Ethical AI Use In Business

🤝 How Do We Build Trust Between Humans and Agents? Everyone is talking about AI agents. Autonomous systems that can decide, act, and deliver value at scale. Analysts estimate they could unlock $450B in economic impact by 2028. And yet… Most organizations are still struggling to scale them. Why? Because the challenge isn’t technical. It’s trust. 📉 Trust in AI has plummeted from 43% to just 27%. The paradox: AI’s potential is skyrocketing, while our confidence in it is collapsing. 🔑 So how do we fix it? My research and practice point to clear strategies: Transparency → Agents can’t be black boxes. Users must understand why a decision was made. Human Oversight → Think co-pilot, not unsupervised driver. Strategic oversight keeps AI aligned with values and goals. Gradual Adoption → Earn trust step by step: first verify everything, then verify selectively, and only at maturity allow full autonomy—with checkpoints and audits. Control → Configurable guardrails, real-time intervention, and human handoffs ensure accountability. Monitoring → Dashboards, anomaly detection, and continuous audits keep systems predictable. Culture & Skills → Upskilled teams who see agents as partners, not threats, drive adoption. Done right, this creates what I call Human-Agent Chemistry — the engine of innovation and growth. According to research, the results are measurable: 📈 65% more engagement in high-value tasks 🎨 53% increase in creativity 💡 49% boost in employee satisfaction 👉 The future of agents isn’t about full autonomy. It’s about calibrated trust — a new model where humans provide judgment, empathy, and context, and agents bring speed, precision, and scale. The question is: will leaders treat trust as an afterthought, or as the foundation for the next wave of growth? What do you think — are we moving too fast on autonomy, or too slow on trust? #AI #AIagents #HumanAICollaboration #FutureOfWork #AIethics #ResponsibleAI

Algorithmic transparency refers to the principle that the operations and decision-making processes of algorithms should be open and understandable to people who interact with or are impacted by them. It’s an aspect of accountability and fairness that seeks to mitigate the ‘black box’ nature of complex AI systems. For high-risk AI systems, strict transparency requirements will apply under the AI Act, such as adequately informing users when they interact with an AI system and making sure that its capabilities and limitations are clearly outlined. The AI Act will also require that users are aware of the AI's decision-making parameters. Companies must not only disclose how the algorithm works but also need to explain the rationale behind these decisions. This is particularly important for high-risk AI systems, where the consequences of error could be catastrophic. Transparency, in this context, evolves from being a mere buzzword to a structural necessity. The AI Act also focuses on transparency in emotion recognition and biometric categorisation, and deepfakes. For the former, the Act requires that people exposed to these AI systems must be informed, except in cases where the technology is used for criminal investigations. This exception raises ethical questions about balancing privacy with security. For the latter, deepfake technology must come with disclosure that the content isn't authentic, though exceptions exist for legal or artistic purposes. These carve-outs have provoked questions about the potential stifling of creative or journalistic endeavours. While the AI Act has taken the spotlight of AI regulation, the Digital Services Act’s provisions on recommender systems echo the AI Act's call for transparency. Recommender systems, a subset of AI technologies, also must outline their main parameters in "plain and intelligible language," echoing the AI Act's push for clear, comprehensible explanations. The DSA even mandates an explanation of why certain parameters are considered more important than others, extending the notion of transparency into the realm of accountability. Both acts show a commitment to user agency. The AI Act ensures that the user retains a degree of control when interacting with high-risk AI systems, including an ‘off switch’. Meanwhile, the DSA promotes user agency by compelling platforms to allow users to modify their preferences. The AI Act introduces obligatory risk assessments for high-risk applications, mirroring the DSA's requirements for platforms to conduct comprehensive risk assessments. Here, we witness two regulatory streams converging into a river of algorithmic accountability, encouraging a more nuanced, ethical approach to AI development and implementation. Laws on algorithmic transparency reflect the a paradigm shift in our approach to the ethical and social implications of AI. The importance of such legislation will only intensify as AI becomes increasingly interwoven into the fabric of our lives.

