Ethics & Policy
Secret cyborgs and their AI shadows, prompt middleware, dual governance, Chinese AI regulations ++
Welcome to another edition of the Montreal AI Ethics Institute’s weekly AI Ethics Brief that will help you keep up with the fast-changing world of AI Ethics! Every week, we summarize the best of AI Ethics research and reporting, along with some commentary. More about us at montrealethics.ai/about.
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Prompt Middleware: Helping Non-Experts Engage with Generative AI
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Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI
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The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
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The AI Revolution Is Crushing Thousands of Languages | The Atlantic
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Why the Chinese government is sparing AI from harsh regulations—for now | MIT Technology Review
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Speed of AI development stretches risk assessments to breaking point | Financial Times
Bots down: Several major AI services experienced significant outages and disruptions on June 4, 2024. OpenAI’s ChatGPT chatbot went down for many users worldwide on June 4, with the first reports of issues surfacing around 2:30am ET. In addition to ChatGPT, several other prominent AI services like Claude, Gemini, Perplexity, and Copilot also experienced downtime and disruptions on June 4.
Speculation galore: This led to speculation about a potential widespread infrastructure issue or internet-scale disturbance affecting multiple AI providers simultaneously. Some theorized that the surge of traffic to alternative AI services in the wake of ChatGPT’s outage may have overloaded their systems as well.
Why it matters: The widespread AI service disruptions highlighted the growing reliance on these tools and the significant impact outages can have on businesses and users who depend on them daily. AI service providers need to invest in robust infrastructure, redundancy, failover mechanisms, and improved scalability to handle usage spikes and prevent future outages. The incidents also sparked discussions about the risks of centralized AI systems and the importance of transparency and accountability from providers when failures occur.
Did we miss anything?
Every week, we’ll feature a question from the MAIEI community and share our thinking here. We invite you to ask yours, and we’ll answer it in the upcoming editions.
Here are the results from the previous edition for this segment:
It is interesting to see here that there is an almost equal balance between those who think that AI ethics processes are too complex vs. those who don’t. It would be enlightening to analyze further what are the factors that separate the two groups. Hopefully, those who answered that they found the processes to be too complex can read and benefit from last week’s article on Keep It Simple, Keep It Right: A Maxim for Responsible AI.
This week Maira G. wrote in to ask us about how to improve the Responsible AI program that they have at their organization, in particular, they found that they were having to undergo quite a few revisions to the setups that they had created in terms of policies and processes due to new releases and capabilities in AI models.
In this week’s article, we try and answer that question for Maira by looking at a framework that we use in our advisory work grounded in (1) proactive risk assessment, (2) scenario planning, and (3) ethical foresight. By extrapolating further than conventional methods and anticipating potential scenarios that might initially seem improbable, organizations can build AI systems that are responsible today and resilient to future challenges. The goal is to create AI governance structures that are adaptable, inclusive, and prepared for a wide range of outcomes. The key takeaways are:
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Anticipate Future Challenges: By looking beyond the obvious, you prepare for a wider range of outcomes.
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Integrate Diverse Perspectives: Combining insights from various fields helps in understanding complex trends.
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Adapt and Evolve: Flexible policies and continuous learning ensure the Responsible AI program remains relevant and effective.
Are there any specific approaches that you’ve found useful in making Responsible AI programs more resilient to changing AI capabilities? Please let us know! Share your thoughts with the MAIEI community:
Secret Cyborgs and Their AI Shadows: Navigating the Copilot+ PCs Frontier
The introduction of Copilot+ PCs marks a significant shift in the landscape of personal computing and organizational AI usage. While these devices offer tremendous potential for enhancing productivity and enabling advanced AI applications, they also present novel challenges like Shadow AI and secret cyborgs. As AI capabilities become more deeply embedded into everyday tools, organizations must proactively adapt their governance frameworks to ensure responsible and transparent usage.
