Last week with the Diplab team, we 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.
Together, we delved into the intricacies of the human labor that fuels artificial intelligence and ensures safe participation to social media. Together, we discussed workers’ expectations, concerns and common struggles to move forward toward a world in which where technology serves all humans equally and responsibly.
Digital labor is at the heart of our evolving economies. To address the specific challenges and developments in the Middle East and Africa (MEA), we are launching a dedicated chapter of INDL for the region.
This conference provides a unique platform to present research related to the MEA region, both ongoing and/or burgeoning. The conference offers opportunities for scholars and practitioners to engage with topics such as platformization, automation, gig economy dynamics, and technology-mediated labor.
INDL-MEA will feature three tracks: one in Arabic, one in English, and one in French, reflecting the linguistic diversity of the region.
Topics
Submissions must be in reference to the MEA region, for instance: in perspective, case studies, or focus.
Submission topics may include but are not limited to
Case studies examining platforms, gig economy workers, and online digital labor in MEA
Exploring algorithmic management practices in work processes, recruiting, and HR in MEA
Issues of digital platform labor on gender and inclusion in the MEA region
Consequences of the shift to digital labor on workers, businesses, economies, and labor markets in MEA
Effects of remote work and digital labor on employee well-being and productivity in MEA
Policy responses to the rise of digital labor and automation in MEA, including regulatory measures and government intervention
Strategies for organizing digital workers and managing geographically distributed workforces in MEA
Intersectional perspectives on digital labor in MEA
Exploring AI and digital labor through a decolonial lens in MEA
Challenges posed by Generative AI to human labor in MEA
Submissions
We invite submissions of anonymized abstracts for papers, case studies, and policy briefs related to these topics. Abstracts, up to 500 words, can be submitted in Arabic, English, or French through our website INDL-MEA.
Important Dates
Deadline for submissions: January 31, 2024
Acceptance notification: February 15, 2024
Registration opens: TBA
INDL-MEA conference date: May 28, 2024
Together, let’s foster a thought-provoking dialogue and contribute to shaping the future of digital labor in the Middle East and Africa.
For more information, please see the INDL website.
I had the privilege and pleasure to visit Madagascar in the last two weeks. I had an invitation from Institut Français where I participated in a very interesting panel on “How can Madagascar help us rethink artificial intelligence more ethically?”, with Antonio A. Casilli, Jeremy Ranjatoelina et Manovosoa Rakotovao. I also conducted exploratory fieldwork by visiting a sample of technology companies, as well as journalists and associations interested in the topic.
A former French colony, Madagascar participates in the global trend toward outsourcing / offshoring which has shaped the world economy in the past two decades. The country harnesses its cultural and linguistic heritage (about one quarter of the population still speak French, often as a second language) to develop services for clients mostly based in France. In particular, it is a net exporter of computing services – still a small-sized sector, but with growing economic value.
Last year, a team of colleagues has already conducted extensive research with Madagascan companies that provide micro-work and data annotation services for French producers of artificial intelligence (and of other digital services). Some interesting results of their research are available here. This time, we are trying to take a broader look at the sector and include a wider variety of computing services, also trying to trace higher-value-added activities (like computer programming, website design, and even AI development).
It is too early to present any results, but the big question so far is the sustainability of this model and the extent to which it can push Madagascar higher up in the global technology value chain. Annotation and other lower-level services create much-needed jobs in a sluggish economy with widespread poverty and a lot of informality; however, these jobs attract low recognition and comparatively low pay, and have failed so far to offer bridges toward more stable or rewarding career paths. More qualified computing jobs are better paid and protected, but turnover is high and (national and international) competition is tough.
At policy level, more attention should be brought to the quality of these jobs and their longer-term stability, while client tech companies in France and other Global North countries should take more responsibility over working conditions throughout their international supply chains.
Most of my current research aims to unpack artificial intelligence (AI) from the viewpoint of its commercial production, looking in particular at the human resources needed to prepare the data it needs – whence my studies on the data work and annotation market. However, for once, I am focusing on AI as a set of scientific theories and tools, regardless of their market positioning; indeed, I have joined a team of science-of-science specialists to study the disciplinary origins and subsequent spread of AI over time.
