Éthique de l'IA et technologie responsable
Des conférenciers qui interrogent les conséquences humaines des décisions algorithmiques, l’éthique des données et les technologies émergentes
Speakers Associates represents 149 speakers on Éthique de l'IA et technologie responsable, including Kemal Apaydin, Rahaf Harfoush, Limor Ziv, Harriet Farlow, Saakshar Duggal, Dr Sidney Shapiro, Tina Stowell, Timandra Harkness, Dame Wendy Hall, et Susi O’Neill.
Cybersecurity and digital identity decisions are being made at the architecture layer faster than most boards can scrutinise them. The standards that will govern extended reality, distributed ledger systems and biometric identity are being drafted right now in working groups most senior leaders cannot name. Once those standards harden, the choices embedded in them shape regulatory exposure and competitive position for the decade that follows.
AI capability is advancing faster than the organisations buying it can absorb. Boards are committing serious capital to systems whose behaviour will change before the contracts are signed, in markets where the regulatory floor is still moving. The question is no longer whether to invest. It is how to set strategy around technology that does not yet sit still.
Consumer trust is not declining because products are worse. Organisations are deploying AI and persuasive technology faster than they understand its effect on human behaviour. The commercial cost shows up as rising disengagement, eroding brand loyalty and deepening consumer scepticism.
Most enterprises now have an AI strategy on paper and very little operating advantage to show for it. Pilots stall, governance is improvised, and the gap between board ambition and frontline deployment keeps widening. Leaders need a credible operator who has built AI inside a Fortune 500 and shaped it inside the United Nations, not another commentator describing the trend.
Boards are being asked to make consequential decisions about AI systems they do not fully understand, on timelines set by competitors, regulators and the technology itself. The vocabulary used inside these conversations, alignment, capability, existential risk, governance under uncertainty, was largely built by a small group of thinkers before the commercial AI race began. Without that vocabulary, leaders end up either dismissing the risk or capitulating to it.
The rules that govern AI, data, and global platforms are being rewritten in Washington, Brussels and Beijing at the same time, and rarely in the same direction. Boards now have to make capital and product decisions inside a regulatory environment that no single jurisdiction controls. Reading that landscape, and acting on it before it forces your hand, is now a core leadership task.
Most boards still treat AI as a software question their CIO will solve. The story is bigger than that. The contest is over compute, fabs, energy supply, and the sovereign infrastructure that will decide which companies and which countries hold the next decade of pricing power. Leaders who frame AI as a productivity tool are already a strategy cycle behind.
Trust inside organisations is being tested faster than leaders can rebuild it. Restructuring, hybrid working, and the arrival of AI tools have stripped the assumptions that used to hold teams together. The result is a workforce that complies but does not commit, and decisions that get slower precisely when they need to be quicker.
Every organisation is now running an experiment on its own people. AI is reshaping how leaders think and how they decide, and most of them are watching it happen without a framework for what they are seeing. The productivity tools assume creativity is an output problem. The transformation programmes assume culture is a training problem. Neither assumption is true, and the gap between them is where the real cost is accumulating.
Most AI initiatives stall between the pilot and the operating line. Boards have approved spend, teams have shipped demos, and nothing in the actual product, process, or P&L has changed. The pressure now is to move from curiosity to deployed advantage, with governance that holds up to scrutiny and design choices that customers will actually use.
Women leave technology and senior roles at every stage of the pipeline, and the reasons are now well documented: a culture that rewards perfectionism over risk, and a workplace built for workers without caregiving responsibilities. Most organisations respond with policy statements and employee resource groups. What they need is a structural account of why their female talent is stalling and a tested set of interventions that work.
Once a financial or strategic commitment depends on AI, evidence is needed that the system placed into use can do the work that commitment assumes.