🌳 Design Patterns For Building Trust. With practical guidelines for designers on how to make products — AI and non-AI — more trustworthy, reliable and honest. In the noisy and polluted world today, trust doesn’t come for free. It doesn’t emerge by default. It must be earned and meticulously preserved — by being reliable, accountable and treating customers with respect. This holds true for people but it also for software. According to Anyi Sun, there are 5 psychological foundations of user trust: 1. Reliability 🔰 The degree to which the product consistently behaves as expected. It's a sense that that the product is dependable — based on a track record of past actions. Reliability comes from promising what you do, and doing what you promised. 2. Technical competence ⚡ Perceived intelligence, sophistication and capability of the product. It's user's belief that the product can successfully perform what they are being trusted to do. It's about trusting product's capability. 3. Understandability 🧠 The extent to which users feel they can understand how the system works or why it made a certain decision. The product must be able to articulate how a decision came along, with references to fragments that underpin a decision. 4. Faith and Care 🌱 Emotional, almost "blind trust" in the product, especially when users don't understand the underlying logic. It's a belief that the trusted party actually cares about the positive outcome for you, and intends to do good. 5. Personal attachment 🌳 A sense of rapport, connection or emotional engagement with the product. Typically it emerges when a user feels that they get meaningful value from the product, and from interactions with people supporting it. Personally, I would also add the value of repeated positive experiences that build confidence in the quality of the product, and hence its reliability. --- With AI products, hitting all these psychological foundations is extremely hard. Surely some people trust AI almost instinctively, others are more critical. But people's attitude often changes dramatically once they realized that they've made severe mistakes because of AI. Recovering from it is very hard. We can help with some design patterns: 1. Avoid "Ask me anything" → push for scoping and constraints 2. Slow down users in prompting → request specific details 3. Present multiple viewpoints, explain that experts disagree 4. Allow users to manage “memory”, profiles personalization 5. Highlight what is AI-generated and what isn't (AI disclosure) 6. Allow users to override AI-generated suggestions manually 7. Allow users to tweak AI output and refine it for their needs 8. Adapt AI's tone depending on the severity of user's task Trust is why people stay or leave. It builds long-term loyalty and helps users overcome hesitation. But it must be designed and retained — across all psychological foundations and with thoughtful UX work. I think designers will be quite busy for years to come. #ux #design

✨New working paper on the trade-offs involved in AI transparency in news 🤖📝 How does a global news organisation disclose its use of AI? Where, when and how should readers be told when algorithms shape the news they consume? Based on a case study of the Financial Times and led by Liz Lohn we argue that transparency about AI in news is best understood as a spectrum, evolving with tech advancements, commercial, professional and ethical considerations and shifting audience attitudes. 🔗Pre-print: https://lnkd.in/gV3dPXgS 1️⃣ AI‑transparency ≠ a binary. At the FT it’s a hybrid of policy, process and practice. Senior leadership sets explicit principles, cross‑functional panels vet new applications, and AI use is signposted in internal/external tools and reinforced through training. 2️⃣ Disclosure is calibrated to context. Internally, full disclosure aims to reduce frictions and surfaces errors early; externally, labels are scaled with autonomy and oversight. No‑human‑in‑the‑loop features (e.g. Ask FT) get prominent warnings, whereas AI‑assisted, journalist‑edited outputs (e.g. bullet‑point summaries) get lighter labelling. 3️⃣ Nine factors shape what, when & how the FT discloses AI use. These include legal/provider requirements, industry benchmarking, the degree of human oversight, the nature of the task, system novelty, audience expectations & research, perceived risk, commercial sensitivities and design constraints. 4️⃣ Persistent challenges include achieving consistent labelling (especially on mobile), breaking organisational silos, keeping pace with evolving models and norms, guarding against creeping human over‑reliance, and mitigating against “transparency backfire” where disclosures reduce trust. For those of you more academically interested in this, we argue that AI transparency at the FT is shaped by isomorphic pressures – regulations, peer practices and audience expectations – and by intersecting institutional logics. Internally, managerial and commercial logics push for efficient adoption and risk management; externally, professional journalism ethics and commercial imperatives drive an aim to remain trustworthy. Crucially, we argue that AI transparency is best seen as a spectrum: optimising one factor (e.g. maximum disclosure) can undermine others (e.g. perceived trust or revenue). There does not seem to be a one‑size‑fits‑all rule; instead transparency must adapt to org context, audiences and technology. We are very grateful to the team at the Financial Times, particularly Matthew Garrahan, for supporting this study from the outset – and to the participants from the FT who volunteered their precious time to help us in understanding this issue. Feedback welcome, especially on the theoretical section and the discussion as well as literature that we will have missed! So feel free to plug your own or other people’s material, all of which will be appreciated as Liz and I work towards a journal submission.

