Tag Archives: technology

2024 in review

My great regret is that I always have very little time to write posts, and the emptiness of this blog does not reflect the numerous, great and stimulating scientific events and opportunities that I have enjoyed throughout 2024. As a last-minute remedy (with a promise to do better next year…hopefully), I try to summarize the landmarks here, month by month.

In January, I launched the Voices from Online Labour (VOLI) project, which I coordinate with a grant of about €570,000 from the French National Agency for Research. This four-year initiative brings together expertise from sociology, linguistics, and AI technology across multiple institutions, including four French research centres, a speech technology company, and three international partners.

In February with the Diplab team, I spent two exciting days at the European Parliament in Brussels, engaging in profound discussions with and about platform workers as part of the 4th edition of the Transnational Forum on Alternatives to Uberization. I chaired a panel with data workers and content moderators from Europe and beyond, aiming to raise awareness about the difficult working conditions of those who fuel artificial intelligence and ensure safe participation to social media.

In March, three publications saw the light. One is a solo-authored chapter, in French, on ‘Algorithmes, inégalités, et les humains dans la boucle‘ (Algorithms, inequalities, and the humans in the loop) in a collective book entitled ‘Ce qui échappe à l’intelligence artificielle‘ (What AI cannot do). The other two are journal articles that may seem a little less close to my ‘usual’ topics, but they are important because they constitute experiments in research-informed teaching. One is a study of the 15-minute city concept applied to Paris, realized in collaboration with a colleague, S. Berkemer of Ecole Polytechnique, and a team of brilliant ENSAE students. The other is an analysis of the penetration of AI into a specific field of research, neuroscience, showing that for all its alleged potential, it created a confined subfield but did not entirely disrupt the discipline. The study, part of a larger project on AI in science, was part of the PhD research of S. Fontaine (who has now got his degree!), also co-authored with his co-supervisors F. Gargiulo and M. Dubois.

In April, I co-published the final report from the study realized for the European Parliament, ‘Who Trains the Data for European Artificial Intelligence?‘. Despite massive offshoring of data tasks to lower-income countries in the Global South, we find that there are still data workers in Europe. They often live in countries where standard labour markets are weaker, like Portugal, Italy and Spain; in more dynamic countries like Germany and France, they are often immigrants. They do data work because they lack sufficiently good alternative opportunities, although most of them are young and highly educated.

I then attended two very relevant events. On 30 April-1 May, I was at a Workshop on Driving Adoption of Worker-Centric Data Enrichment Guidelines and Principles, organised by Partnership on AI (PAI) and Fairwork in New York city to bring together representatives of AI companies, data vendors and platforms, and researchers. The goal was to discuss options to improve working conditions from the side of the employers and intermediaries. On 28 May, I was in Cairo, Egypt, to attend the very first conference of the Middle East and Africa chapter of INDL (International Network on Digital Labour), the research network I co-founded. It was a fantastic opportunity to start opening the network to countries that were less present before, and whose voices we would like to hear more.

June also provided exciting opportunities, with a workshop on ‘The Political Economy of Green-Digital Transition‘ at LUT University in Lappeenranta, Finland.

In July, the final version of our article on ‘Who bears the burden of a pandemic? COVID-19 and the transfer of risk to digital platform workers‘ came out in American Behavioral Scientist.

August is a quieter month (but I greatly enjoyed a session at the Paralympics in Paris!), so I’ll jump to September. Lots of activities: a trip to Cambridge, UK, and a workshop on disinformation at the Minderoo Centre for Technology and Democracy; a workshop on Invisible Labour at Copenhagen Business School in Denmark; and a one-day conference on gender in the platform economy in Paris. Another publication came out: a journal article, in Spanish, on Argentinean platform data workers.

More publications in October: a book chapter, in Portuguese, on ‘Fabricar os dados: o trabalho por trás da Inteligência Artificial‘, and a journal article, in French, on the ethics and methodology of using graph visualizations in fieldwork (an older topic to which I’m still attached – and which takes renewed importance with today’s fast renewal of research ethics!).

