AI-etik och ansvarsfull teknik
Talare som granskar de mänskliga konsekvenserna av algoritmiska beslut, dataetikett och framväxande teknik
Speakers Associates represents 149 speakers on AI-etik och ansvarsfull teknik, including Kemal Apaydin, Rahaf Harfoush, Limor Ziv, Harriet Farlow, Saakshar Duggal, Dr Sidney Shapiro, Tina Stowell, Timandra Harkness, Dame Wendy Hall och Susi O’Neill.
Boards are being asked to make capital and workforce decisions on AI without a shared map of where the technology is actually heading. Internal teams default to either pilot-by-pilot caution or unchecked enthusiasm, and neither produces a defensible long-range position. What is missing is a credible read of what the next decade looks like, grounded in technology history rather than vendor marketing.
Most boards now accept that AI will change their business. Few have a defensible view on what it changes first, what it changes structurally, and what it does to the labour model their P&L assumes. The gap between accepting AI as a trend and treating it as a strategic variable is where serious organisations are exposed.
Most executives have mapped their AI technology landscape; far fewer have mapped the governance architecture being built around it. The EU AI Act now sets binding constraints on which AI applications can be deployed, which require conformity assessments, and which are prohibited entirely. Parallel frameworks at UN level will extend these obligations globally.
Most organisations make product, workforce, and policy decisions on data that under-represents half their market. The gap is structural, not incidental, and it shows up in safety failures, missed customers, and AI systems that inherit the bias of their training sets. Leaders who suspect this is happening rarely have a defensible way to find it, fix it, or explain it to a board.
Most organisations are spending heavily on AI without a clear view of which decisions the technology is actually supposed to improve. Models get shipped, dashboards proliferate, and senior leaders still cannot tell whether any of it is changing the quality of the choices the business makes. The missing layer is not more data or better algorithms, it is a disciplined way to connect AI outputs to the decisions a company is trying to get right.
Boards now treat information integrity as an operating risk, not a communications problem. Coordinated manipulation, hostile narratives and regulator pressure arrive on the same week, and most leadership teams do not have a shared language for any of it. The gap sits between the security function that sees the signals and the executives who have to act on them.
Boards are being asked to make large, irreversible bets on AI while the rules governing it are still being written. The people drafting those rules, and the people deploying the technology, rarely sit in the same room. Without a translator between Westminster, Silicon Roundabout and the executive committee, firms either over-invest in the wrong guardrails or under-invest and wait for enforcement to find them.
Regulators, lawmakers and users have stopped giving technology companies the benefit of the doubt. Privacy, safety and public policy are no longer back-office functions; they shape product, valuation and executive exposure. Most leadership teams are trying to build that capability after the scrutiny has already arrived, not before.
Boards have approved AI pilots, signed responsible-AI principles, and named ethics committees, and still cannot answer whether their deployed systems would survive a regulator’s audit or a serious public failure. The gap is not awareness. It is the operating distance between governance language and the decisions engineers, product leads and procurement teams actually make every week.
Boards are being asked to make capital and risk decisions on AI while the rules around it are still being written. The pressure is no longer whether to deploy, but how to deploy defensibly when regulators in Brussels, Washington and Beijing are pulling in different directions. Most executive teams do not yet have a clear view of who is setting those rules, on what timetable, and what compliance, data and infrastructure choices will look like on the other side.
Most large organisations have run AI pilots. Few have moved AI into operating reality at scale, with clear lines on governance, accountability and where it is allowed to make decisions. Boards now need a sharper read on what AI can actually do for their business, what it should not do, and how to deploy it without inheriting risks they cannot defend in front of regulators or customers.
Leaders talk about culture, trust and performance as if they are separate problems. They are the same problem, surfacing in different meetings. Teams disengage when the people above them cannot read the room, cannot hold a hard conversation, and cannot connect the strategy they are selling to the daily reality of the people being asked to deliver it.