Ethics & Policy
Generative AI Ethics: 11 Biggest Concerns and Risks
Like other forms of AI, generative AI can affect ethical issues and risks pertaining to data privacy, security, energy usage, political impact and workforces. GenAI technology can also potentially introduce a series of new business risks, such as misinformation and hallucinations, plagiarism, copyright infringements and harmful content. Lack of transparency and the potential for worker displacement are additional issues that enterprises might need to address.
“Many of the risks posed by generative AI … are enhanced and more concerning than those [associated with other types of AI],” said Tad Roselund, managing director and senior partner at consultancy BCG. Those risks require a comprehensive approach, including a clearly defined strategy, good governance and a commitment to responsible AI.
Corporate cultures that use GenAI should consider the following 11 issues:
1. Distribution of harmful content
Generative AI systems can create content automatically based on text prompts by humans. “These systems can generate enormous productivity improvements, but they can also be used for harm, either intentional or unintentional,” explained Bret Greenstein, partner and generative AI leader at professional services consultancy PwC. An AI-generated email sent on behalf of the company, for example, could inadvertently contain offensive language or issue harmful guidance to employees. GenAI should be used to augment but not replace humans or processes, Greenstein advised, to ensure content meets the company’s ethical expectations and supports its brand values.
2. Copyright and legal exposure
Popular generative AI tools are trained on massive image and text databases from multiple sources, including the internet. When these tools create images or generate lines of code, the data’s source could be unknown, which might be problematic for a bank handling financial transactions or a pharmaceutical company relying on a formula for a complex molecule in a drug. Reputational and financial risks could also be massive if one company’s product is based on another company’s intellectual property. “Companies must look to validate outputs from the models,” Roselund advised, “until legal precedents provide clarity around IP and copyright challenges.”
Generative AI large language models (LLMs) are trained on data sets that might include personally identifiable information (PII) about individuals. This data can sometimes be elicited with a simple text prompt.
Moreover, compared with traditional search engines, it can be more difficult for consumers to locate and request removal of the information. Companies that build or fine-tune LLMs must ensure that PII isn’t embedded in the language models and that it’s easy to remove PII from these models in compliance with privacy laws.
4. Sensitive information disclosure
GenAI is democratizing AI capabilities and making them more accessible. This combination of democratization and accessibility, Roselund said, could potentially lead to a medical researcher inadvertently disclosing sensitive patient information or a consumer brand unwittingly exposing its product strategy to a third party. The consequences of unintended incidents like these could irrevocably breach patient or customer trust and carry legal ramifications. Roselund recommended that companies institute clear guidelines, governance and effective communication from the top down, emphasizing shared responsibility for safeguarding sensitive information, protected data and IP.
5. Amplification of existing bias
Generative AI can potentially amplify existing bias. For example, there can be bias in the data used for training LLMs, which can be outside the control of companies that use these language models for specific applications. It’s critically important for companies working on AI to have diverse leaders and subject matter experts to help identify bias in data and models, Greenstein said.
6. Workforce roles and morale
AI is being trained to do more of the daily tasks that knowledge workers do, including writing, coding, content creation, summarization and analysis, Greenstein said. Although worker displacement and replacement have been ongoing since the first AI and automation tools were deployed, the pace has accelerated as a result of the innovations in generative AI technologies. “The future of work itself is changing,” Greenstein added, “and the most ethical companies are investing in this [change].”
Ethical responses have included investments in preparing certain parts of the workforce for the new roles created by generative AI applications. Businesses, for example, will need to help employees develop generative AI skills such as prompt engineering. “The truly existential ethical challenge for adoption of generative AI is its impact on organizational design, work and ultimately on individual workers,” said Nick Kramer, vice president of applied solutions at consultancy SSA & Company. “This will not only minimize the negative impacts, but it will also prepare the companies for growth.”
7. Data provenance
GenAI systems consume tremendous volumes of data that could be inadequately governed, of questionable origin, used without consent or biased. Additional levels of inaccuracy could be amplified by social influencers or the AI systems themselves.
“The accuracy of a generative AI system depends on the corpus of data it uses and its provenance,” explained Scott Zoldi, chief analytics officer at credit scoring services company FICO. “ChatGPT-4 is mining the internet for data, and a lot of it is truly garbage, presenting a basic accuracy problem on answers to questions to which we don’t know the answer.” FICO, according to Zoldi, has been using generative AI for more than a decade to simulate edge cases in training fraud detection algorithms. The generated data is always labeled as synthetic data, so Zoldi’s team knows where the data is allowed to be used. “We treat it as walled-off data for the purposes of test and simulation only,” he said. “Synthetic data produced by generative AI does not inform the model going forward in the future. We contain this generative asset and do not allow it ‘out in the wild.'”
