# Humanities and social sciences (HSS) and the challenges posed by AI: a French point of view

Laurent Petit

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Laurent Petit. Humanities and social sciences (HSS) and the challenges posed by AI: a French point of view. AI & Society: Knowledge, Culture and Communication, 2023, 39 (6), pp.2791-2797. 10.1007/s00146-023-01746-6. hal-05347455

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The humanities and social sciences (HSS) are being turned upside down by advances in artificial intelligence (AI), and their very existence could be threatened. These sciences are being profoundly destabilised by a dual process of naturalisation of social phenomena and fetishisation of numbers, accentuated by the development of AI (part 1). Both STM (science, technology, medicine) and HSS are facing major epistemological challenges, but for the latter they carry the risk of marginalisation (part 2). The humanities and social sciences remain the best equipped to question the social construct represented by the development of AI. However, this essential approach is not enough. We need to ask ourselves: how can the HSS reintroduce interpretation when they have less and less control over how data is put together? Only a balanced partnership between STM and HSS is likely to meet all these challenges (part 3). Using the case of education, which has long been at the forefront of developments in other sectors of social life, we would like to show how and on what priority issues such a partnership can be built (part 4).

# 1. AI disruption of HSS

Given the speciality for progress that the area has developed, hopes raised by AI are nothing new. This is all the more the case than in other domains as a result of self-fulfilling prophecies (Merton 1948), the most well-known being the defeat of the chess world champion Garry Kasparov to Deep Blue in 1997. But many such promises have been, purely and simply, broken (Jordan 2019), thereby contributing to the abundant growth of science fiction literature, raising as many hopes as fears.

Just as true is the fact that, for many years, artificial intelligence appears to have enjoyed a media hype already experienced by post-war cybernetics<fnref n="1" /> for example, a hype whose origins merit investigation. We will not be doing so here, as it requires its own research programme.

## 1.1. Connectionist AI, or the anticipated revolution

AI, given recent developments, seems to have provided a posthumous victory for the first wave of so-called ‘connectionist’ AI (which was inspired by cybernetics) compared to symbolic AI, which had for so long dominated the field.

The tension between these two approaches began with the birth of artificial intelligence and the line in the sand that it drew with its predecessor, cybernetics. The *symbolic* approach, which established the initial frame of reference for AI, represents a conventional kind of cognitivism: thinking consists of calculating symbols which have both a material reality and a representative semantic value. On the other hand, the connectionist paradigm considers thought to resemble a massively parallel calculation of basic functions - distributed across a neural network - whose meaningful behaviours only appear on the collective level as an emergent effect of the interactions produced by these basic operations (Cardon et al. 2018).

This approach gives weight to the theory behind a science that is already capable of fulfilling its promise to provide a system of instruments for social mediation thanks to the progress represented by *big data*, which is considered to be the dynamic management of heterogenous data, and should not be confused with massive data processing (which we will come back to in the second part).

Adopting this approach, whose consequences we will try to measure later on, can only be understood by making reference to a double process of naturalisation of social phenomena and the fetishisation of numbers, phenomena which are linked but which need to be distinguished at this stage.

<footnote n="1">¹ Cybernetics, or the science of the control of systems, is a neologism attributed to Norbert Wiener (1948).</footnote>
## 1.2. The naturalisation of social phenomena

A process of naturalisation, which is neither new nor homogenous, is therefore at work. Daniel Andler (2016) relies on philosophy to explain the contemporary position of naturalism. He neither outright subscribes to it nor rejects it, instead offering a nuanced critical analysis by closely following multiple research programmes, examining their capacity for reporting of reality. He provides a detailed focus on three up-and-coming domains, whose place within the sciences is as praised as it is feared: cognitive science, neuroscience and evolutionary biology. This punctilious approach, which is particularly welcome and well-documented, makes no attempt to hide the fact that these sciences are attempting to occupy a space which, until recently in the academic sphere, did not belong to them, leading to understandable rejection from humanities and social sciences who use other approaches to the same ends.

