How Companies Should Address AI Ethics

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.

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

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?

"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!

Deloitte’s latest State of Ethics and Trust in Technology report (https://deloi.tt/3XJtOnD) is out and it couldn’t have come at a more important moment of unprecedented change! With more organizations adopting AI and GenAI to drive faster and more impactful business outcomes, it’s critical for business leaders to have the right ethical technology standards and safeguards in place. However, as our survey of 1,800 global business and technical professionals found, more than half of professionals reported “no” or “unsure’ when asked if their organizations had ethical standards established. So, how can leaders get ahead of this and develop sound ethical standards for emerging technologies? 1) Define how the organization approaches trust and ethics.  2) Clearly communicate ethical standards and trustworthy principles within the workforce.  3) Invest in the leaders, such as a Chief Ethics Officer, who will drive ethical standards forward.  4) Foster collaboration within and outside the organization.  5) Scale ethical standards across adopted emerging technologies and their outlined use cases. For those beginning this journey, our Technology Trust Ethics Framework is a great starting point: https://deloi.tt/3XZFMe7