Addressing Ethical Concerns in Tech Design

✳ 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.

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.

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

A must read on operationalizing AI ethics. This excellent paper describes the real-life challenges of practitioners. Highlights and proposed solutions ➤ In the background: ⭐ We have a problem of ethics washing in AI ethics. Organizations talk the talk but don’t walk the walk. ⭐ Understanding the problem is the first step to solving it. This paper gives us a look from within. ➤ Problem 1 ⭐ Struggles to prioritize AI ethics topics in environments that push for launches ⭐ I have seen this myself, too. I think that some of the difficulty lies in how AI ethics processes are structured rather than with the topics themselves. The solution could be including engineering teams in the design of the processes, and creating processes that are solution-oriented. ➤ Problem 2 ⭐ Difficult to quantify “ethics” metrics ⭐ No easy fix for this one. But, I will say that the perfect can be the enemy of the good in this one. Sometimes it’s good to settle on a metric even if it’s not ideal. In addition, using research papers for inspiration for metrics. ➤ Problem 3 ⭐ Frequent re-orgs make it difficult to access knowledge and maintain relationships ⭐This problem seems the most difficult to me, especially as some of the re-orgs intentionally weaken the ethics teams. One (partial) solution could be prioritizing embedding the ethics work within engineering teams, so that when the re-org happens the work is already anchored in the organization. ➤What do other people think? ⭐ What the the greatest challenges to operationalizing AI ethics? How can they be addressed? ⭐ And do you know similar papers you could recommend?

A new article in the The Chronicle of Philanthropy about responsible AI in practice. I was interviewed along with Afua Bruce, Nathan Chappell, MBA, MNA, CFRE, Karen Boyd Ben Miller, and Dan Kershaw. Rather than using ethical concerns to discourage AI adoption, the piece emphasizes how ethical guidelines serve as helpful guardrails that enable nonprofits to harness AI's potential while protecting their mission and stakeholder trust. The article brings together perspectives from multiple nonprofit tech experts (including me!) who share these practical insights on implementing AI responsibly. ✅ Why Create AI policies even if leadership doesn't plan to use it (to address "shadow use") ✅Start with organizational values: Talk to staff first to identify needs and concerns before crafting guidelines Don't just cut and paste another organization's policy Build AI policies around your nonprofit's core values Articulate AI use through your organization's mission and values lenses ✅Human-Centered: Understand how staff currently use AI and their concerns Have conversations to address misconceptions about AI Consider whether AI use might displace human workers inappropriately Practice co-intelligence, but keep decision-making under human control ✅Data security/privacy: Never upload private donor information to systems you don't control Be mindful that data uploaded to AI tools may be used for training Consider strategically sharing public mission-focused content to help train AI with nonprofit perspectives ✅Managing bias & accuracy: Watch for potential biases in AI-generated content Have diverse teams review content to ensure alignment with values Have humans review AI outputs before letting it out in the world Define what harm looks like and create clear procedures for correcting mistakes Use validation and checking techniques for accuracy ✅Transparency: Disclose AI use based on extent of usage (minor edits like reducing word count may not need disclosure, while more extensive use of AI to draft the content should be credited) Identify specific language for disclosure of externally facing content Always disclose AI-generated images since people assume images are real Be upfront if you're nervous about disclosing AI use – it may signal inappropriate use What other practice advice would you add to this list? Taping into the AI & social sector wisdom out there ... Rachel Kimber, MPA, MS Meenakshi (Meena) Das Andrew Dunckelman Marnie Webb Tim Lockie Rev. Tracy Kronzak, MPA 🇺🇦 Kim Snyder Devi T. Jean Westrick Lawana Jones Jim Fruchterman Rhea Wong Josh Hirsch, MS John Kenyon Susan Mernit Nancy J. Smyth, PhD Zoe Amar FCIM Amy Neumann, M.A. Law, Justice, and Culture Allison Fine Jen García Gayle Roberts, CFRM 🏳️🌈 Wayan Vota Joshua Peskay Amy Sample Ward Anne Murphy Woodrow Rosenbaum Jonathan Waddingham https://lnkd.in/g5zUqFDZ

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?

𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 𝗮 𝗕𝘂𝘇𝘇𝘄𝗼𝗿𝗱 — 𝗜𝘁’𝘀 𝗮 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 In an age where AI agents are making decisions on behalf of humans, the conversation can no longer be limited to accuracy and efficiency alone. We must ask:  🔹 Are these systems fair?  🔹 Can we trust their decisions?  🔹 Are we prepared for their unintended consequences? As we move toward an AI-first world, the responsibility of creators, adopters, and regulators grows exponentially. AI is not just a tool — it’s a force that influences how people live, work, and connect. 🚨 𝗧𝗵𝗲 𝗸𝗲𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗼𝗳 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: Bias & Fairness: Tackling algorithmic discrimination, data imbalance, and outcome disparities across demographics. 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Building robust protections without compromising usability or performance. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 & 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Moving beyond the black box to foster trust and accountability. 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Ensuring AI agents understand and respect human values, intentions, and context. 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Developing AI that is dependable even in complex, high-risk environments. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Creating clear rules of responsibility, auditability, and legal frameworks. 𝗦𝗼𝗰𝗶𝗲𝘁𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁: Proactively managing the ripple effects of AI on jobs, education, access, and equality. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 & 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Striking the right balance between automation and essential human oversight. It’s not about slowing down innovation — it’s about scaling it responsibly.  We must design systems that not only solve technical problems but also protect human rights, promote social good, and empower communities. This is not a one-time checklist — it’s a continuous commitment to ethical design, transparent systems, and inclusive deployment. 🌐 Responsible AI is the foundation on which the future of tech will be judged. Let’s lead with intention, empathy, and accountability. Follow Dr. Rishi Kumar for similar insights! ------- 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 - https://lnkd.in/dFtDWPi5 𝗫 - https://x.com/contactrishi 𝗠𝗲𝗱𝗶𝘂𝗺 - https://lnkd.in/d8_f25tH