. #ResponsibleAI #MachineLearning