Social Responsibility Metrics

⚠️ Can AI Serve Humanity Without Measuring Societal Impact?⚠️ It's almost impossible to miss how #AI is reshaping our industries, driving innovation, and influencing billions of lives. Yet, as we innovate, a critical question looms: ⁉️ How can we ensure AI serves humanity's best interests if we don't measure its societal impact?⁉️ Most AI governance metrics today focus solely on compliance and while vital, the broader question of societal impact (environmental, ethical, and human consequences of AI) remains largely underexplored. Addressing this gap is essential for building human-centric AI systems, a priority highlighted by frameworks like the OECD.AI's AI Principles and UNESCO’s ethical guidelines. ➡️ The Need for a Societal Impact Index (SII) Organizations adopting #ISO42001-based AIMS already align governance with principles of transparency, fairness, and accountability. But societal impact metrics go beyond operational governance, addressing questions like: 🔸Does the AI exacerbate inequality? 🔸How do AI systems affect mental health or well-being? 🔸What are the environmental trade-offs of large-scale AI deployment? To address, I see the need for a Societal Impact Index (SII) to complement existing compliance frameworks. The SII would help measure AI systems' effects on broader societal outcomes, tying these efforts to recognized standards. ➡️Proposed Framework for Societal Impact Metrics Drawing from OECD, ISO42001, and Hubbard’s measurement philosophy, here are key components of an SII: 1️⃣ Ethical Fairness Metrics Grounded in OECD principles of fairness and non-discrimination: 🔹 Demographic Bias Impact: Tracks how AI systems impact diverse groups, focusing on disparities in outcomes. 🔹Equity Indicators: Evaluates whether AI tools distribute benefits equitably across socioeconomic or geographic boundaries. 2️⃣ Environmental Sustainability Metrics Inspired by UNESCO’s call for sustainable AI: 🔹Energy Use Efficiency: Measures energy consumption per model training iteration. 🔹Carbon Footprint Tracking: Calculates emissions related to AI operations, a key concern as models grow in size and complexity. 3️⃣ Public Trust Indicators Aligned with #ISO42005 principles of stakeholder engagement: 🔹Explainability Index: Rates how well AI decisions can be understood by non-experts. 🔹Trust Surveys: Aggregates user feedback to quantify perceptions of transparency, fairness, and reliability. ➡️Building the Societal Impact Index The SII builds on ISO42001’s management system structure while integrating principles from the OECD. Key steps include: ✅ Define Objectives: Identify measurable societal outcomes ✅ Model the Ecosystem: Map the interactions between AI systems and stakeholders ✅ Prioritize Measurement Uncertainty: Focus on areas where societal impacts are poorly understood or quantified. ✅ Select Metrics: Leverage existing ISO guidance to build relevant KPIs. ✅ Iterate and Validate: Test metrics in real-world applications

CSRD Nature Metrics Compared with TNFD, CDP, and GRI 🌎 Nature is becoming a central pillar of sustainability reporting, reflecting its critical role in global environmental and economic systems. Companies are increasingly expected to disclose their impacts and dependencies on nature with greater precision and alignment to evolving standards. A recent comparative analysis highlights how CSRD’s nature-related metrics overlap with TNFD, CDP, and GRI standards, showcasing a growing convergence across frameworks. Pollution, water, biodiversity, and waste emerge as key thematic areas where disclosure expectations are sharpening. Pollution metrics, including emissions, microplastics, and expenditures related to incidents, demonstrate strong alignment across frameworks, signaling a heightened need for transparent reporting on pollution-related risks and costs. Water-related disclosures, such as consumption, withdrawals, and discharges, show close alignment, especially in stress-prone areas. This reinforces the critical role of water stewardship as a material topic across industries. Biodiversity metrics reveal a more fragmented alignment, yet the direction of travel is clear: the use of land, protection of nature-oriented areas, and ecosystem impact assessments are gaining prominence in corporate reporting expectations. Waste management metrics, particularly on secondary material use, total waste generated, and breakdowns by type and treatment, are highly aligned. Circular economy principles are becoming embedded in nature-related disclosures. Financial information about the risks and opportunities linked to environmental impacts is increasingly demanded across all categories. Forward-looking disclosures are no longer optional—they are becoming a regulatory and market expectation. Nature reporting is evolving rapidly. Businesses that proactively integrate nature-related metrics into sustainability strategies will be better positioned to navigate regulatory shifts, meet stakeholder expectations, and build long-term resilience. #sustainability #sustainable #business #esg #nature #biodiversity