The G7 Toolkit for Artificial Intelligence in the Public Sector, prepared by the OECD.AI and UNESCO, provides a structured framework for guiding governments in the responsible use of AI and aims to balance the opportunities & risks of AI across public services. ✅ a resource for public officials seeking to leverage AI while balancing risks. It emphasizes ethical, human-centric development w/appropriate governance frameworks, transparency,& public trust. ✅ promotes collaborative/flexible strategies to ensure AI's positive societal impact. ✅will influence policy decisions as governments aim to make public sectors more efficient, responsive, & accountable through AI. Key Insights/Recommendations: 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: ➡️importance of national AI strategies that integrate infrastructure, data governance, & ethical guidelines. ➡️ different G7 countries adopt diverse governance structures—some opt for decentralized governance; others have a single leading institution coordinating AI efforts. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 & 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ➡️ AI can enhance public services, policymaking efficiency, & transparency, but governments to address concerns around security, privacy, bias, & misuse. ➡️ AI usage in areas like healthcare, welfare, & administrative efficiency demonstrates its potential; ethical risks like discrimination or lack of transparency are a challenge. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 ➡️ focus on human-centric AI development while ensuring fairness, transparency, & privacy. ➡️Some members have adopted additional frameworks like algorithmic transparency standards & impact assessments to govern AI's role in decision-making. 𝐏𝐮𝐛𝐥𝐢𝐜 𝐒𝐞𝐜𝐭𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 ➡️provides a phased roadmap for developing AI solutions—from framing the problem, prototyping, & piloting solutions to scaling up and monitoring their outcomes. ➡️ engagement + stakeholder input is critical throughout this journey to ensure user needs are met & trust is built. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐔𝐬𝐞 ➡️Use cases include AI tools in policy drafting, public service automation, & fraud prevention. The UK’s Algorithmic Transparency Recording Standard (ATRS) and Canada's AI impact assessments serve as examples of operational frameworks. 𝐃𝐚𝐭𝐚 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: ➡️G7 members to open up government datasets & ensure interoperability. ➡️Countries are investing in technical infrastructure to support digital transformation, such as shared data centers and cloud platforms. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐎𝐮𝐭𝐥𝐨𝐨𝐤 & 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: ➡️ importance of collaboration across G7 members & international bodies like the EU and Global Partnership on Artificial Intelligence (GPAI) to advance responsible AI. ➡️Governments are encouraged to adopt incremental approaches, using pilot projects & regulatory sandboxes to mitigate risks & scale successful initiatives gradually.