In a newly published, open-acces article, we unveil the disciplinary composition of AI, and the links between its various sub-fields. We question a common distinction between ‘native’ and ‘applicative’ disciplines, whereby only the former (typically confined to statistics, mathematics, and computer science) produce foundational algorithms and theorems for AI. In fact, we find that the origins of the field are rather multi-disciplinary and benefit, among others, from insights from cognitive science, psychology, and philosophy. These intersecting contributions were most evident in the historical practices commonly known as ‘symbolic systems’. Later, different scientific fields have become, in turn, the central originating domains and applicators of AI knowledge, for example operations research, which was for a long time one of the core actors of AI applications related to expert systems.
While the notion of statistics, mathematics and computer science as native disciplines has become more relevant in recent times, the spread of AI throughout the scientific ecosystem is uneven. In particular, only a small number of AI tools, such as dimensionality reduction techniques, are widely adopted (for example, variants of these techniques have been in use in sociology for decades). But if transfer of AI is largely ascribable to multi-disciplinary interactions, very few of them exist. We observe very limited collaborations between researchers in disciplines that create AI and researchers in disciplines that only (or mainly) apply AI. The small core of multi-disciplinary champions who interact with both sides, and the presence of a few multi-disciplinary journals, sustains the whole system.
Inter- and multi-disciplinary interactions are essential for AI to thrive and to adequately support scientific research in all fields, but disciplinary boundaries are notoriously hard to break. Strategies to better reward inter-disciplinary training, publications, and careers, are thus essential. Of course the potential for AI to significantly advance knowledge is still (largely) to be proven, and there have been disappointing experiences with, for example, the comparatively limited effectiveness of these tools in research on Covid-19. In all cases, the status quo is not ideal, and important steps forward are now needed.
We establish these results by analyzing a large corpus of scientific papers published between 1970 and 2017, extracted from Microsoft Academic Graph through the AI keywords used by the authors, and explored with different relational structures among the scientometric data (keyword co-occurrence network, authors’ collaboration network).
Full citation: Floriana Gargiulo, Sylvain Fontaine, Michel Dubois, Paola Tubaro. A meso-scale cartography of the AI ecosystem. Quantitative Science Studies, 2023; doi: https://doi.org/10.1162/qss_a_00267
What shapes differences in how people get paid, are deemed productive, or receive respect? Alongside traditional explanations of social inequalities such as class, gender, age, disability, race, migration status, rural vs. urban residence, and others, a recent literature highlights the effects of digital divides. The digitally resourced have more opportunities across all life spheres, from consumption to education, work, and health. Ironically, though, digital technologies also generate new vulnerabilities by generalizing low-paid and contingent work. Digital labour platforms like Uber, Deliveroo and Upwork use data and algorithms to match clients with workers, construed as independent contractors, for one-off ‘gigs’ without any long-term commitment. These workers are largely exposed to the vagaries of the market and have limited or no social protection, although increasing efforts aim to bring labour law to bear on platforms.
Growing concerns that platform workers compare unfavourably to conventional employees have already attracted significant research and policy attention. But more remains to be done to fully understand how the recent rise of labour platforms has undermined the relationship between digitization and inequalities, adding a layer of complexity. Scattered, but growing evidence indeed suggests that platforms may be accelerating transmission to digital worlds of ’legacy’ inequalities for example vis-à-vis race and gender, while also fostering the proliferation of ’emerging’ inequalities that diminish users’ agency and augment the power of technology creators and big-tech multinationals. Especially platforms for remote online-only labour change the geographical scale at which these questions arise, projecting workers toward a competitive planetary market that relentlessly selects winners and losers.
To tackle these questions, I’m happy and honoured to announce that I have just been awarded a major grant (almost 570k euros, at marginal cost) by the French National Agency for Research (ANR) for a new 4-year study called VOLI: Voices from Online Labour. As a team effort that builds on a solid record of interdisciplinary collaborations, VOLI innovatively combines hypotheses and methods from sociology and neighbouring disciplines, notably large-scale corpus linguistics (I’ll explain why below), and relies on speech technology and artificial intelligence to tackle the rising economic risks that coalesce around the nexus between online platform labour, digitization, and social inequalities. The project leverages the power and potential of the very digital tools whose societal effects it studies, to develop an original and potentially transferable methodology.