In the world of Environmental, Social, and Governance (ESG) investing, Key Performance Indicators (KPIs) play a crucial role in measuring a company's sustainability and ethical practices. But did you know there are both leading and lagging indicators within ESG KPIs? Leading Indicators: Leading indicators are proactive metrics that predict future performance and help companies identify areas for improvement before issues arise. Examples include: - Employee Engagement Scores: High engagement often leads to better productivity and lower turnover rates. - Training Hours: Investment in employee development can drive innovation and efficiency. - Carbon Footprint Reduction Targets: Setting ambitious goals for reducing emissions can drive sustainable practices. Lagging Indicators: Lagging indicators, on the other hand, measure outcomes that have already occurred. They provide insights into past performance and help assess the effectiveness of strategies. Examples include: - Employee Turnover Rate: Reflects the company's ability to retain talent. - Carbon Emissions: Measures the actual environmental impact of the company's operations. - Incidents of Non-Compliance: Indicates how well the company adheres to regulations and ethical standards. Understanding and balancing both leading and lagging indicators is essential for a comprehensive view of a company's ESG performance. By focusing on leading indicators, companies can proactively improve their practices, while lagging indicators help assess the effectiveness of these efforts.

Very proud to have helped written and led work on a new paper coming from IEEE, Prioritizing People and Planet as the Metrics for Responsible AI: https://lnkd.in/gn4hzuNa. As part of our Ethically Aligned Design for Business series, this provides enterprise / corporate level recommendations for how to identify, utilize (for design) and leverage metrics and standards that increase ecological and human flourishing at the outset and through all of design / implementation and after-product (cradle to cradle). Defining "innovation" in a paradigm prioritizing growth without recognizing a technology's potential harm for the planet makes no sense. One example: where LLMs are using half a liter of water for a 20-minute session from one person, it makes no sense to then use those tools in an "AI for Planet" framing. Having the water for people and planet is the priority. But whatever the case or output, having ways to identify and design knowing these types of situations is the new definition of "Responsible." And what a wondrous world we'd have if planet and people flourishing was the metric for society versus unfettered, ill-defined "growth." HUGE thanks to our two Chairs, Adam Cutler and Milena Pribić and all our other amazing Committee Members. For anyone working on AI, SDG, ESG, climate or sustainability issues - this is a must read.

How Do You Turn ESG Goals into Tangible Results? Have you ever wondered how to translate your company’s ESG (Environmental, Social, and Governance) ambitions into actionable, measurable outcomes? It’s a journey that many organizations are embarking on, and it starts with understanding the numbers behind your performance. The key lies in identifying the right metrics. Imagine you’ve conducted a materiality assessment and pinpointed energy management as a core focus. You might track total energy use, energy reduction, and the percentage of renewable energy utilized. These metrics tell a story about your environmental impact and progress. Or perhaps your priority is human capital management. In this case, metrics like training hours, employee satisfaction scores, and career development opportunities reveal how well your organization supports its workforce. These numbers aren't just data points; they reflect your commitment to improving employee well-being. But how do you get these numbers? It’s all about setting up the right infrastructure. Start by identifying where your data will come from. Operations might handle energy data, while HR provides workforce insights. Clearly communicate what data is needed and in what format, and consider automating data collection to save time and improve accuracy. Data accuracy is crucial. Investors and stakeholders rely on credible ESG reports, so each data point must be verified and validated. Establishing strong protocols for data collection and reporting ensures that your metrics are reliable and your organization’s reputation remains intact. Remember, Every company that excels in ESG reporting started at the beginning. By investing time in building solid processes today, you’re setting the foundation for reliable, actionable insights tomorrow. What metrics are you focusing on to track your ESG performance? Let’s share ideas and learn from each other’s experiences! 💬

