Ética da IA e Tecnologia Responsável
Oradores que questionam as consequências humanas da tomada de decisão algorítmica, ética de dados e tecnologia emergente
Speakers Associates represents 149 speakers on Ética da IA e Tecnologia Responsável, including Kemal Apaydin, Rahaf Harfoush, Limor Ziv, Harriet Farlow, Saakshar Duggal, Dr Sidney Shapiro, Tina Stowell, Timandra Harkness, Dame Wendy Hall e Susi O’Neill.
Boards are pouring resources into AI and seeing thinner returns than promised. Regulatory scrutiny is rising in parallel. The two pressures converge at the same operational layer, and that is where most deployments quietly fail.
Senior teams know the AI race rewards speed and punishes caution, even when caution is what their own risk function is asking for. Coordination across competitors looks naive; unilateral restraint looks like ceding ground. The question is how to operate, and govern, inside that pressure without sleepwalking into outcomes no one in the room actually wants.
Sustainable advantage has collapsed for most early-stage businesses. Distribution is cheap, features are copied within weeks, and capital alone no longer protects a category position. The companies that hold ground are the ones whose customers, contributors and earliest believers are bound to the product by something the balance sheet cannot buy.
Most companies have spent a decade publishing diversity statements without moving the numbers on women in senior leadership. The gap between policy and outcome is now a board-level credibility problem. The harder question is what disciplined, measurable inclusion practice looks like when public commitments alone have stopped persuading employees, investors, or regulators.
Most organisations have AI governance policies. Very few have a principled account of what those policies are actually trying to govern. The result is compliance frameworks that cannot answer the questions boards now face: when AI acts, who is responsible, and why.
Technology-first approaches to AI and digital transformation tend to produce systems that solve technical problems, not organisational or civic ones. When the people affected by those systems have no stake in how they are designed or governed, trust erodes and adoption fails. The gap between deployment speed and governance readiness is where most digital strategies break down.
Most organisations have run AI pilots. Far fewer have managers who can govern AI decisions, interrogate model outputs, or redesign a process around an agentic system. The gap is not tooling. It is a workforce of decision-makers who do not yet know enough about AI to lead with it.
Boards now operate inside a thicker regulatory perimeter than at any point in the post-2008 cycle, with competition, digital and capital markets rules tightening at EU and national level at once. Most leadership teams read these moves as compliance cost, not as a market signal. The blind spot is structural. Pricing, M&A, data strategy and capital allocation are all being repriced by regulators while executives still treat regulation as a downstream constraint.
Leaders are running organisations inside an information environment they no longer control. Algorithmic distribution, generative AI and coordinated manipulation now decide what stakeholders believe about a company, a product or a policy long before facts catch up. The question is no longer whether to engage with platform risk, but how to operate, communicate and govern when shared reality itself has fractured.
Boards have approved AI strategies they cannot fully explain, govern, or defend. Pilots multiply, ethical frameworks lag, and the human side of the operating model erodes faster than anyone planned. The question is no longer whether to deploy AI, but how to do it without losing the judgement, trust, and accountability that hold the enterprise together.
Most organisations now run two AI agendas in parallel and neither one is working. The compliance agenda is ahead of the strategy agenda, and the strategy agenda is ahead of the operating model. Boards need a coherent way to think about AI as economic infrastructure, not as a procurement question, while the technology is still moving faster than their policies, their hiring, and their planning cycles can absorb.
Boards are being asked to make ten-year commitments on technologies that change every six months. Most leadership teams lack a decision architecture for this: they either freeze, or they pilot endlessly without operational deployment. The unresolved question is how to commit capital and reorganise work around AI without betting the firm on a single forecast.