Ethical AI Use In Business

I’m so happy to see this! Yesterday, the ISO published a new standard, ISO/IEC 42001:2023 for AI Management Systems. My suspicion is that it will become as important to the AI world as ISO/IEC 27001 arguably became the most important standard for information security management systems. The standard provides a comprehensive framework for establishing, implementing, maintaining, and improving an artificial intelligence management system within organisations. It aims to ensure responsible AI development, deployment, and use, addressing ethical implications, data quality, and risk management. This set of guidelines is designed to integrate AI management with organisational processes, focusing on risk management and offering detailed implementation controls. Key aspects of the standard include performance measurement, emphasising both quantitative and qualitative outcomes, and the importance of AI systems’ effectiveness in achieving intended results. It mandates conformity to requirements and systematic audits to assess AI systems. The standard also highlights the need for thorough assessment of AI's impact on society and individuals, stressing data quality to meet organisational needs. Organisations are required to document controls for AI systems and rationalise their decisions, underscoring the role of governance in ensuring performance and conformance. The standard calls for adapting management systems to include AI-specific considerations like ethical use, transparency, and accountability. It also requires continuous performance evaluation and improvement, ensuring AI systems' benefits and safety. ISO/IEC 42001:2023 aligns closely with the EU AI Act. The AI Act classifies AI systems into prohibited and high-risk categories, each with distinct compliance obligations. ISO/IEC 42001:2023's focus on ethical AI management, risk management, data quality, and transparency aligns with these categories, providing a pathway for meeting the AI Act’s requirements. The AI Act's prohibitions include specific AI systems like biometric categorisation and untargeted scraping for facial recognition. The standard may help guide organisations in identifying and discontinuing such applications. For high-risk AI systems, the AI Act mandates comprehensive risk management, registration, data governance, and transparency, which the ISO/IEC 42001:2023 framework could support. It could assist providers of high-risk AI systems in establishing risk management frameworks and maintaining operational logs, ensuring non-discriminatory, rights-respecting systems. ISO/IEC 42001:2023 may also aid users of high-risk AI systems in fulfilling obligations like human oversight and cybersecurity. It could potentially assist in managing foundation models and General Purpose AI (GPAI), necessary under the AI Act. This new standard offers a comprehensive approach to managing AI systems, aiding organisations in developing AI that respects fundamental rights and ethical standards.

The Water Footprint of AI: Why We Need to Pay Attention to Its Environmental Cost As artificial intelligence continues to advance, its environmental impact, particularly concerning water consumption in data centres, warrants attention. Understanding AI's Water Usage AI models, especially large language models, require substantial computational resources. This computing power, concentrated in data centres, generates significant heat, necessitating extensive cooling, often through water-based systems. - Per Query Water Usage: Each interaction with AI models like ChatGPT consumes water. For instance, a 20-50 question session can use approximately 500 millilitres of water, primarily for cooling purposes. - Industry Impact: Data centres globally consumed over 660 billion liters of water in 2022 to cool servers running various services, including AI workloads. Key Areas of Concern 1. Water Scarcity: Many data centres are located in regions with limited water resources. In areas like California, where numerous tech companies operate, water-intensive cooling for AI adds strain to local supplies. 2. Seasonal Impact: During summer, data centres often double their water usage to maintain optimal temperatures. With climate change leading to more frequent heatwaves, this demand could increase, exacerbating the impact. 3. Comparative Impact: Training large AI models can consume up to five times more water than traditional data center operations, highlighting the need for efficient resource management. Steps Toward Sustainability To foster a more sustainable AI ecosystem, the tech industry can consider the following measures: 1. Adopt Alternative Cooling Solutions: Implementing methods like liquid immersion cooling, direct air cooling, and utilising recycled water systems can reduce water demands by up to 90% in certain environments. 2. Enhance Transparency and Accountability: Publicly reporting water usage and environmental impact data allows companies to foster accountability and enable informed consumer choices. Currently, only a few tech giants release detailed sustainability reports on water use. 3. Optimise Model Efficiency: Redesigning models to perform with lower computational intensity can significantly reduce both water and energy requirements. Model efficiency improvements, even by 10-15%, can save millions of litres of water annually. While AI offers transformative benefits across various sectors, it's crucial to balance its growth with responsible resource use. Focusing on sustainable AI practices is essential not only for environmental preservation but also for the technology's long-term viability.By embracing these strategies, we can ensure AI's advancement doesn't come at the expense of our planet's resources. Visual: The Times #ai #waterconsumption #sustainability #datacenters #environmentalimpact #greenai