The innovative idea that underpins the project is to tackle the problem through language, benefiting from recent advances in linguistics research and its capacity to recast methods and tools from artificial intelligence in a broad sense – including speech and language technology and machine learning techniques – to capture features and processes that used to escape its traditional methods. Despite the importance of linguistic tasks (such as translation, transcription, writing, and editing) in online labour platforms, linguistic methods have never been applied to the study of these workers before, and thus are best positioned to bring fresh insight. To this end, we have assembled a team composed of speech technology scientists, computational linguists specialized in multilingual and large-scale corpora analysis, and computational, digital, and labour sociologists. Expected results sustain our ambition to devise policy solutions to mitigate the effects of inequalities, and to support the individuals and groups that accumulate multiple sources of disadvantage.
To harness our previous research experience and ensure continuity, we focus on so-called ’micro-work’, the necessary but inconspicuous contribution of low-paid masses who annotate, tag, label, correct and sort data to fuel the digital economy, especially artificial intelligence. Because it is performed remotely and can be allocated to providers worldwide, micro-work differs from location-based platform ’gigs’ such as delivery and transport. It also differs from online-only jobs for freelancers, for example in computer programming and design, insofar as its extreme segmentation and standardization allow dispersing tasks to an undefined crowd instead of a selected individual (whence the alternative denomination of ’crowdwork’). Micro-tasks include, for example, recording one’s voice while reading aloud a sentence, labelling files, translating short bits of text, classifying contents in an image or webpage. They perform essential functions in the development of machine learning and artificial intelligence, from data generation and enrichment to quality controls of automated outputs. We give voice to these workers, often invisibilized by the automation narratives popular in the technology industry, in that we interview them about their lived experience, aspirations, motivations and perhaps regrets; and we rely on their voices as data for the simultaneous development of sociology, linguistics, and artificial intelligence (specifically, speech recognition) itself.
Indeed while bringing to the next level our sociological knowledge of the linkages between micro-work and digital inequalities, the methods that will be developed within this highly interdisciplinary project advance the study of the factors driving speech variation within the discipline of linguistics, augmenting language corpora with rich sets of metadata from sociological surveys, while also building and testing new and improved tools for automated transcription, with potential commercial applications.
I am the PI of the VOLI project which involves four research centres within France:
AI is not just a Silicon Valley dream. It relies among other things, on inputs from human workers who generate and annotate data for machine learning. They record their voice to augment speech datasets, transcribe receipts to provide examples to OCR software, tag objects in photographs to train computer vision algorithms, and so on. They also check algorithmic outputs, for example, by noting whether the outputs of a search engine meet users’ queries. Occasionally, they take the place of failing automation, for example when content moderation software is not subtle enough to distinguish whether some image or video is appropriate. AI producers outsource these so-called “micro-tasks” via international digital labor platforms, who often recruit workers in Global-South countries, where labor costs are lower. Pay is by piecework, without any no long-term commitment and without any social-security scheme or labor protection.
In a just-published report co-authored with Matheus Viana Braz and Antonio A. Casilli, as part of the research program DiPlab, we lifted the curtain on micro-workers in Brazil, a country with a huge, growing, and yet largely unexplored reservoir of AI workers.
We found among other things that:
Three out of five Brazilian data workers are women, while in most other previously-surveyed countries, women are a minority (one in three or less in ILO data).
9 reais (1.73 euros) per hour is the average amount earned on platforms.
There are at least 54 micro-working platforms operating in Brazil.
One third of Brazilian micro-workers have no other source of income, and depend on microworking platforms for subsistence.
Two out of five Brazilian data workers are (apart from this activity) unemployed, without professional activity, or in informality. In Brazil, platform microwork arises out of widespread unemployment and informalization of work.
Three out of five of data workers have completed undergraduate education, although they mostly do repetitive and unchallenging online data tasks, suggesting some form of skill mismatch.
The worst microtasks involve moderation of violent and pornographic contents on social media, as well as data training in tasks that workers may find uncomfortable or weird, such as taking pictures of dog poop in domestic environments to train data for “vacuuming robots”.
Workers’ main grievances are linked to uncertainty, lack of transparency, job insecurity, fatigue and lack of social interaction on platforms.