AI is improving much faster than most business leaders realize. I just watched an interview from Davos featuring Demis Hassabis (co-founder and CEO of Google Deepmind) and Dario Amodei (co-founder and CEO of Anthropic). While their timelines for AGI differ slightly, it's very apparent they share high conviction that we are on a near-term path to much more powerful and generally capable AI systems. It is becoming increasingly important that organizations plan for this future, now. One of the key actions leaders can take is to establish and govern a set of responsible AI principles that guide a human-centered approach to AI. Here are 12 principles that I set forth in January 2023 as part of a Responsible AI Manifesto. The manifesto was meant to codify our responsible AI principles at SmarterX, and serve as an open template for other organizations and leaders who want to pilot and scale AI in an ethical way. 1) We believe in the responsible design, development, deployment and operation of AI technologies. 2) We believe in a human-centered approach to AI that empowers and augments professionals. AI technologies should be assistive, not autonomous. 3) We believe that humans remain accountable for all decisions and actions, even when assisted by AI. The human must remain in the loop in all AI applications. 4) We believe in the critical role of human knowledge, experience, emotion, and imagination in creativity, and we seek to explore and promote emerging career paths and opportunities for creative professionals. 5) We believe in the power of language, images and videos to educate, influence, and affect change. We commit to never knowingly use generative AI technology to deceive; to produce content for the sole benefit of financial gain; or to spread falsehoods, misinformation, disinformation, or propaganda. 6) We believe in understanding the limitations and dangers of AI, and considering those factors in all of our decisions and actions. 7) We believe that transparency in data collection and AI usage is essential in order to maintain the trust of our audiences and stakeholders. 8) We believe in personalization without invasion of privacy, including strict adherence to data privacy laws, mitigation of privacy risks for consumers, and following our moral compass when legal precedent lags behind AI innovation. 9) We believe in intelligent automation without dehumanization, and the potential of AI to have profound benefits for humanity and society. 10) We believe in an open approach to sharing our AI research, knowledge, ideas, experiences, and processes in order to advance the industry and society. 11) We believe in the importance of upskilling and reskilling professionals, and using AI to build more fulfilling careers and lives. 12) We believe in partnering with organizations and people who share our principles.

Kevin Klyman: "📣📣 We just published the third annual Foundation Model Transparency Index! Our comprehensive study shows that AI companies have become less transparent in 2025. Some highlights from the paper: ➡️ Transparency on the decline: The average transparency score for AI companies declined from 58/100 in 2024 to 40/100 in 2025. xAI scores lower than any company we have ever assessed, releasing almost no information about its practices or its flagship model. ➡️ Companies withhold key information: Top tech companies release little or no information about the environmental impact of AI, whose data they use to build their systems, or whether the risk mitigations they put in place actually work. We definitively show that this information is not publicly available and that companies refuse to release it. ➡️ Companies share the capabilities of their models, but do not adequately evaluate risks. Just 4 of 13 companies comprehensively evaluated risks prior to release of their foundation model and report results upon release, and only IBM releases an externally reproducible risk evaluation. ➡️ Companies have changed their practices to release less information. In 2024, Meta and Mistral released technical reports alongside their flagship models (Llama 2 and Mistral 7B), but in 2025 neither released technical reports (for Llama 4 and Mistral Medium 3 respectively). As a result, Meta no longer discloses which risk mitigations it uses, quantitative evaluations of those risk mitigations, the amount and type of hardware it used to train its model, or prohibited model behaviors. ➡️ Our method: We break down transparency of AI companies into 100 indicators, develop concrete definitions and rubrics for those indicators, and send each company a transparency report template to fill out. This year 7 companies filled out the transparency report, and we independently assessed 6 other companies. We then worked with these companies to help them improve their disclosures, often resulting in companies disclosing new information to the public. You can read the full paper in the comments below! Thanks to the team behind the index - Alex Wan, Sayash Kapoor, Nestor Maslej, Shayne Longpre, Betty Xiong, Percy Liang, Rishi Bommasani! I'd also like to thank Stanford Institute for Human-Centered Artificial Intelligence (HAI) for supporting this work, Loredana Fattorini for making the visuals, and the Foundation Model Transparency Index board for their guidance Dr. Rumman Chowdhury, Daniel Ho, Arvind Narayanan, Danielle Allen and Daron Acemoglu. "

Much has been said about what AI can enable. But being able to unlock the potential remains a distant dream for many — as we face a widening divide in access to essential infrastructure, computing capabilities, and high-quality data. How then, can we best create sustainable AI systems that serve all people equitably and uplift all communities? [1] Sustainable infrastructure is non-negotiable: Path forward requires environmentally responsible AI infrastructure. [2] Data equity is a fundamental right: Diverse and inclusive datasets aren't just nice-to-have. When AI systems are trained predominantly with data that is western-centric, we risk perpetuating culture biases. [3] Responsible AI must be built in: We must ensure that technology improves the human condition, not just efficiency gains. We must strive for a future where the impact of AI is guided by robust ethical guardrails, safety and security controls. [4] Collaboration is key: No single nation can navigate this transformation journey alone; rather, it will require unprecedented collaboration between governments, academia, private and public sectors. Success cannot be measured in just tech achievements alone, but in how we can ensure AI's benefits can reach every corner of our society. #AI #FinancialServices #ResponsibleAI #BankingOnAI

