Ethics & Policy
Building an AI Ethics Review Board
Tim King offers insight on building an AI ethics review board for the empathetic enterprise, part of Solutions Review’s coverage on the human impact of AI.
As artificial intelligence moves from experimental tools to enterprise-wide infrastructure, the stakes of every algorithmic decision grow exponentially. No longer confined to backend systems or data analytics, AI now shapes who gets hired, what price a customer sees, how performance is evaluated, and even what medical or financial opportunities are offered. These aren’t just technical outputs—they’re moral choices carried out at machine scale. And with that scale comes risk: bias, exclusion, overreach, opacity, and unanticipated harm. That’s why organizations serious about responsible innovation must move beyond vague ethical intentions and implement formal structures of accountability.
Enter the AI Ethics Review Board (AIERB)—the empathetic enterprise’s internal compass for ensuring AI aligns not just with business goals, but with human values. It’s not a bureaucratic hurdle. It’s a safeguard for dignity, fairness, and trust in a time when automated decisions can feel impersonal and impenetrable. For companies building an Empathetic AI Framework, the AIERB is where empathy becomes operationalized—where diverse voices review potential impacts, where human consequences are surfaced before systems go live, and where real authority exists to say, “Not yet,” or even “Not at all.” In this era of accelerated transformation, having a thoughtful, empowered, and interdisciplinary ethics board isn’t just best practice—it’s a moral and strategic necessity. Because as AI gets smarter, enterprises must get more human. And that starts with building a board that keeps people at the center of every decision machine.
Why Empathy Demands Ethics
At the heart of every responsible AI strategy lies a moral imperative: to ensure that technological advancement does not come at the cost of human dignity. This is where empathy and ethics converge. Empathy is the ability to understand and value human experience, while ethics is the discipline that translates those values into action.
When artificial intelligence begins to influence hiring, healthcare, policing, compensation, or opportunity, it no longer lives in the abstract—it shapes lives. And when AI makes decisions that impact people, organizations must make decisions about AI with care. This is not just about compliance with regulations; it’s about cultivating trust, preserving agency, and preventing harm. An AI Ethics Review Board (AIERB) serves as the operational embodiment of that empathy, ensuring that ethical questions are not deferred, ignored, or outsourced—but addressed openly, collaboratively, and with accountability.
In an empathetic enterprise, ethics is not a brake on innovation; it is the steering wheel. It helps organizations not only avoid harm, but design for fairness, transparency, and long-term legitimacy.
AI Ethics Review Board Example
The Role of an AI Ethics Review Board
An AI Ethics Review Board (AIERB) is more than a symbolic gesture—it is a structured, decision-capable body that exists to embed ethical reasoning into the core of how an organization develops, purchases, deploys, and monitors artificial intelligence.
As AI technologies touch increasingly sensitive domains, from employee surveillance to algorithmic hiring to customer profiling, the risks of unintended harm grow exponentially. The role of the AIERB is to identify those risks before they become outcomes. It acts as an internal conscience, asking the hard questions about fairness, transparency, consent, and power imbalance.
At a procedural level, the board is responsible for reviewing high-impact AI systems prior to deployment, ensuring those systems undergo rigorous impact assessments, fairness testing, and documentation of purpose and scope. It has the authority to approve, delay, or reject use cases based on ethical criteria. It may also be tasked with reviewing third-party vendor systems for alignment with the organization’s standards.
Importantly, the AIERB is not a one-time reviewer but a persistent governance mechanism. It monitors post-deployment outcomes, investigates complaints or red flags, and can recommend system changes or suspensions. In mature programs, the board reports regularly to senior leadership or even the board of directors, elevating AI ethics to the same level as financial risk or brand integrity.
Key Design Principles for an Empathetic AI Ethics Review Board
Designing an effective AI Ethics Review Board (AIERB) requires more than selecting a few senior leaders and assigning them oversight. It demands intentional architecture that reflects the interdisciplinary, high-stakes nature of ethical AI governance—especially within an empathetic enterprise.
First and foremost, the board must be cross-functional. AI ethics is not purely technical, legal, or philosophical; it is all of these and more. Board members should represent diverse perspectives from data science, legal, compliance, human resources, DEI, product, and front-line employee roles. Including rotating seats or external advisors can bring needed independence and critical distance. Second, the board must operate on a risk-based review trigger model.
Not every AI system merits full review, but any system affecting human livelihoods, legal rights, or access to opportunity should undergo mandatory ethical assessment. Clear criteria—based on impact, sensitivity, and reversibility—help prevent review bottlenecks while prioritizing human consequence.
Third, the board must have structured workflows and documentation protocols. Submitting teams should provide a standardized packet including an AI Impact Review, fairness and bias testing results, a Deployment Ethics File (DEF), and a plain-language purpose statement.
These inputs should be evaluated using a consistent framework that weighs risk, benefit, alternatives, and mitigation plans. Fourth, red flag and escalation pathways must be clearly defined. The board needs real authority—not just the power to advise, but the power to pause or halt deployments when ethical concerns are unresolved. Fifth, the board must have post-deployment oversight responsibilities. Ethical risk doesn’t end at go-live. The board should receive regular reports on model performance, incident trends, and system modifications that could trigger re-review. And sixth, the board must make space for stakeholder and employee voice.
Empathy means listening to those affected by AI—not just those who build it. This could include anonymous feedback portals, designated employee seats, or user research summaries as required review materials. These principles ensure the board is not just procedural, but protective—and that it reflects the lived experience of those who stand to benefit or be burdened by AI decisions.
Embedding Empathy into AIERB Governance
Embedding empathy into the governance of artificial intelligence means intentionally designing systems of oversight that prioritize human experience over mere efficiency. For an AI Ethics Review Board (AIERB), this means shifting the focus from compliance to compassion, from minimum viable risk to maximum responsible care.