At the end of October, and until mid-November, I travelled to Chile for the seventh conference of the International Network on Digital Labour (INDL-7), which I co-organised. It was an immensely rewarding experience. I took the opportunity to strengthen my linkages and collaborations with colleagues there. It was a very intense, and super-exciting, time: after INDL-7 (28-30 October), I spent a week in Buenos Aires, Argentina, where I co-presented work in progress at the XV Jornadas de Estudios Sociales de la Economía, UNSAM. I then returned to Chile where I gave a keynote at the XI COES International Conference in Viña del Mar, Chile, on 8 November, and another at the ENEFA conference in Valdivia (Chile) on 14 November. I also gave a talk as part of the ChiSocNet series of seminars in Santiago, 11 November.

December was my return to teaching… and planning for the new year! Of note, I was interviewed for a Swiss podcast.

The socio-contextual basis for disinformation

Within the Horizon-Europe project AI4TRUST, we published a first report presenting the state of the art in the socio-contextual basis for disinformation, relying on a broad review of extant literature, of which the below is a synthesis.

What is disinformation?

Recent literature distinguishes three forms:

  • ‘misinformation’ (inaccurate information unwittingly produced or reproduced)
  • ‘disinformation’ (erroneous, fabricated, or misleading information that is intentionally shared and may cause individual or social harm)
  • ‘malinformation’ (accurate information deliberately misused with malicious or harmful intent).

Two consequences derive from this insight. First, the expression ‘fake news’ is unhelpful: problematic contents are not just news, and are not always false. Second, research efforts limited to identifying incorrect information alone, without capturing intent, may miss some of the key social processes surrounding the emergence and spread of problematic contents.

How does mis/dis/malinformation spread?

Recent literature often describes the characteristics of the process of diffusion of mis/dis/malinformation in terms of ‘cascades’, that is, the iterative propagation of content from one actor to others in a tree-like fashion, sometimes with consideration of temporality and geographical reach. There is evidence that network structures may facilitate or hinder propagation, regardless of the characteristics of individuals: therefore, relationships and interactions constitute an essential object of study to understand how problematic contents spread. Instead, the actual offline impact of online disinformation (for example, the extent to which online campaigns may have inflected electoral outcomes) is disputed. Likewise, evidence on the capacity of mis/dis/malinformation to spread across countries is mixed. A promising perspective to move forwards relies on hybrid approaches mixing network and content analysis (‘socio-semantic networks’).

What incentivizes mis/dis/malinformation?

Mis/dis/malinformation campaigns are not always driven solely by political tensions and may also be the product of economic interest. There may be incentives to produce or share problematic information, insofar as the business model of the internet confers value upon contents that attract attention, regardless of their veracity or quality. A growing, shadow market of paid ‘like’, ‘share’ and ‘follow’ inflates the rankings and reputation scores of web pages and social media profiles, and it may ultimately mislead search engines. Thus, online metrics derived from users’ ratings should be interpreted with caution. Research should also be mindful that high-profile disinformation campaigns are only the tip of the iceberg, low-stake cases being far more frequent and difficult to detect.

Who spreads mis/dis/malinformation?

Spreaders of mis/dis/malinformation may be bots or human users, the former being increasingly controlled by social media companies. Not all humans are equally likely to play this role, though, and the literature highlights ‘super-spreaders’, particularly successful at sharing popular albeit implausible contents, and clusters of spreaders – both detectable in data with social network analysis techniques.

How is mis/dis/malinformation adopted?

Adoption of mis/dis/malinformation should not be taken for granted and depends on cognitive and psychological factors at individual and group levels, as well as on network structures. Actors use ‘appropriateness judgments’ to give meaning to information and elaborate it interactively with their networks. Judgments depend on people’s identification to reference groups, recognition of authorities, and alignment with priority norms. Adoption can thus be hypothesised to increase when judgments are similar and signalled as such in communication networks. Future research could target such signals to help users in their contextualization and interpretation of the phenomena described. 

Multiple examples of research in social network analysis can help develop a model of the emergence and development of appropriateness judgements. Homophily and social influence theories help conceptualise the role of inter-individual similarities, the dynamics of diffusion in networks sheds light on temporal patterns, and analyses of heterogeneous networks illuminate our understanding of interactions. Overall, social network analysis combined with content analysis can help research identify indicators of coordinated malicious behaviour, either structural or dynamic.