🚀 Unveiling A Guiding Framework: Aligning AI and ESG for a Sustainable Future Really proud to have co-authored this Responsible AI Institute white paper, "AI's Impact on Our Sustainable Future: A Guiding Framework for Responsible AI Integration Into ESG Paradigms." Our work seeks to identify the benefits and challenges that AI presents for sustainability. It provides a practical framework for navigating the intersection of AI and ESG. It covers relevant metrics, regulatory landscapes, and integration methods, enriched with use cases and case studies. Our framework offers practical guidance for decision-makers, emphasizing continuous monitoring, adaptation, and collaboration in the evolving AI and ESG landscape. We encourage organizations, policymakers, and stakeholders to engage with this framework and work collaboratively towards sustainable practices, social responsibility, and stronger governance. Super grateful to my amazing co-authors, our reviewers, and Responsible AI for making this happen! Much more to come, so watch this space! ➡ Access the white paper here: https://lnkd.in/gnZMTa6b #ResponsibleAI #ESG #Sustainability #AI #Data #Tech #Climate Alyssa Lefaivre Škopac Andrea Ruotolo, Fulbright PhD Deval Pandya Rosalie Day Roshni Joshi

The next evolution of sustainable AI isn’t just about using more efficient hardware—it’s about Autonomous AI Agents that code with sustainability in mind. These agents are designed to operate independently, learning and adapting as they go, and have the potential to transform software development by writing energy-efficient code. They don't just optimize for speed; they prioritize minimal resource consumption. Why This Matters for Sustainability Modern AI models consume massive amounts of power, yet software development still prioritizes performance over energy efficiency. Agentic AI could change that paradigm by: ✅ Reducing Computational Waste: AI agents could select or generate the most efficient algorithms based on real-time constraints instead of defaulting to resource-heavy models. For example, they could optimize database queries to reduce data retrieval and processing or dynamically adjust resource allocation based on demand. ✅ Automating Green Software Principles: AI-driven frugal coding practices could optimize data structures, reduce redundant calculations, and minimize memory overhead. This could involve choosing the most energy-efficient programming language or framework for a specific task. ✅ Measuring & Optimizing in Real Time: The reward function would be clear: lower energy consumption, less latency, and reduced emissions—all while maintaining accuracy. ✅ Parallel & Distributed Optimization: AI agents could continuously refine codebases across thousands of cloud instances, improving sustainability at scale. AI-Driven Innovation Archive for Green Coding One of the most exciting ideas in autonomous coding is the "Green Code Archive"—an AI-generated repository of energy-efficient code snippets that could continuously improve over time. Imagine: 🔹 Reusing optimized code instead of reinventing energy-intensive solutions. 🔹 Carbon-aware coding suggestions for green data centers & renewable energy scheduling. 🔹 AI-driven legacy refactoring, automating migration to sustainable architectures. Measuring AI’s carbon footprint after the fact isn’t enough—the goal should be AI that reduces energy use at the source. The future of sustainable tech isn’t just about efficient hardware—it’s about intelligent, autonomous software that optimizes itself for minimal environmental impact. While this technology is still emerging, challenges remain in areas like training complexity and robust validation. However, the potential benefits for a greener future are undeniable. Learn more about leading with Agentic AI and its transformative potential in my book, "Empowering Leaders with Cognitive Frameworks for Agentic AI: From Strategy to Purposeful Implementation" (link in the comments section). #agenticai #greenai #sustainability

We are not yet ready for this. A growing army of autonomous agents are engaging with not just humans and other agents, but also economic and legal institutions. An "agent infrastructure" of systems and protocols could maximize benefits and contain risks, suggest a group of researchers from Centre for the Governance of AI (GovAI) Harvard Law School University of Oxford University of Cambridge and others (link in comments). Most AI safety research is focused on AI system-level interventions. However different approaches are required in a proliferating multi-agent environment. The researchers propose 3 major functions in effective agent infrastructure: Attribution, Interaction, and Response: 💡 Attribution: Ensuring accountability. Attribution is critical for linking AI agent actions to responsible parties, such as users or organizations. Mechanisms including identity binding, to associate an agent’s actions with a legal entity. Certification provides verifiable assurances about an agent’s behavior, such as data handling policies or autonomy levels. Implementing agent IDs enables tracking and monitoring specific agents, facilitating incident response and accountability. 🤝 Interaction: Shaping behaviors. Interaction infrastructure defines how agents engage with the world to enable reliability and security. Dedicated agent channels isolate agent activities from regular digital traffic, reducing risks like data contamination or accidental disruptions. Oversight layers empower users or managers to intervene when necessary, improving operational control and accountability. Inter-agent communication protocols support seamless collaboration and negotiation among agents, promoting cooperative outcomes in multi-agent systems. 🔄 Response: Mechanisms to mitigate harm. Response infrastructure addresses problems caused by agents using proactive and reactive measures. Incident reporting systems collect detailed data on harmful events, enabling developers and regulators to understand root causes and implement safeguards. Rollback mechanisms allow reversal of unintended actions, such as erroneous financial transactions, protecting users from significant harm. The concept of agent infrastructure and proposed framework provide a very useful framework to build the next phase of scalable agent ecosystems. We need to develop and agree on these principles soon, as the foundations of a burgeoning agent economy will be built through this year.

