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.
Most boards have approved AI strategies. Very few have AI in production at the heart of a regulated business. The gap between pilot enthusiasm and operating reality is where strategy stalls, governance gets nervous, and customer-facing teams quietly lose faith in the technology.
Most diversity programmes have stopped producing measurable change. Budgets stay flat or fall, while the political cost of running them rises. Leaders need someone who can rebuild equity as an operating practice inside talent processes, products, and AI tooling, not as a campaign that lives on the side.
Boards are being asked to deploy AI faster than they can govern it. The question is no longer whether to adopt the technology but how to make decisions about it that hold up under scrutiny from regulators, employees, and the public. Most organisations have no working model for that, only policies that lag the systems they are meant to oversee.
Organisations are deploying AI in hiring, healthcare, and operations before they understand whose assumptions are encoded in those systems. AI bias is not a data problem – it is a design problem, and it traces directly to the homogeneity of the teams building the tools. The second risk is less visible: research shows that humans routinely defer to automated systems in ways that go well beyond the reliability of those systems, including in high-stakes scenarios. Boards that have approved AI adoption have often not reckoned with either problem.
Most large organisations have funded AI programmes and run pilots. Most of those pilots never reach production. The gap is not technical capability. It is the absence of an outcome architecture that connects experimentation to structural change. Meanwhile, boards are approving AI investment without the governance frameworks to manage the risks that sit inside AI agents and automated decision-making systems.
Most organisations are adopting AI faster than their leaders can define what human leadership is actually for. Emotional judgment is being automated by default, not by design. The competitive advantage now belongs to organisations that treat empathy as a measurable capability, not a management soft skill.
Most organisations still treat cyber as an IT department problem. The attack surface has moved: it now runs through the personal devices, social profiles, and travel patterns of senior leaders, and through the open-source data their organisations leak every day. Boards need someone who can show them what an adversary actually sees, not another briefing on compliance.
Most leadership teams have formally committed to AI and data as strategic priorities. The harder problem is what comes next. Boards and executive committees that cannot interrogate vendor claims, distinguish genuine capability from hype, or set coherent data governance policy become dependent on specialists whose priorities may not align with theirs. Strategic intent without strategic fluency produces expensive, poorly governed technology programmes – and the gap is widening faster than internal capability is growing.
Autonomous systems, from self-driving vehicles to generative AI, are moving from lab to revenue faster than most boards can absorb. The strategic question is no longer whether the technology works. It is which timelines are real, which are marketing, and which regulatory and civil-liberties fights will decide who gets to deploy at scale.
Most security programmes are designed by defenders who have never sat on the attacker side of the screen. That gap shows up in the controls that get prioritised, the scenarios that get war-gamed, and the fraud losses that keep arriving through channels the team believed were covered. Closing it takes an honest account of how criminals actually choose their targets, move money, and defeat the layers a bank or retailer has spent years building.
Every executive team is being asked to deploy AI faster than their governance can keep up. The harder question, which boards now own, is which use cases should be refused. Bias inside the models is not the only risk; the bigger one is shipping systems into contexts where the cost of being wrong is borne by people the organisation cannot see.
Leaders assume that deploying AI leaves their own judgment intact, but that assumption has not been tested. Algorithmic systems shape beliefs and steer decisions from within organizations, through the architecture of information rather than through visible force. The organization that cannot distinguish its own conclusions from those it has been guided to reach has a governance risk without a name.