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.
Most cyber breaches do not begin with a clever exploit. They begin with a person clicking, sharing, or trusting the wrong thing. Boards keep pouring budget into tooling while the human layer, where the real exposure lives, goes underdeveloped and largely unmeasured.
Boards keep hearing that frontier AI is either an existential threat or an inevitable productivity engine, and neither framing helps them set policy. Inside the firm, the practical question is sharper: which capabilities are safe to deploy, what governance is credible to regulators, and how do you tell hype from a real shift in the technology. Most leadership teams have no independent technical voice they trust to answer that.
Industry boundaries are moving faster than strategy teams can redraw them. Software firms, platforms and AI entrants now compete inside sectors that once felt structurally protected, and the rules of value capture have changed with them. Boards keep asking the same question: where in this ecosystem do we still own the customer, and where are we becoming a component in someone else’s stack.
Regulation and activist coalitions now shape more corporate outcomes than many of the competitive moves around which strategy frameworks are built. The forces that decide whether a factory gets built or a product reaches a shelf often sit outside the market. Leaders who only know how to compete lose ground to those who can read and shape the political environment around the business.
Boards and executive teams know they need to act on AI, but most are stuck between vendor pitches, pilot fatigue and a regulatory picture that keeps moving. The harder question is not whether to invest, but which decisions belong in the boardroom, which belong with the operators, and how to govern the technology without stalling it. Few advisors have sat on all three sides of that table: building the technology, running it at scale, and writing the policy that shapes its limits.
Most boards now have an AI policy. Very few have a defensible answer to what the policy actually controls when models are deployed across operations, products, and decisions about people. The harder question is how to keep AI ambition moving without losing public trust, regulatory standing, or internal credibility when the first serious failure lands.
Boards and investment committees are being told that AI is now embedded in their managers, their operations and their risk models. Most cannot independently verify what is genuine machine learning, what is a relabelled factor model, and what governance their fiduciary duty actually requires. The decision-makers writing the cheques do not yet have the diagnostic tools to ask the right questions.
Most boards now treat AI as a strategic priority without a grounded view of how the systems setting that pace are actually built. Executive advice tends to swing between technical detail no operator needs and speculation no fiduciary can act on. The view from inside a frontier lab is rarely in the room with the people who most need it.
Boards are being asked to govern sustainability, AI risk and inclusion at the same time, often with the same committee, and often with the same hour on the agenda. The instruments most directors were trained on were not designed for this. The question is no longer whether to address these pressures, but what defensible governance actually looks like when the political wind on each is moving in a different direction.
Most boards now have an AI position on paper. Very few have a confident view of what their organisation should actually do with the technology, on what timeline, and at what cost to existing structures. The gap between AI as a slide in the strategy deck and AI as a real operating capability is where senior teams quietly stall.
Organisations are racing to deploy AI without an equivalent investment in the ethical or human frameworks needed to govern it. The competitive pressure to adopt is overriding the slower, harder work of deciding what values to encode into systems that will operate well beyond any individual leadership team’s tenure. The decisions being made now are difficult to reverse – and most boards do not yet have the reference points to make them well.
Most boards now run two parallel conversations: how fast to adopt AI, and how to defend against attacks AI is making cheaper and harder to detect. The two rarely meet in the same room. Adoption races ahead while governance and trust catch up only after a breach forces the question.