Ethics & Policy
Mapping AI ethics narratives: evidence from Twitter discourse between 2015 and 2022
The findings of this paper consist of an overview of the hierarchical topic structure, a story map with geographic locations, and a topic evolution view integrating fragmented information.
Overview of hierarchical topic structure
To address our first research question (RQ1), about how AI ethics discourse is framed within Twitter discourse overall, we extracted all relevant tweets and clustered them into a hierarchical topic structure, as shown in Fig. 2. This structure consists of three layers. The first layer comprises seven main topics: Legal & Ethical, Society & Culture, Technology, Science & Research, Health & Safety, Education & Learning, and Business & Economics. Figure 2 presents a clear and intuitive visualization of the hierarchical structure of AI ethics discourse through the visualization of the sunburst chart. This shows the categories of each topic and the containment relationships between the hierarchies. Furthermore, the underlying 64 fine-grained topics (lowest level) have not been overlooked, covering mainstream public discourse and issues of small but critical scope, such as Intellectual Property in AI and Gender Discrimination.
Table 1 is a codebook for the topic of AI ethics discourse. It describes each category of topics in the top-level structure and provides examples of topics discussed within each category. An in-depth analysis of the topics of the hierarchical structure can be found in the Discussion section.
In addition to the fine-grained hierarchical structure analysis, the temporal trend of topic discussions over time also conveys rich meaning. we chose stream chart and bar chart to illustrate the changes of the seven main topics related to AI ethics from 1 January 2015 to 31 December 2022 as Fig. 3A shows. The overall volume of discussions on AI ethics was relatively low during 2015–2016, gradually increasing from 2017 and peaking in early 2020. There was a slight decline in 2020, possibly influenced by the pandemic outbreak, which may have diverted substantial discourse resources. After 2020, the discourse on AI ethics remained relatively stable. Figure 3B shows that among the seven topics, a significant portion of the discussions revolve around “Legal & Ethical,” surpassing 90% of the total tweet volume. The remaining six topics have relatively similar amount of discussion, with “Business & Economics” being the least discussed. This skewed distribution, where a few categories (also called heads) contain a large number of samples, while most categories (also called tails) have very few samples, conforms to a long-tail distribution (Anderson, 2012). The long-tail distribution of topics related to AI ethics reveals that although public concern in this field is focused on Legal & Ethical discussions, niche topics such as Education & Learning and Business & Economics are also significant and should not be overlooked (Agarwal et al., 2012; Mustafaraj et al., 2011).
Visual analytics of AI ethics discourse in Twitter
To answer RQ2, this study presents coherent and readable visual analytics from two parts, including story map and topic evolution diagram.
Story Map: The World and The United States as Examples
We constructed a global story map and a more detailed story map of the United States, which has the highest tweet volume, as an example. Among them, the global story map combines mainstream AI ethics discourse with geospatial information, presenting the distribution of seven topics worldwide. The American Story Map integrates mainstream AI ethics discourse, sentiment information, and spatial locations, built upon the changes in the number of tweets related to AI ethics in the United States from 2015 to 2022.
Figure 4 illustrates the distribution of AI ethics-related tweets worldwide. Most AI ethics discourse is concentrated in the United States and Europe. Some countries, such as China and Cuba, have limited use of Twitter, so their distribution data may not provide accurate references. The surrounding maps in Fig. 4 display the distribution of the seven topics worldwide, they are generally similar but with some subtle differences. For instance, discussions on “Health & Safety” in African countries are more prevalent compared to “Technology” and “Business & Economics,” while India has more discussions on “Technology” and “Education & Learning” compared to other topics.
To analyze the information conveyed by the story map in Twitter data further, we present an example using the United States. Figure 5A displays the distribution of AI ethics-related tweets in the United States. We observe that the discussion of AI ethics topics is most concentrated in California, New York, and Massachusetts. This concentration might be attributed to the large population size, numerous high-tech companies, and the abundance of universities in these three states. Considering the “long-tail distribution” characteristic of the seven topics related to AI ethics, although the tail-end topics constitute a relatively small proportion, they still reveal significant information. After excluding the most discussed topic “Legal & Ethical,” we illustrate the distribution of the remaining topics in Fig. 5B. More than 50% of states focus on “Science & Culture,” with “Technology” as the next prominent topic. Interestingly, New Mexico is most interested in the “Health & Safety” topic. This may be due to the diverse social and cultural backgrounds of the different federal states. For example, California and Washington State are home to numerous large tech companies. Still, California’s industries include globally renowned tourism and film industries, while Washington State is known for aerospace and agriculture, resulting in differing AI ethics discourse between the two states.
A The overall distribution of AI ethics discourse in the United States. B The distribution of the seven topics of AI ethics discourse across US states. C The distribution of positive sentiments in AI ethics discourse across US states. D The distribution of negative sentiments in AI ethics discourse across US states. E The word cloud distribution of positive sentiments in AI ethics discourse. F The word cloud distribution of negative sentiments in AI ethics discourse. G The normalized curve showing the number of AI ethics-related tweets over time in the USA, with significant AI-related events annotated for context.