Beyond the scope of the sciences in a strict sense, what we prefer to call a process of naturalisation (rather than naturalism) has gained significant ground in fields that receive more media coverage, including when resulting proposals are emerging from leading figures with scientific backgrounds, many of whom from the field of biology. To take just a recent example, we will mention the biologist François Taddei who, without quoting Pierre Lévy (1994), incorporated an element of “collective intelligence” into his definition of a “learning society” (Becchetti-Bizot, Houzel, Taddei 2017).

The process of “naturalisation” at work is in fact twofold: entailing attempts, in one sense, to reduce human behaviour to its biological dimension (as we mentioned above) but also in the sense defined by Yves Jeanneret (2014), of what is no longer discussed. Even if we begin by looking at simple metaphors, we can see that little by little, they tend to develop into concepts, if only by default. However, the underlying concepts benefit from becoming explicit and being put into perspective. We will come back to this key point.

## 1.3. The fetishisation of numbers

In a remarkable work on the fetishisation of numbers adapted from courses given at the Collège de France between 2012 and 2014, Alain Supiot (2015) pays particular attention to the resurgence of scientism which began in the 1980s, and which goes hand in hand “with the ultraliberal belief in a global world presided over by the immanent laws of the economy, and populated with contracting particles driven by the calculation of their individual usefulness” (op. cit.). He shows how this belief spurred the market paradigm to extend into all human activities. These evolutions, he says, explain the current absolute reign of quantification<fnref n="2" /> and the shift from government to governance, a system whereby “quantification serves less to represent reality, and more to evaluate performance.” (op. cit.).

To describe contemporary developments, Alain Supiot (2016) talks about an “opposition which continues to this day, entailing on the one hand a return to biologism and to what Alain Prochiantz called “the strange fury of being an ape”, and on the other hand, a return to postmodernism which, conversely, claims to uproot the symbolism of biology in the name of “the arbitrary nature of signs”.” The “Google sciences” held in high regard by Chris Anderson (2008)<fnref n="3" /> are testament to this. They claim to be a better prediction of human behaviour than they are a revelation of their motives, for the sake of a behaviourism (Pasquale 2015) emanating from a process of naturalisation which could be considered successful.

This radical behaviourism, which is based on transformations generated by algorithmic governmentality<fnref n="4" />, and understood as “a certain type of (a)normative or (a)political rationale relying on the automated collection,

<footnote n="2">²He relies explicitly on the works on the critical history of statistics carried out by Alain Desrosières (2014).</footnote>

<footnote n="3">³ French translation (2019). For the “demonstration” in his article, Anderson relies heavily on physics and biology, two experimental disciplines in which the notion of model does not have the same meaning at all.</footnote>

<footnote n="4">⁴ Can “algorithmic governance” be discussed further by integrating the pertinent analyses of Supiot? Rouvroy and Berns (2013) say that “this algorithmic governance further entrenches the liberal ideal of an apparent disappearance of the very project of governance”.</footnote>
aggregation and analysis of massive quantities of data to model, predict and affect potential behaviours in advance” (Rouvroy, Berns 2013), can only be made credible through a prior process of naturalisation. What we are dealing with here is a groundless man or, as Antoinette Rouvroy and Thomas Berns put it, a statistical double that is detached from us. Paradoxically, it is the moment at which this detachment occurs that the temptation to naturalise behaviours becomes compelling in a number of disciplines. Conversely, we must dare to redefine these as “social facts”, consisting “of ways of acting, thinking, and feeling, external to the individual, and endowed with a power of coercion, by reason of which they exert control” (Durkheim 1895). They are thereby to be distinguished from organic and psychological phenomena.

2. The epistemological challenges that AI poses for STM (science, technology, medicine) and for HSS (humanities and social sciences)

The challenge that has been set applies to all research, not just to humanities and social sciences. Before we can ask ourselves how it affects HSS, we need to understand how the calculation revolution and *big data* apply to the STM field.

2.1. AI and paradigm shift

According to Bruno Bachimont (2015), science has moved from an epistemology of measurement to an epistemology of calculation or data. He begins by showing how the paradigm of measurement in STM works. When Bachimont said that correlation supersedes causality, or the epistemology of data supersedes the epistemology of measurement, we can specify that correlation studies naturally existed within the paradigm of measurement, but between a finite number of variables and based on hypotheses which themselves were shaped from theories.

