Ethics & Policy
AI and Human Rights: Protecting Data Workers
While many organizations are reasonably beginning to consider the environmental impacts of AI, especially with the growing energy demands of generative AI, greater focus is still needed on the human cost embedded in the development of AI models.
This International Workers’ Day, as we celebrate labor movements and workers’ rights across the globe, we are reminded of the ongoing efforts to uphold human dignity and foster equity in the workplace. Data enrichment workers who perform the essential role of training, validating, and fine-tuning AI models are one of the many groups of workers who are still overlooked, lack recognition, and lack protections.
It is clear that building responsible AI means building responsible AI data supply chains. While the specifics of the EU Corporate Sustainability Due Diligence Directive (CSDDD) get ironed out, the debate around this regulation underscores the need to leverage risk-based due diligence practices grounded in internationally accepted human rights principles. Specifically, it is important to strengthen human rights and environmental protections across global supply chains, to improve conditions for data enrichment workers globally.
The Responsible AI Movement Needs to Include Data Workers
AI relies on data, but before that data can power algorithms, it must first move through a vast global data supply chain built by data enrichment workers. These workers label, categorize, and moderate data and information to make it usable for AI systems. Yet despite their essential role, data enrichment workers remain largely invisible and undervalued. The AI data supply chain is opaque, fragmented, and unregulated, leaving workers vulnerable to precarious working conditions, low wages, and even mental health impacts from reviewing traumatic content.
“Responsible AI cannot be achieved without responsible treatment of the people whose labor makes AI possible.”
Through our work at PAI, leading workshops, producing case studies, developing a resource library and targeted resources, and proposing pathways for responsible data enrichment, we’ve learned that addressing these issues at scale requires ecosystem-wide accountability. Two central and practical challenges stand out, making it difficult for the industry to change practices:
- Internal decision fragmentation: internal decisions that meaningfully impact workers’ conditions are currently distributed across multiple teams and functions.
- External complexity: the complexity and scale of the supply chain means that multiple organizations across a complex data supply chain are shaping decisions that impact workers’ livelihoods.
While these challenges require formalization and standardization of practices, they are not impossible to overcome. Other industries, such as garments, minerals, manufacturing, have faced similar supply chain dilemmas. Their experience shows us that existing human rights principles and governance frameworks can be adapted to AI data supply chains as well. We don’t need to start from scratch. We can apply what we already know works.
Bridging Human Rights and Data Supply Chains
We’re encouraged to see momentum building with more human rights impact assessments now including the impact on data enrichment workers. Our Guidelines for Responsible Data Enrichment Practices have been incorporated into the OECD’s upcoming Due Diligence Guidance on Trustworthy AI, signaling momentum on a global scale. However, work is still needed to bridge the gap between responsible data supply chain practices and human rights principles.
Organizations, including PAI, the OECD, and United Nations are already making the connection between human rights due diligence frameworks and responsible data supply chains. To advance this dialogue, we hosted a webinar, Human Rights Due Diligence Frameworks & Ethical Data Enrichment Supply Chains, bringing together experts from the UN OHCHR B-Tech initiative, the OECD’s Responsible Business Conduct group, and industry leaders from Cisco, Intel, and Google DeepMind. This discussion brought together diverse perspectives spanning human rights expertise, experience bridging principles across various supply chains, and experience implementing responsible AI practices. Five key insights emerged from that conversation, which we believe will enable us to better understand and tackle challenges in developing responsible AI data supply chains:
- Human rights frameworks offer a strong foundation for protecting data enrichment workers: Internationally recognized human rights principles provide trusted foundational guidelines for navigating complex supply chains. They prioritize the rights of the most vulnerable and can help identify appropriate actions for preventing, mitigating, and remedying harms along the AI data ecosystem. These internationally recognized frameworks have been tested in complex contexts with multiple levels of responsibility, much like the data enrichment ecosystem.
- Existing infrastructure can be leveraged: Many companies already have teams and processes focused on supply chain human rights. These can be expanded to address data enrichment work, accelerating progress without reinventing the wheel.
- Data enrichment brings unique risks: While data enrichment workers face some challenges similar to those in traditional supply chains, they also face distinct issues unique to the mentally taxing work of data enrichment (e.g. the mental and emotional cost of reviewing traumatic content). Due diligence tools developed for traditional supply chains will need to be carefully adapted to address the unique challenges of work that has psychological harms.
- The fragmented and varied nature of data supply chains must be addressed: Data enrichment work happens across offices and homes, through various employment structures including full-time work, contractors, subcontractors, gigwork. This makes it difficult to map out who is doing what and where. Addressing this complexity will require more transparency and standardized accountability mechanisms.
- Worker voices are essential: Finally, we cannot design ethical AI supply chains without directly engaging workers themselves. Cross-sector collaboration, including with worker organizations, must be at the center of future frameworks.
Why This Matters
Data is the essential building block of AI. If the people who curate, label, and enrich that data are treated unfairly, it undermines not only the integrity of those systems, but also their safety, quality, and trustworthiness. Ignoring the conditions of data enrichment workers isn’t just a human rights failure, it’s also a technical and business risk.
By leveraging existing human rights frameworks and intentionally embedding responsible data enrichment practices within them, we can build AI systems that are not only more ethical, but also safer and more reliable.
This International Workers’ Day, let’s commit to building AI that respects every contributor, especially those whose labor remains unseen but essential. The foundation for responsible AI already exists in human rights frameworks. Now, it’s up to us to build on it.
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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