As part of a large, interdisciplinary European research project, we are seeking a motivated, open-minded student to join CNRS (specifically, the Centre for Research in Economics and Statistics, CREST) in Palaiseau, France, for three years.
The thesis aims to model the production and dissemination of ‘fake news’ in situations of uncertainty and socio-economic inequality. A rich sociological literature suggests that actors contextualise messages received and emitted as questions or answers, interpret them according to their recipients and senders, and assess their social acceptability within their own networks of relationships, taking into account their relative position. Building on this research, the goal is to identify the social processes underpinning misinformation-generating digital communications: collective identity, inequalities of status or authority, hierarchy of shared norms. This will enable interpreting the online social interactions through which actors collectively judge the (appropriate or inappropriate) quality of a message or information and then decide whether to relay or share it – and with whom. In particular, the thesis work will contribute to: 1/ drawing up a state of the art, mainly within sociology but open to the neighbouring disciplines which have also addressed these questions; 2/ illustrating and testing these theories through an empirical analysis of a digital database, mainly with quantitative methods, which may be enriched through a small complementary qualitative fieldwork; 3/ to contribute to the preparation of guidelines that help information professionals and policy-makers to detect the sources and modalities of emergence and propagation of misinformation.
The thesis will be done within the framework of the interdisciplinary project “AI-based-technologies for trustworthy solutions against disinformation” (AI4TRUST), funded by the European Union over the period 2023-2026, involving 17 partners (research institutions and media professionals) in 10 countries, and coordinated by Fondazione Bruno Kessler (Italy).
The AI4TRUST project aims to build a hybrid system, with advanced artificial intelligence solutions capable of cooperating with humans in the fight against disinformation. The new algorithms that will be developed in this framework, constantly checked and improved by human fact-checkers, will monitor multiple online social platforms in nearly real time, analysing text, audio, and visual contents in several languages. The resulting quantitative indicators, including infodemic risk, will be inspected under the lens of social and computational social sciences, to build the trustworthy elements required by media professionals.
The successful candidate will have the opportunity to join a group of highly motivated scientists and practitioners from across the continent; to participate in collaborations with other teams working on the project in an interdisciplinary framework; to attend regular meetings with the project’s principal Investigator, the scientists and experts involved, and public decision-makers; to present and publish research results in international conferences and journals.
The ideal candidate has a good background in quantitative sociology or in a STEM discipline (e.g., mathematics, statistics, computer science) with a strong interest in societal issues and challenges. A very good knowledge of English, an interdisciplinary approach and the ability to work in teams are essential.
Candidates should apply on the CNRS portal, where they will also find more details.
We are excited to announce the 6th Conference of the International Network on Digital Labor (INDL-6), scheduled to take place 9-11 October, 2023. The conference aims to bring together experts from various fields to discuss the latest research findings and share ideas on the topic of Digital Labor in the Wake of Pandemic Times. Following long-term technological trends as well as exogenous shocks, the field of digital labor is constantly expanding. This year’s INDL conference will be an excellent opportunity to exchange insights and perspectives, as well as a great way to make new friends among researchers, workers, policymakers, and practitioners who study the future of work, social justice, platforms, and artificial intelligence (AI).
The INDL-6 conference will be held in-person at the Weizenbaum Institute for the Networked Society in Berlin, Germany. It is co-organized by the International Labor Organization (ILO), the Digital Platform Labor (DiPLab) group, and Wissenschaftszentrum Berlin für Sozialforschung (WZB).
We encourage all interested researchers, post-graduate students, and practitioners to submit proposals that address aspects of digital labor, including but not limited to: gig economy, online labor, workplace surveillance, algorithmic management, AI-assisted recruiting, remote work, employee well-being, inequality, policy responses to Covid-19 crisis, regulation, organizing digital workers, gender and work, LGBTQ+ workers, intersectionality, disability, inclusion, AI, decolonial lens, informal labor markets, generative AI and work.
We welcome submissions that are interdisciplinary in nature and strongly encourage proposals by researchers and practitioners from the Global South across all topics.
The Call for Papers is available here and the deadline is 12 April.