This is true for HSS too, even if the search for correlations is not the most widely-used technique in this field. Consider Emile Durkheim for example, who is considered the father of a quantitative approach to sociology. On Durkheim's *Suicide* (1897), Jean-Claude Passeron (2001) says that, in search of an exemplary argumentation for quantitative sociology, he shows however, through his arguments, the active role that the interpretation of the "meaning" of actions plays in Durkheim's construction of the "three types of suicide" (sic)<fnref n="5" />.

But it would appear that this is no longer where the issue lies. Indeed, to evaluate the epistemological rupture made possible by the phenomenon of widespread digitalisation, we must avoid confusing *big data*, understood as the dynamic management of heterogenous data, with *bulk data*, understood as the management of large amounts of data made possible by an increased capacity for automated calculation. The former cannot exist without the latter, but the change is more than just quantitative.

2.2. Rupture, or coexistence?

This shift, according to Bachimont, operates a triple rupture: a rupture of data from its source and nature (currently, the collection of data relies on capturing information from a wide range of sources which are heterogenous in terms of their nature and format, as well as conforming to higher frequencies), a rupture of the processing from the data (processing which relies on the use of tools, generally of a mathematical and statistical nature), and finally, a rupture of what is shown from what is calculated (the visualisation of results, often presented in line with cartographic conventions inspired by techniques from the InfoViz, or *Information Visualisation* field). By way of a paradigm shift<fnref n="6" />, this rupture would lead to a new kind of “data science”.

3. The challenge to HSS

<footnote n="5">⁵Durkheim distinguished four types of suicide: egoistic suicide, altruistic suicide, anomic suicide and fatalistic suicide, but he barely touched on the last of these types, which only appears in an end-of-page annotation.</footnote>

<footnote n="6">⁶Éric Dagiral and Sylvain Parasie (in Menger, Paye 2017) contextualised this epistemological rupture by showing that on the contrary, in the different social spheres in which data science is disseminated, its plasticity allows it to perfectly adapt to different “local epistemologies” without ever standardising them.</footnote>
After these few remarks, we can assess the magnitude of the challenge facing HSS, at a time when they have never been so discredited (in terms of discourse and means provided). They can set about deconstructing prophetic discourses and uncovering the stakes of the saturation effect they impose on the public space. But will it prove sufficient?

### 3.1. What comes after the social construct is questioned?

An initial response seems obvious, as it is constitutive of multiple HSS disciplines: they are equipped to discuss notions, put them into perspective, deconstruct discourses, in short, to consider these advancements as a social construct, while protecting themselves from the criticism of relativism made against Bruno Latour when he studied the functioning of research laboratories (*La science en action*, 1989). This would involve putting the circumstances and protagonists responsible for their construction into perspective (Bourdieu talks about defatalisation; neither chance nor necessity). But a discussion of deconstruction (which overly resembles a radical criticism in the negative sense of the term, that which destroys without producing anything) does not seem adequate here. Rather, it would involve making it visible and understandable, like an exploded-view diagram or an anatomical figure.

But this would never be enough. We are effectively confronted with a major issue: how can interpretation be reintroduced without a mastery of the data construction methods? Anticipating the *big data* revolution, HSS will come up to speed with STM: statistic correlations based on the processing of a considerable amount of data will, in a way, reveal a hidden meaning, free from the ideological trappings of certain disciplines. This research will not so much be the construction of a discourse by interpretations based on facts, comparisons, investigations, etc., as the ‘objective’ discovery of formerly hidden realities: conducting research into social realities could be regarded as a kind of unveiling, without allowing for the subjectivity of the researcher to affect it.

But is there a way to avoid choosing “...between the power to act based on precise but blind correlation, and an intelligibility which permits understanding without allowing for effective prediction or decision-making” (René Thom 2009)<fnref n="7" />? HSS have a rightful place to claim, provided they do not remain confined to this alternative.

