There are a number of pros and cons to hiring a Chief AI Ethics Officer, as suggested in an earlier article. On the upside, such an appointment makes it clear that the organization is taking ethics seriously. Authority and responsibility for enforcing standards across the enterprise are centralized in the hands of one individual rather than being fragmented around the business. In theory at least, the role should also be removed from revenue-generating or other technical pressures.
But there are downsides too. These include the risk of Chief AI Ethics Officers becoming largely symbolic, or even isolated, if the role is not well integrated into the rest of the organization. If not clearly enough defined, the role could also potentially overlap with legal, compliance, or diversity, equity and inclusion positions, potentially causing conflict.
Another challenge relates to the difficulties involved in quantifying ‘success’ in this area, particularly in a way that is meaningful to the C-suite. As Dr Christina Inge, an Instructor for the AI in Marketing Graduate Certificate at Harvard University, says:
To be effective, this role needs real power, not just a fancy title and no budget.
A more sustainable hub-and-spoke model
A more “sustainable” approach in the shape of a hub-and-spoke model is starting to make its presence felt though, according to Wyatt Mayham. He is Co-Founder and Chief Executive of Northwest AI Consulting.
The ‘hub’ takes the form of a Chief AI Officer (CAIO). A member of the C-suite, they set AI strategy and standards and own ethical governance. Mayham explains:
Separating ethics from strategy is a mistake. It treats ethics as a cost center or a police force. A CAIO with a dual mandate ensures that ethics are embedded into the entire AI lifecycle, from conception to deployment. This role acts as a bridge between the CTO and technical teams, the Chief Risk Officer and risk management, legal counsel, and the CEO. Their remit is to maximize the value of AI while minimizing its risks. The main pro is having a single, accountable leader driving both innovation and safety. The con is that finding someone with this incredibly diverse skillset is extremely difficult.
Inge agrees that the success of the role is based on three key factors. The first is that leaders, whether CAIOs or Chief Ethics Officers, must have cross-functional authority and report directly to the C-suite rather than to legal or comms leaders:
Ethics needs teeth. This isn’t an advisory role – it’s a governance role. It also requires fluency in both tech and values. You need someone who can talk to engineers and ethicists – and make both feel heard. There’s also the issue of soft power. Persuasion matters. An effective ethics lead builds buy-in, not just policies.
An excellent start
The ‘spoke’, meanwhile, consists of a distributed network of ethics champions that are embedded in business units and product teams. Responsible AI Councils, which include representatives from legal, IT, information security, compliance, data governance and data science, are another option. Their role is to support the implementation of the AI ethics leader’s strategy, standards and policies to ensure ethics does not fall by the wayside under the weight of other priorities.
Kjell Carlsson is Vice President Analyst on the Analytics and AI team at research and advisory firm Gartner. He is seeing the creation of AI Councils become more commonplace and believes they are an “excellent start”. But there are difficulties too, he says:
The struggle is to make them work productively. They can easily become entities that talk about implementation and don’t execute. Or they become overly restrictive coming up with semi-legitimate, non-risky use cases formulated on requirements that are impossible to meet.
The third layer consists of multi-skilled, multi-disciplinary teams. These include technical experts, such as data scientists and engineers to test for AI bias, drift and other weaknesses in AI models.
Domain experts are required to understand the context in which the AI system will be deployed and spot any potential real-world harms too. Legal and compliance experts are also necessary to navigate the rapidly changing regulatory landscape.
The aim is to train and empower participants at each level to identify and mitigate risks, with the aim of creating a culture of responsibility. But as Carlsson points out:
The biggest gap in leadership is recognizing that you need to incentivize people to do this and have the necessary processes in place. Unless leaders require it and make clear it’s important, things will dissolve as this kind of approach requires ongoing repetition and enforcement before it’s internalized.
Ethics as risk management discipline
Mayham agrees that getting it right is not easy:
The pro [of this approach] is scalability and real-world impact. The con is that it requires significant investment in training and a strong governance structure to avoid inconsistency.
A final consideration when trying to embed ethics into the organization, meanwhile, is the importance of independent, external audits and certification. Mayham explains:
Just as you have financial audits, you need AI audits. The pro is objectivity and stakeholder trust. The con is that it’s a snapshot in time. It can’t replace the need for continuous, embedded ethical oversight.
Ultimately though, he believes:
The secret is to treat AI ethics not as a philosophical problem but as a risk management discipline…the best way to address the challenges is through rigorous documentation, adversarial testing or ‘red teaming’, and maintaining ‘human-in-the-loop’ systems for high stakes decisions.
The shift to operationalizing ethics
As Inge points out though, few organizations today have adopted this hub-and-spoke model in any meaningful way to date:
The gold standard hybrid – centralized ethics lead, embedded team practices and third-party audits – is still aspirational for most. What I’m seeing more often are partial implementations: a responsible AI committee that meets quarterly but lacks authority. Or some bias-testing built into development workflows without an overarching strategy. That’s not nothing, but it’s not enough.
However, she does expect this hybrid approach to grow over time as a result of pressure from regulators, investors and consumers. Moreover, as organizations move beyond the pilot stage and onto real-world AI deployments, Inge believes they will inevitably realize that the current “patchwork style of governance” is neither scalable nor sustainable. She explains:
The tipping point will be when compliance risk becomes more tangible – think lawsuits, denied insurance, or failed procurement bids because ethics wasn’t demonstrably baked in.
This situation will, in turn, result in ethics becoming operationalized. At this point, organizations will shift from a ‘should we care?’ stance to a ‘how do we do this without killing innovation?’ approach, Inge says:
We’re at the very early stage here. Most organizations are still talking about ethics. Very few are operationalizing it at scale. The catalyst will be a mix of regulation [such as the European Union’s AI Act] and public backlash. Think a major AI-related scandal that exposes harm and results in legal or financial consequences. We haven’t had our ‘Facebook/Cambridge Analytica moment’ for AI yet, but it’s coming. When that moment hits, the conversation will shift overnight from ‘do we need ethics?’ to ‘why didn’t we do this sooner?’
My take
AI ethics will (hopefully) over time move from being a nice-to-have to a legal necessity as government legislation increasingly forces the issue. This could lead to ‘ethical AI’ branding becoming a competitive market differentiator.
It could also lead to ‘AI ethics’ morphing into ‘AI Assurance, believes Mayham, as the focus moves from high level principles to a more practical approach. This would generate demand for testing, auditing, and certifying AI systems to ensure their safety and reliability, potentially creating a new ‘AI Assurance’ ecosystem in the process.