We organized the one-day conference AIGLe on 27 October 2022 to present the outcomes of interdisciplinary research conducted by our DiPLab teams in French-speaking African countries (ANR HuSh Project) and Spanish-speaking countries in Latin America (CNRS-MSH TrIA Project). Both initiatives study the human labor necessary to generate and annotate the data needed to produce artificial intelligence, to check outputs, and to intervene in real time when algorithms fail. Researchers from economics, sociology, computer science, and linguistics shared exciting new results and discussed them with the audience.
AIGLe is part of the project HUSh (The HUman Supply cHain behind smart technologies, 2020-2024), funded by ANR, and the research project TRIA (The Work of Artificial Intelligence, 2020-2022), co-financed by the CNRS and the MSH Paris Saclay. This event, under the aegis of the Institut Mines-Télécom, was organized by the DiPLab team with support of ANR, MSH Paris-Saclay and the Ministry of Economy and Finance.
PROGRAM 9:00 – 9:15 Welcome session
9:15 – 10:40 – Session 1 – Maxime Cornet & Clément Le Ludec (IP Paris, ANR HUSH Project): Unraveling the AI Production Process: How French Startups Externalise Data Work to Madagascar. Discussant: Mohammad Amir Anwar (U. of Edinburgh)
10:45 – 11:00 Coffee Break
11:00 – 12:30 – Session 2 – Chiara Belletti and Ulrich Laitenberger (IP Paris, ANR HUSH Project): Worker Engagement and AI Work on Online Labor Markets. Discussant: Simone Vannuccini (U. of Sussex)
12:30 – 13:30 Lunch Break
13:30 – 15:00 Session 3 – Juana-Luisa Torre-Cierpe (IP Paris, TRIA Project) & Paola Tubaro (CNRS, TRIA Project): Uninvited Protagonists: Venezuelan Platform Workers in the Global Digital Economy. Discussant: Maria de los Milagros Miceli (Weizenbaum Institut)
15:15 – 15:30 Coffee Break
15:30 – 17:00 Session 4 – Ioana Vasilescu (CNRS, LISN, TRIA Project), Yaru Wu (U. of Caen, TRIA Project) & Lori Lamel (LISN CNRS): Socioeconomic profiles embedded in speech : modeling linguistic variation in micro-workers interviews. Discussant: Chloé Clavel (Télécom Paris, IP Paris)
Today’s artificial intelligence, largely based on data-intensive machine learning algorithms, relies heavily on the digital labour of invisibilized and precarized humans-in-the-loop who perform multiple functions of data preparation, verification of results, and even impersonation when algorithms fail. This form of work contributes to the erosion of the salary institution in multiple ways. One is commodification of labour, with very little shielding from market fluctuations via regulative institutions, exclusion from organizational resources through outsourcing, and transfer of social reproduction costs to local communities to reduce work-related risks. Another is heteromation, the extraction of economic value from low-cost labour in computer-mediated networks, as a new logic of capital accumulation. Heteromation occurs as platforms’ technical infrastructures handle worker management problems as if they were computational problems, thereby concealing the employment nature of the relationship, and ultimately disguising human presence. My just-published paper highlights a third channel through which the salary institution is threatened, namely misrecognition of micro-workers’ skills, competencies and learning. Broadly speaking, salary can be seen as the framework within which the employment relationship is negotiated and resources are allocated, balancing the claims of workers and employers. In general, the most basic claims revolve around skill, and in today’s ‘society of performance’ where value is increasingly extracted from intangible resources and competencies, unskilled workers are substitutable and therefore highly vulnerable. In human-in-the-loop data annotation, tight breakdown of tasks, algorithmic control, and arm’s-length transactions obfuscate the competence of workers and discursively undermine their deservingness, shifting power away from them and voiding the equilibrating role of the salary institution.
Following Honneth, I define misrecognition as the attitudes and practices that result in people not receiving due acknowledgement for their value and contribution to society, in this case in terms of their education, skills, and skill development. Platform organization construes work as having little value, and creates disincentives for micro-workers to engage in more complex tasks, weakening their status and their capacity to be perceived as competent. Misrecognition is endemic in these settings and undermines workers’ potential for self-realization, negotiation and professional development.
My argument is based on original empirical data from a mixed-method survey of human-in-the-loop workers in two previously under-researched settings, namely Spain and Spanish-speaking Latin America.
Data Big and Small 