Ultimately, the rise of Shadow AI and secret cyborgs underscores the need for a proactive and adaptive approach to AI governance. By embracing this challenge head-on, organizations can position themselves to thrive in an increasingly AI-driven world, ensuring that the power of AI is wielded responsibly and in alignment with their core values and objectives.
To delve deeper, read the full article here.
Given the above article about secret cyborgs and their AI shadows, what are some odd (and unexpected!) places you’ve seen AI starting to be used given that it is now so democratized in terms of access because of applications like ChatGPT?
We’d love to hear from you and share your thoughts with everyone in the next edition:
So you’re working on AI systems and are interested in Responsible AI? Have you run into challenges in making this a reality? Many articles mention a transition from principles to practice but end up falling flat when you try to implement them in practice. So what’s missing? Here are some ideas on building an AI ethics team that I think will help you take the first step in making it a reality.
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Get leadership buy-in
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Set up feedback mechanisms
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Empower people to make decisions
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Align with organizational values
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Make Responsible AI the norm rather than the exception
You can either click the “Leave a comment” button below or send us an email! We’ll feature the best response next week in this section.
Prompt Middleware: Helping Non-Experts Engage with Generative AI
Generative AI models, like ChatGPT, have facilitated widespread access to advanced AI assistants for individuals lacking AI expertise. Yet, a persistent challenge remains: the absence of essential domain knowledge required to effectively instruct these agents and to skillfully elicit good responses through the “art” of prompt engineering. This work explores the question of how interfaces can best convey expert practices to users when using generative AI models.
To delve deeper, read the full summary here.
The increasing prominence of Generative Artificial Intelligence is evidenced by its use in consumer-facing, multi-use text and image-generating models. However, this is accompanied by a wide range of ethical and safety concerns, including privacy violations, misinformation, intellectual property theft, and the potential to impact livelihoods. To mitigate these risks, there is a need not just for policies and regulations at a centralized level but also for crowdsourced safety tools. In this work, we propose Dual Governance, a framework that combines centralized regulations in the U.S. and safety mechanisms developed by the community to tackle the harms of generative AI. We posit that implementing this framework promotes innovation and creativity while ensuring generative AI’s safe and ethical deployment.
To delve deeper, read the full summary here.
Large language models (LLMs) fuel transformative changes across multiple sectors by democratizing AI-powered capabilities; however, a critical concern is the impact of internal biases on downstream performance. Our paper examines biases within ChatGPT and LLaMA in the context of job recommendations, identifying clear biases, such as consistently steering Mexican workers toward low-paying positions and suggesting stereotypical secretarial roles to women. This research underscores the significance of evaluating LLM biases in real-world applications to comprehend their potential for perpetuating harm and generating inequitable outcomes.
To delve deeper, read the full summary here.
The AI Revolution Is Crushing Thousands of Languages – The Atlantic
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What happened: The dominance of English in digital spaces, coupled with the surge in generative AI technology, poses a significant threat to linguistic diversity. Despite the vast number of languages spoken globally, the majority of online content and AI applications are in English or a select few other languages. This trend risks further marginalizing Indigenous and low-resource languages, potentially erasing them from the digital landscape altogether.
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Why it matters: Efforts are underway to address this imbalance, with initiatives like Masakhane striving to develop AI tools for underrepresented languages. There’s hope that AI models, trained on more commonly spoken languages, can exhibit some degree of universality, aiding in preserving minority languages. However, the challenges are immense, requiring extensive resources and collaboration to create and curate training data for thousands of languages.
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Between the lines: The rapid spread of technology and the internet may exacerbate the decline of minority languages, with younger generations losing interest in their ancestral tongues. To combat this, it’s crucial to engage native speakers in discussions about how AI can effectively serve their linguistic needs. Building platforms tailored to these languages’ linguistic and cultural contexts could ensure their presence and relevance in the digital sphere for future generations.