8. Lack of explainability and interpretability
Many generative AI systems group facts together probabilistically, going back to the way AI has learned to associate data elements with one another, Zoldi explained. But these details aren’t always revealed when using applications like ChatGPT. Consequently, data trustworthiness is called into question.
When interrogating GenAI, analysts expect to arrive at a causal explanation for outcomes. But machine learning models and generative AI search for correlations, not causality. “That’s where we humans need to insist on model interpretability — the reason why the model gave the answer it did,” Zoldi said. “And truly understand if an answer is a plausible explanation versus taking the outcome at face value.”
Until that level of trustworthiness can be achieved, GenAI systems should not be relied upon to provide answers that could significantly affect lives and livelihoods.
9. AI hallucinations
Generative AI techniques all use various combinations of algorithms, including autoregressive models, autoencoders and other machine learning algorithms, to distill patterns and generate content. As good as these models are at identifying new patterns, they sometimes struggle with teasing out important distinctions relevant to human use cases.
This can include creating authoritative-sounding but inaccurate prose or producing pictures with realistic-looking imagery but misshapen representations of humans that contain extra fingers or eyes. With language models, these errors can show up as chatbots inaccurately representing corporate policies, such as in the case of an Air Canada chatbot that misrepresented corporate policies regarding bereavement benefits. Lawyers using these tools have also been fined for filing briefs that cited nonexistent court cases.
Newer techniques like retrieval augmented generation and agentic AI frameworks can help reduce these issues. However, it’s important to keep humans in the loop to verify the accuracy of generative AI information to avoid customer backlash, sanctions or other problems.
10. Carbon footprint
Many AI vendors argue that bigger AI models can deliver better results. This is partly true, but it can often involve considerably more data center resources, either for training new AI models or running AI inference processes in production. The issue is hardly clear-cut. As some argue, improving an AI model that has the potential to reduce the carbon footprint of an employee traveling to work or the efficiency of a product could be a good thing. Conversely, developing that model could also exacerbate global warming or other environmental problems
11. Political impact
The political impact of GenAI technologies is a fraught topic. On the one hand, better GenAI tools have the potential to make the world a better place. At the same time, they could also enable various political actors — voters, politicians, authoritarians — to make communities worse. One example of generative AI’s negative impact on politics can be found in social media platforms that algorithmically promote or create divisive comments as a strategy for increasing engagement (and profits) for their owners over comments that find common ground but might not have the same click-through and sharing numbers.
These issues will continue to be thorny for years to come as societies sort out which GenAI use cases serve the public good and whether that should be the end goal.
Editor’s note: This article was updated in 2025 to include additional ethical issues and concerns stemming from the use of generative AI.
George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.
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
Ethics & Policy
Preparing Timor Leste to embrace Artificial Intelligence
UNESCO, in collaboration with the Ministry of Transport and Communications, Catalpa International and national lead consultant, jointly conducted consultative and validation workshops as part of the AI Readiness assessment implementation in Timor-Leste. Held on 8–9 April and 27 May respectively, the workshops convened representatives from government ministries, academia, international organisations and development partners, the Timor-Leste National Commission for UNESCO, civil society, and the private sector for a multi-stakeholder consultation to unpack the current stage of AI adoption and development in the country, guided by UNESCO’s AI Readiness Assessment Methodology (RAM).
In response to growing concerns about the rapid rise of AI, the UNESCO Recommendation on the Ethics of Artificial Intelligence was adopted by 194 Member States in 2021, including Timor-Leste, to ensure ethical governance of AI. To support Member States in implementing this Recommendation, the RAM was developed by UNESCO’s AI experts without borders. It includes a range of quantitative and qualitative questions designed to gather information across different dimensions of a country’s AI ecosystem, including legal and regulatory, social and cultural, economic, scientific and educational, technological and infrastructural aspects.
By compiling comprehensive insights into these areas, the final RAM report helps identify institutional and regulatory gaps, which can assist the government with the necessary AI governance and enable UNESCO to provide tailored support that promotes an ethical AI ecosystem aligned with the Recommendation.
The first day of the workshop was opened by Timor-Leste’s Minister of Transport and Communication, H.E. Miguel Marques Gonçalves Manetelu. In his opening remarks, Minister Manetelu highlighted the pivotal role of AI in shaping the future. He emphasised that the current global trajectory is not only driving the digitalisation of work but also enabling more effective and productive outcomes.
Ethics & Policy
Experts gather to discuss ethics, AI and the future of publishing
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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