Figure 5C, D present the distribution of positive and negative sentiments related to AI ethics discourse in various states. Interestingly, the top five states with the strongest positive sentiment are the same as the top five states with the strongest negative sentiment: California, New York, Massachusetts, Washington, and Texas. This result reflects the consistent intensity of public sentiment; regions expressing positive sentiments do not necessarily have reduced negative sentiments. Furthermore, we explored the content discussed behind these positive and negative sentiments and displayed them using word clouds. Figures 5E, F show the word clouds corresponding to positive sentiment and negative sentiment respectively. This indicates that people discuss similar topics with different sentiments, focusing on data, humans, and artificial intelligence. However, those expressing positive sentiments are more likely to see the positive impacts of data and AI on humanity, while those expressing negative sentiments demonstrate more ethical concerns and are also more concerned about the potential problems with data. Figure 5G provides statistical information on the trend of AI ethics discourse in the United States over time, serving as background information for the story map.
Topic evolution view
We integrated fragmented AI ethics discourse information into readable and coherent narratives. Figure 6A depicts the evolution of AI ethics discourse worldwide over time, with some critical events related to AI as background information. Figure 6B, C illustrate the evolution of AI ethics discourse from 2015 to 2022. The horizontal axis represents the timeline, while the vertical axis indicates the magnitude of each topic. Since this topic evolution diagram aims to display the evolution of topics, time and quantity serve as reference information for the distribution of topic bubbles. We first extracted the main topics from the discourse in January 2015, identifying “Internet” as the most discussed one in that month. Then, by calculating semantic similarity, we selected five topics semantically most related to “Internet” in 2015, such as AI-tech, Crypto, etc. The topic with the latest timestamp in the previous year then evolved into the following year’s five topics, and so on.
A The normalized curve showing the number of AI ethics-related tweets over time in the world, with significant AI-related events annotated for context. B, C Topic evolution graphs on Twitter from 2015 to 2022, using the topic “Internet” as an example. B shows period from 2015 to 2018, while C shows the period from 2019 to 2022. The same color represents topics belonging to the same category within the seven topics of AI ethics discourse. The x-axis in the figure represents the timeline, indicating the appearance of topics over time, while the y-axis represents the number of topics. The higher the bubble representing a topic is distributed on the Y-axis in the graph, the larger its relative quantity. Note that this graph is a schematic, and the time and quantity do not represent precise values.
Figure 6 takes ‘Internet’, the most frequent keyword in AI ethics discourse in January 2015, as an example of how the story metaphor framework can be used to build fragmented information into easy-to-understand stories in cross-level fine-grained social media data mining. For other topics, we can also select an event that happened at a particular time and explore the topic discussions or other events triggered by the event. This paper starts with the evolution of the ‘Internet’. Through the consolidation of fragmented information related to AI ethics from 2015 to 2022, combined with background information, we present a topic evolution view. In 2015, the formal launch of Ethereum and incidents such as hacking of Bitcoin exchanges sparked discussions on “Crypto” and “Cryptocurrency” related to AI technology. In 2016, following the theft of Ether from the DAO, an Ethereum-based intelligent contract organization, attention towards financial technology continued to rise. Blockchain technology gradually began to be used in the financial sector to ensure security and compliance. In 2017, the continued development of AI technology drew multidisciplinary attention. Single-discipline advancements were no longer sufficient to address complex real-world issues, leading to an emphasis on interdisciplinary research. In 2018, public discussions on ethics reached unprecedented levels. In May of the same year, the European Union formally implemented the General Data Protection Regulation (GDPR), setting a benchmark for AI ethics regulations. In addition to “Ethics,” the public showed increased interest in cultural and economic-related topics. In 2019, the trade war triggered fluctuations in the global economic market. Bitcoin plummeted, leading to widespread closures of mining operations. The strengthening of the US dollar and continuous interest rate hikes by the Federal Reserve caused emerging market currencies to collapse. Topics such as “Economic Studies,” “Business Analytics,” and “Market Research” became focal points of discussion. In 2020, the outbreak of COVID-19 consumed a significant portion of social media resources, resulting in fewer discussions related to AI ethics. As the pandemic spread, many countries implemented surveillance and tracking measures to control its spread, leading to ethical debates on privacy and surveillance. The pandemic-induced home isolation and remote work shifted learning and work patterns, making topics like “Ethics & Artificial Intelligence,” “Collaborative Learning Community,” and “E-Learning Teleformacion” new focal points of discussion. In 2021, the global gaming platform Roblox became the first metaverse concept stock listed on the New York Stock Exchange, sparking discussions on “Augmented Reality” and “Virtual Reality.” In April of the same year, the European Union proposed the Artificial Intelligence Act (EU AI Act), the world’s first comprehensive legislative attempt to address the phenomenon and risks of artificial intelligence. The establishment of this AI regulatory framework led to an increase in discussions on “Ethical AI.” In 2022, DeepMind successfully predicted the structures of approximately 200 million proteins from 1 million species using AlphaFold, covering almost all known proteins on Earth, ushering humanity into a new era of digital biology. In April, the international academic journal Science revealed the mystery of human genes, announcing the completion of the first complete map of the human genome. Topics such as “Neuromuscular Network,” “Molecular Biology,” “Cellular Biology,” and “Ergonomic Design” became focal points of discussion.
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
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
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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