Definitely a must read for all #sustainability professionals getting to grips with the impact of AI on their #netzero transition plans... “Measuring What Matters: How to Assess AI’s Environmental Impact” The International Telecommunication Union has just released their report with help from some great partners, which looks at how we can measure the environmental impact of #AI Check out the comprehensive overview of current approaches to evaluating the environmental impacts of AI systems - focusing on identifying which components of AI’s #environmental impacts are being measured, evaluating the transparency and methodology soundness of these measuring practices, and determining their relevance and actionability. The report synthesizes findings from academic studies, corporate sustainability initiatives, and emerging environmental tracking technologies, and examines measurement methodologies, identifies current limitations, and offers recommendations for key stakeholder groups: developers (producers), users (consumers), and policy-makers. 💡 One of the most pressing issues uncovered is the widespread reliance on indirect estimates when assessing #energy consumption during the training phase of #AImodels. These estimates often lack real-time, empirical measurement. Furthermore, equally important #lifecycle stages remain significantly underexplored. This reliance on proxies introduces substantial data gaps, impedes accountability, and restricts consumers’ ability to make informed, sustainable choices about AI. Awesome work Nadim Kapadia, Tim Smolcic, Mark Butcher, Mohan Gandhi and all the contributors who worked so hard on this! Read the report: https://lnkd.in/dqbpuFYc #AIforGood #Sustainability #ClimateTech #ResponsibleAI #GreenAI #TechForGood

𝟐𝟎 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 𝐁𝐞𝐟𝐨𝐫𝐞 𝐘𝐨𝐮 𝐃𝐞𝐩𝐥𝐨𝐲 𝐀𝐈 Most AI Failures in enterprises are not Technical. They are Compliance Failures. Before deploying AI into Production,  Here are the 20 Non-Negotiables: 1. Appoint AI Accountability Leader   Assign a senior executive responsible for AI compliance, oversight, and reporting. 2. Establish Cross-Functional AI Board   Include legal, security, HR, data, and business teams for governance and approvals. 3. Define Legal AI Role   Clarify provider versus deployer obligations and compliance responsibilities. 4. Maintain Technical Documentation   Document architecture, data sources, performance metrics, and intended use limitations. 5. Disclose AI Usage Transparently   Notify users about AI interactions and synthetic content usage. 6. Publish Model Transparency Reports   Document purpose, performance across demographics, limits, and out-of-scope scenarios. 7. Implement Logging and Audits   Track inputs, outputs, versions, and decisions for investigations and traceability. 8. Ensure Decision Explainability   Provide meaningful explanations and enable human review of high-impact decisions. 9. Create Comprehensive AI Inventory   Document all AI systems, APIs, models, and embedded SaaS tools. 10. Develop AI Acceptable Use Policy   Define permitted uses, prohibited activities, and approved data types. 11. Classify AI Risk Levels   Categorize systems into prohibited, high, limited, or minimal risk tiers. 12. Conduct Formal Risk Assessments   Identify harms, discrimination risks, and safety issues before deployment. 13. Test for Bias Regularly   Evaluate outputs across protected groups and document mitigation steps. 14. Review Third-Party AI Risk   Assess vendor compliance, contracts, liabilities, and regulatory responsibilities. 15. Govern Training Data Legality   Track licenses, avoid unauthorized scraping, and respect copyrights. 16. Perform Required DPIAs   Assess high-risk personal data processing under GDPR and similar regulations. 17. Confirm Lawful Data Basis   Verify consent, contractual necessity, or legitimate interest before processing data. 18. Apply Data Minimization Rules   Limit data usage and enforce strict retention schedules. 19. Secure AI Infrastructure Assets   Protect pipelines, weights, APIs, and model endpoints with strong controls. 20. Support Data Subject Rights   Enable access, correction, deletion, restriction, and automated decision opt-outs. The real shift in enterprise AI is this. From model performance to governance readiness. From proof of concept to regulatory durability. If your AI cannot pass audit, it cannot scale. Compliance is not friction. It is infrastructure. PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #EnterpriseAI #AIGovernance #ResponsibleAI