How do we scale Generative AI without compromising ethics, sustainability, or data integrity? Here are my ten principles: 🔹 Strong Data Foundation: Ensure clean, reliable, and well-structured data to build effective AI systems. 🔹 Bias Mitigation: AI must fairly represent all voices through diverse datasets and rigorous testing. 🔹 Energy Efficiency: Consider the full environmental footprint—carbon, water, and energy consumption—to minimize AI’s impact. 🔹 Transparency: Explainable AI is key to earning user trust by making decisions understandable. 🔹 Data Privacy: Privacy-first design must be prioritized to respect users’ growing data concerns. 🔹 Human Oversight: AI should enhance human judgment, with human-in-the-loop systems ensuring responsible outcomes. 🔹 Guardrails: Implement ethical guardrails to prevent misuse and ensure AI aligns with societal values. 🔹 Collaboration with Regulators: Work closely with regulators like the EU AI Act to ensure compliance and trust. 🔹 Continuous Monitoring and Auditing: Regularly audit AI systems to catch biases and inefficiencies, ensuring ongoing alignment with ethical goals. 🔹 Inclusive Development: Diverse, inclusive teams bring varied perspectives, helping avoid blind spots and foster fair AI. These principles offer a roadmap for scaling AI that is both innovative and responsible, ensuring a balance between growth and ethical standards. #ai #generativeai #responsibleai #genai #ethicalai

G7 cybersecurity agencies, including Cybersecurity and Infrastructure Security Agency, have released a “Software Bill of Materials for AI: Minimum Elements,” which provides a practical baseline for what organizations should expect in an AI SBOM. It is not mandatory and does not create new requirements, but it details recommended minimum elements to improve cyber and supply chain transparency for AI systems. Some of the recommended elements include: ✅ Model information - model name, version, producer, hash value/hash algorithm, license, training properties, and model description/known limitations. ✅ Dataset information - dataset provenance, sensitivity, license, hash, and dependency relationships. ✅ Security properties - security controls, compliance information, cybersecurity policy information, and vulnerability references. ✅ Infrastructure details - software and hardware dependencies needed to run and support the AI system. Why does this matter? Imagine your organization is using a third-party AI model in a customer support workflow. A new vulnerability or licensing issue emerges around one of the model’s dependencies, training datasets, or deployment frameworks. Without an AI SBOM, your team may not know whether you are exposed. With one, especially when tied into your asset inventory, model registry, third-party risk process, or vulnerability management workflow, the security team can quickly answer: ❓ Where is this model used ❓Which version is deployed ❓Who produced it ❓What datasets or dependencies are involved ❓What security controls are in place No Log4j-era guessing. It is the foundation for robust management of AI supply chain risk. Closely related is the important community work led by Helen Oakley , alongside Daniel Bardenstein and Dmitry R., on AI BOM implementation. At RSAC2025, Helen introduced an open-source tool for generating AI SBOMs for Hugging Face models using the CycloneDX format, with human-readable quality indicators. That community work is complementary to the CISA/G7 guidance, which gives us a public-sector consensus baseline for cyber and supply chain transparency. The SBOM for AI / AIBOM work on GitHub gives teams a practical path to implementation mapping fields to CycloneDX and SPDX AI Profile-compatible formats and integrating into engineering and risk workflows. The CISO takeaway: 💠 Use the CISA/G7 minimum elements as the baseline. Start asking AI vendors and internal AI teams for AI SBOMs. 💠 Use the AIBOM community work to understand how AI BOMs can be implemented in practice. 💠 And tie the output into third-party risk, model governance, vulnerability management, and incident response. AI supply chain transparency is a critical AI security control, not just a documentation exercise. Sources: https://lnkd.in/enH9vK3t https://lnkd.in/ebV-_2Hv https://lnkd.in/eydmfD98