It begins with the mindset that every AI system impacts real people—and that those impacts must be understood, anticipated, and mitigated with empathy as a guiding principle. In practice, this means the board doesn’t simply ask “Does this system comply with our standards?” but also, “How will this system feel to the person it affects?”
Empathy-driven governance incorporates scenario modeling that surfaces not just intended use cases, but worst-case human consequences. It demands that board members challenge design assumptions by stepping into the shoes of those being scored, monitored, assessed, or filtered by algorithms.
Embedding empathy also requires reviewing AI systems in context—not just as abstract technologies, but as tools embedded in complex social systems. A system that appears fair in testing may reinforce workplace hierarchies or cultural biases once deployed. An empathetic board probes these dynamics. It questions the power imbalances between who builds the system and who is subject to it. It considers the psychological toll of surveillance, the dignity of manual review, and the downstream ripple effects of automated decision-making.
Empathy is also embedded structurally through stakeholder representation. Giving impacted employees or users a voice in review decisions—whether through participation, anonymized testimony, or survey data—ensures governance is grounded in lived experience. And empathy means requiring explainability not as a technical feature, but as a moral obligation: if a system affects someone’s livelihood, they have a right to understand how.
Ultimately, embedding empathy into AIERB governance transforms the board from a gatekeeper of compliance into a guardian of trust. It ensures AI does not just function—but respects, protects, and dignifies the people it touches.
Measuring Ethical Oversight Effectiveness
To ensure that an AI Ethics Review Board (AIERB) is more than symbolic—more than a well-meaning committee with no teeth—organizations must define and track specific metrics that evaluate how well ethical oversight is functioning. In the context of an empathetic enterprise, measurement is not about vanity; it’s about verifying that structures designed to protect human dignity are actually doing so.
One of the most telling metrics is the percentage of AI systems reviewed by the board before deployment. This figure reflects whether ethical governance is being consistently applied or bypassed under pressure to ship fast or avoid scrutiny. A high review rate—especially for systems with high impact or sensitivity—demonstrates that ethical review is embedded in operational processes. A low rate suggests a breakdown in enforcement or culture.
Complementing this is the percentage of AI systems with completed Deployment Ethics Files (DEFs) and AI Impact Reviews. These documents capture the intent, assumptions, risks, mitigation strategies, and fairness testing related to each system. When completed thoroughly and reviewed systematically, they provide an auditable record of ethical due diligence and preemptive accountability. In sensitive domains, organizations should also track the percentage of AI deployments escalated to senior leadership or board-level oversight, as this reflects whether high-risk systems are being evaluated at the appropriate level of organizational responsibility.
Other meaningful indicators include the number of red flag incidents reported, the average time-to-resolution for ethics-related issues, and the percentage of appeals or complaints that result in system changes (e.g., model retraining, increased human oversight, or decommissioning). These metrics show not only how responsive the organization is to ethical concerns, but whether governance mechanisms have real corrective power.
Additionally, the frequency of AIERB meetings, average attendance rates, and percent of retrained or materially altered systems that are re-reviewed help gauge whether governance is being maintained over time or allowed to lapse post-launch.
Together, these metrics allow leadership to identify blind spots, measure cultural compliance, and reinforce ethical rigor. They send a clear message that the AIERB is not a rubber stamp—it is a critical institution that ensures AI systems do not drift silently from helpful to harmful. By measuring ethical oversight, the enterprise affirms that trust and accountability are not abstract values, but operational priorities.
A Governance Framework Built for the Future
In a world where artificial intelligence is evolving faster than regulation and impacting lives faster than most organizations can track, the need for forward-looking ethical governance has never been greater. The AI Ethics Review Board (AIERB), when structured thoughtfully and operated with empathy, becomes more than a safeguard—it becomes a strategic advantage. It ensures that innovation is not pursued blindly, but with moral clarity and human respect.
As enterprises scale their use of AI across hiring, productivity, personalization, risk modeling, and more, the potential for both benefit and harm grows. The future will not be kind to companies that ignore this duality. Trust, reputation, employee loyalty, and regulatory readiness will increasingly hinge on the visibility and credibility of AI governance practices.
A governance framework built for the future must be flexible enough to evolve with the pace of technology, yet principled enough to remain anchored in enduring human values. It must integrate empathy not as an afterthought, but as a design input—baked into review checklists, stakeholder interviews, system documentation, and final approvals.
It must accommodate cross-border deployments, third-party tools, and foundation models whose internal logic may be opaque even to their creators. And most of all, it must be people-centered. AI systems are not just code—they are policies in action. They affect real people, in real ways, every day.
The empathetic enterprise recognizes that ethics is not a limitation—it is a form of leadership. By building and empowering an AIERB, organizations declare that their AI systems will not only be effective, but just. Not only powerful, but accountable. Not only innovative, but inclusive. Such a framework builds more than compliant systems—it builds resilient, future-ready organizations that earn trust and deserve it.
Note: These insights were informed through web research and generative AI tools. Solutions Review editors use a multi-prompt approach and model overlay to optimize content for relevance and utility.
Ethics & Policy
AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?
I don’t trust AI, but I use it all the time.
Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.
Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:
We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.
And, yes, it’s you he’s talking about:
I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?
Genuine
Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:
There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.
His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here to some degree, because there’s not a lot happening elsewhere:
To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.
I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.
Originality
That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:
Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.
But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.
More Human Intelligence is needed, it seems:
What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.
My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.
And there’s one last ‘tough love’ question to be posed:
What if we’re not special? Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.
My take
This was a candid worldview that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.
I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’, I shouted. Back came the reply, ‘Oh, the Internet’s gone down’. ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless.
Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing”s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
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