Humanizing AI Through the Kano Model In an era where generative AI has become a ubiquitous offering, true differentiation lies not in merely adopting the technology but in integrating human values into its core. Building on my earlier discussion about applying the Kano Model to Gen AI strategy, let’s explore how this framework can refocus development metrics to prioritize ethics and human-centricity. By aligning AI systems with human needs, organizations can shift from functional tools to trusted partners that inspire lasting loyalty. Traditional metrics such as speed, scalability, and model accuracy have evolved into basic expectations the “must-haves” of AI. What truly elevates a product today is its ability to embody values like safety, helpfulness, dignity, and harmlessness. These qualities, categorized as “delighters” in the Kano Model, transform AI from a transactional tool into a meaningful collaborator. Key Human-Centric Differentiators Safety: Proactive safeguards must ensure AI systems protect users from risks, whether physical, emotional, or societal. Safety is non-negotiable in building trust. Helpfulness: Personalized, context-aware interactions demonstrate empathy. AI should anticipate needs and adapt to individual preferences, turning routine tasks into meaningful experiences. Dignity: Ethical design principles—fairness, transparency, and privacy—must underpin AI development. Respecting user autonomy fosters long-term trust and engagement. Harmlessness: AI outputs and recommendations should prioritize user well-being, avoiding unintended consequences like bias, misinformation, or psychological harm. This human-centered approach represents a paradigm shift in technology development. While traditional KPIs remain important, they are no longer sufficient to stand out in a crowded market. Organizations that embed human values into their AI systems will not only meet user expectations but exceed them, creating emotional connections that drive loyalty. By applying the Kano Model, businesses can systematically align innovation with ethics, ensuring technology serves humanity rather than the other way around. The future of AI isn’t just about efficiency it’s about elevating human potential through thoughtful, responsible design. How is your organization balancing technical excellence with human values?

Most AI compliance programs are built backwards! They start with a policy, end with a risk register, and somewhere in the middle no one owns anything. The result is governance on paper that fails in practice. The Core Problem ⏩ "Diffuse Accountability" - The book is direct about this. Diffuse accountability is the primary organizational failure mode in AI governance. When multiple teams share ownership of an AI system, no single team owns its governance. Risk gets identified and then handed off. Documentation gets created and then orphaned. Incidents happen and escalation paths are unclear (huge problem). The structural fix isn't a better policy. It's assigning clear primary ownership at every layer of the AI Governance Stack, with a designated function responsible for program-wide coherence. The CAO Role The Chief AI Officer (or equivalent title) is the cross-functional accountability anchor. The CAO role isn't primarily technical, it's organizational. This person owns the AI governance framework, drives risk classification decisions, coordinates between legal, engineering, security, and product, and escalates when governance creates friction with shipping timelines. The role only works with actual authority, not advisory standing. A governance function that can raise concerns yet cannot stop a deployment is a documentation function, not a compliance function. A functional AI compliance program requires four things working together ⤵️ First, an AI system inventory with current risk classifications. You cannot govern what you haven't catalogued. Every AI system in production or development needs a record, including what it does, what data it uses, what decisions it affects, and what regulatory obligations attach to it. Second, tiered governance requirements matched to risk classification. High-risk systems require conformity assessment, bias testing, human oversight mechanisms, and audit trails. Lower-risk systems require less. The requirement set has to be proportionate or practitioners will route around it. Third, deployment gates with real teeth. Governance that can be waived under deadline pressure isn't governance. Pre-deployment checklists, required sign-offs, and documented risk acceptance processes need to be embedded in the development workflow, not appended at the end. Fourth, continuous monitoring with defined escalation triggers. Post-deployment monitoring isn't optional for high-risk systems. Fairness metrics, drift detection, and incident response procedures need to be in place before deployment, not built reactively after something goes wrong. Drop a comment: where does your organization's AI compliance program have the biggest gap? #AIGovernance #GRC #Compliance #RiskManagement #AIRisk