AI Ethics & Responsible Technology speakers
Speakers who interrogate the human consequences of algorithmic decision-making, data ethics and emerging technology
Speakers Associates represents 149 speakers on AI Ethics & Responsible Technology, including Kemal Apaydin, Rahaf Harfoush, Limor Ziv, Harriet Farlow, Saakshar Duggal, Dr Sidney Shapiro, Tina Stowell, Timandra Harkness, Dame Wendy Hall and Susi O’Neill.
The integration of brain data, AI, and consumer-grade neurotechnology is moving faster than most senior leaders realise. The organisations engaging with this territory now will set the terms others have to accept later. Most boards do not yet have a real position on it.
Most leadership teams have run their generative AI pilots and now face a harder question: where does the technology actually sit inside the operating model, and which categories of work change shape entirely. The answer is rarely visible from the inside, where vendors pitch tools and consultants pitch frameworks. It comes from people who have built original commercial product with these systems and watched the next layer of human-machine technology arrive in a hospital bed.
Digital transformation has become the flag every board agenda flies. The hard question is which parts of the business model actually change, who is accountable for the outcome, and how governments and regulators will reshape the ground beneath a strategy as it is being executed. Leaders who treat technology, policy and strategy as separate conversations keep losing the argument in all three.
Leaders are more likely than ever to face compound crises – events that do not arrive sequentially but overlap, and that demand governance decisions while the institutional credibility needed to act is itself at risk. Most decision-making frameworks were built for conditions of reasonable stability. They do not account for what happens when a livestreamed act of mass violence forces simultaneous action on security, media, technology regulation, and international diplomacy within hours. The gap between what organisations plan for and what they actually face when a crisis hits is not a training problem. It is a governance design problem.
Most enterprises now have an AI strategy on paper and very little of it in production. The board wants returns, the engineering organisation is still rewriting pilots, and personalisation, agents and generative AI are stuck behind unresolved questions on data, privacy and operating model. The gap between AI ambition and AI in revenue is now the defining technology problem of the cycle.
Most boards have approved a digital strategy and an AI roadmap. Few can say what the company would look like if either one worked. That gap is widest where digital and AI were handed to IT as projects.
Boards and executive teams now make decisions about AI, data, and digital infrastructure that touch every part of the business. The technical case is well rehearsed. The harder questions, what these systems do to customer trust, to employee agency, to the meaning of the work, get pushed to ethics committees or deferred indefinitely. Leaders need a way to think clearly about technology that is neither uncritical adoption nor reflexive fear.
Senior leaders are being asked to act decisively in environments where their institutions are already distrusted. The old playbook, communicate clearly and the public will follow, no longer works. The harder question is how a leadership team earns the permission to make difficult calls on AI, on regulation, on contested social issues, before the decision itself can land.
AI is now a board-level decision, and most boards are making it without a defensible process. Legal teams flag risk, engineering teams ship models, and no one owns the question of whether the system should have been built at all. The gap between AI ambition and the controls needed to govern it is where reputational and regulatory damage accumulates.
Most boards now own an AI strategy on paper. Far fewer can defend, in front of customers, regulators or their own workforce, the design choices behind it. The gap between deploying AI and deploying it in a way that earns trust, holds up to scrutiny, and actually augments the people using it is where serious organisations are getting stuck.
Boards now own cyber risk in a way they did not a decade ago, and most are not equipped for it. Threat actors are using AI to industrialise social engineering, deepfakes and intrusion at a pace that outruns existing controls. Executives need someone fluent in both the intelligence-grade threat picture and the commercial reality of running a business through it.
Most organisations treat AI, robotics and emerging technology as a procurement question. The harder question is whether leadership teams understand the science well enough to set boundaries on what these systems should and should not do. Without that grounding, governance defaults to vendors, and disruptive innovation becomes something that happens to the business rather than something it directs.