Why the Chinese government is sparing AI from harsh regulations—for now | MIT Technology Review
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What happened: Angela Huyue Zhang, a law professor, outlines a three-phase pattern in China’s tech regulation: initial leniency, sudden crackdowns, and eventual relaxation. This cycle is exemplified by the trajectory of tech giants like Alibaba and Tencent, which faced minimal scrutiny as they rapidly expanded, only to be hit with severe fines and antitrust investigations later.
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Why it matters: Zhang’s analysis underscores the volatile nature of China’s tech regulation, where shifts between laxity and stringency can profoundly impact companies and markets. Despite persistent issues like competition obstruction and privacy violations, regulators historically turned a blind eye until sudden crackdowns occurred. The government’s current emphasis on AI reflects its strategic importance for China’s economic growth, leading to comparatively loose regulation compared to Western counterparts.
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Between the lines: The Cyberspace Administration of China (CAC) plays a key role in AI regulation, prioritizing political control over technological innovation. While some restrictions aim to curb undesirable content, they may stifle innovation and competitiveness. Zhang suggests that significant AI misuse could trigger a sudden, severe regulatory response, highlighting the unpredictability and potential consequences of China’s regulatory landscape.
Speed of AI development stretches risk assessments to breaking point
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What happened: As AI technology rapidly advances, traditional methods for evaluating its performance, accuracy, and safety are struggling to keep pace. The emergence of more powerful AI models, fueled by significant investment from tech giants and venture capitalists, has rendered many older evaluation criteria obsolete in a matter of months rather than years.
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Why it matters: With AI systems constantly evolving, existing benchmarks quickly become outdated as new models effortlessly surpass them. This shift has moved the challenge of evaluating AI from academia to boardrooms, with CEOs prioritizing generative AI investments. Ensuring trustworthy AI products is crucial for gaining user trust, driving companies and governments to grapple with deploying and managing the risks associated with these advanced technologies.
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Between the lines: While platforms like Hugging Face offer user-driven leaderboards to assess AI models, companies require more tailored evaluations to meet specific needs. Building internal test sets and prioritizing human evaluation methods are becoming essential strategies for businesses navigating the complex landscape of AI model selection. Choosing the right AI model is as much about intuition and real-world testing as it is about metrics and benchmarks.
How dynamic pricing hinted at algorithmic pricing
👇 Learn more about why it matters in AI Ethics via our Living Dictionary.
Making Responsible AI the Norm rather than the Exception
This report prepared by the Montreal AI Ethics Institute provides recommendations in response to the National Security Commission on Artificial Intelligence (NSCAI) Key Considerations for Responsible Development and Fielding of Artificial Intelligence document. The report centers on the idea that Responsible AI should be made the Norm rather than an Exception. It does so by utilizing the guiding principles of: (1) alleviating friction in existing workflows, (2) empowering stakeholders to get buy-in, and (3) conducting an effective translation of abstract standards into actionable engineering practices. After providing some overarching comments on the document from the NSCAI, the report dives into the primary contribution of an actionable framework to help operationalize the ideas presented in the document from the NSCAI. The framework consists of: (1) a learning, knowledge, and information exchange (LKIE), (2) the Three Ways of Responsible AI, (3) an empirically-driven risk-prioritization matrix, and (4) achieving the right level of complexity. All components reinforce each other to move from principles to practice in service of making Responsible AI the norm rather than the exception.
To delve deeper, read more details here.
Fair allocation of exposure in recommender systems
Within the domain of recommender systems, algorithmic decisions regarding content exposure carry significant ethical implications, potentially marginalizing minority or disadvantaged content producers. In a series of works [2,3,4], we propose to define the fairness of ranked recommendations based on principles from economic fair division. Following these principles, we introduce new recommendation algorithms and show that they can distribute exposure more fairly among content producers while preserving the quality of recommendations for users.
To delve deeper, read the full article here.
We’d love to hear from you, our readers, on what recent research papers caught your attention. We’re looking for ones that have been published in journals or as a part of conference proceedings.
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
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.
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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