What do #ESG ratings actually measure? In a new OECD - OCDE report, we have assessed over 2.000 ESG metrics from eight leading ESG rating products. Here is what we found:   1️⃣ ESG ratings reflect companies’ policies and activities rather than their impacts: 68% of all metrics measure company policies and activities rather than outcomes. 2️⃣ Coverage of ESG topics is uneven: key topics such as biodiversity, human rights and corruption are barely covered in some products and entirely omitted in others. 3️⃣ The same ESG topics are measured differently by different rating products: The number of metrics used to measure corporate governance ranges from 4 metrics in one product to 113 metrics in another product. 4️⃣ Sustainability due diligence is barely measured: Of the 2,000 metrics analysed, less than 5% are associated with enterprise-wide due diligence and only 7% with management of supply chain sustainability   The OECD’s Global Corporate #Sustainability Report, published last year, found that of the 44.000 listed companies globally, almost 9.600 listed companies representing 86% of the total market capitalisation disclosed sustainability-related information.     In other words there is ample demand for and supply of sustainability information. But as our assessment of ESG rating products shows, we need better standardisation before this information can effectively help channel capital allocation. In the absence of such standardisation, disclosure risks being a wasted effort. The answer to this problem should come from policy makers, who can drive standardisation for relevant, credible and comparable sustainability information. Congrats to the team behind this work: Barbara Bijelic Benjamin Michel Konstantin Mann Carmine Di Noia Caio de Oliveira, CFA Khalid Azizuddin Flora Monsaingeon Thorfinnur Omarsson Kamil Zabielski Peter Paul Van De Wijs Robert Patalano, CFA

In the rapidly evolving world of Artificial Intelligence (AI), it's crucial to ensure our AI models produce meaningful, relevant, responsible, and ethical outcomes. This is where ESG+ (Environmental, Social, Governance, and Ethical) metrics play a pivotal role. 🌍👥💼 In my latest blog, "ESG: The Vital Signs for Responsible and Ethical AI Outcomes," I explore how integrating ESG metrics into the AI Utility Function can guide AI models to deliver better outcomes for society and the environment, including: 🌿 Environmental: Energy efficiency, recycling, sustainability, carbon footprint, greenhouse gas emissions, energy consumption, water usage, waste production, circularity rate, biodiversity impact, environmental compliance, pollution reduction, land preservation, forest preservation, renewable energy usage, etc. 👥 Social: Quality of life, clean air, clean water, workforce diversity, equal employment opportunities, affordable housing, affordable healthcare, education equality, diversity and inclusion metrics, employee turnover rates, workplace safety, community engagement, employee training, employee development, customer satisfaction, supplier satisfaction, human rights adherence, etc. 🏛️ Governance: Executive compensation transparency, audit scores, compliance and operational risk measures, compensation equity, reporting transparency, stakeholder (and not just shareholder) engagement and satisfaction, legal and regulatory compliance, conflict of interest policies and compliance, sustainability integration, personal data privacy protection, board diversity, etc. 🤝 Ethical: Donations, charitable contributions, grants, volunteering, community welfare, mentoring, activism, pay equality, hiring transparency, promotional transparency, CSR reporting, ESG compliance reporting, individual privacy, individual rights, etc. 🚀 By integrating these comprehensive ESG+ metrics, we can show that AI Utility Functions can deliver outcomes that are not only beneficial but also ethical and sustainable. #AI #ESG #Sustainability #EthicalAI #Governance #ArtificialIntelligence #ResponsibleAI #DataScience #Innovation