### 3.2. The construction of a balanced partnership

This place can only be found **with** the other sciences, through a scientific partnership as arduous as it is potentially fertile. We should not take what Anderson says about ‘Google science’ at face value, but there is no reason to contest that *big data* (and AI) change the way science is performed. Jean-Gabriel Ganascia (2019) prompts us in this respect when he mentions the heuristic value of automated calculation conducted outside of any theoretical model. Indeed, such an approach can help to create hypotheses which can then be put to the test, in the example used by Ganascia through the experimental approach - Claude Bernard, for medicine - but also, and we must insist on this point, through the science of interpretation. To summarise, HSS should not be reduced to a discourse *on* but should rather, building on their findings and methods, fully participate in the analysis of these new data sets. Therefore, the ‘Google science’ that Anderson calls for would be nothing more than a moment of scientific reasoning (Passeron 2001) which should then bring other approaches using other methods into play, as long as the goal is to understand, not just predict. This requires an acceptance of methodological eclecticism and interdisciplinary dialogue, otherwise the progress of *deep learning* can lead us to dead ends, as Ganascia highlights. In short, it appears obvious that HSS have as much to learn from AI as the latter does from the former. What is therefore needed is a joint dialogue, established on equal terms.

Perhaps it is not necessary to create new categories to achieve this. On this topic, we would like to point out the ambivalence - not to say the ambiguity - of the notion of “digital humanities”. Is the purpose to analyse human facts through digital science, or rather to humanise, in the strong sense of the term, sciences which traditionally do not concern humans in society? Can this be done without just serving as a stand-in or a back-up by accepting, for example, a confinement to the role of merely adding some ethical colour? On the contrary, yes if the aim is the humanisation of digital technology, in the sense of introducing concepts from HSS such as hindsight and the

<footnote n="7">⁷Cited by Bachimont (2015).</footnote>
critical approach. In other words, developments in artificial intelligence offer us the unique opportunity to rethink the possibility of presumption (all the more than proof) which lies at the heart of HSS.

### 3.3. Revealing worldviews

As Antoinette Rouvroy and Thomas Berns (op. cit.) point out, “ever since machines started to become more and more ‘autonomous’ and ‘intelligent’, they have remained reliant on their original design, intentions, scripts or scenarii, depending on how they were conceived. Since their conception (and whatever forms they took thereafter), they have borne the worldviews, conscious or unconscious expectations, and projections of their conceptors”.

But with each new form of artificial intelligence, updating these worldviews becomes less and less straightforward. Jean-Sébastien Vayre (2018), interested in artificial learning technologies applied to customer relations management, showed that in the case of artificial neural networks, they cover the task of devising three sociocognitive frameworks (the learning environment, the processing environment, and the political environment) which influence the forms of algorithmic socialisation of machines by humans. “To put it another way, this bias in their learning culminates in translational operators which transform the regularities embodied by *big data* into information which should allow for organisational communication to be automated.” (op. cit.). One difficulty is found in the fact that within each of these frameworks, the “designer” and the “implementer” (both very much human) play different roles, which require an empirical and detailed analysis. The task is not easy without minimal technical knowledge, or without applying skills rarely seen in our disciplines. The need to enlist the help of experts capable of presenting the design and operating principles of artificial learning procedures in simple terms is obvious.

## 4. The case of education

Here, we would like to illustrate our point through events taken from the education field. This is because it is necessary to reassert that despite common beliefs, education is not merely to be impacted by external social phenomena. On the contrary, the field has often been a testing ground for innovations which are then distributed on a wider scale, as Pierre Mœglin (1994) has shown to be the case for educational satellites. It is through the set of questions it poses that the field of education is likely to be a case, in the meaning defined in (Petit 2018).

### 4.1. Experimental ‘no significance’ studies

For a long time in the education field, experimental approaches in the form of randomised trials have been used to try to measure the efficacy of a given practice compared to another. Thomas Russel (2001) collected nearly 2500 studies dating back to 1928, comparing the results of students who attended courses taught face-to-face and those who attended correspondence courses remotely. The result of his investigation can be seen on a website, bearing the self-explanatory name NSDP, which stands for “*No Significant Difference Phenomenon*<fnref n="8" />”. In the sample group studied, the ways and means of the remote learning courses were varied, comprising printed documents sent to students, radio or television broadcasts, videos, or any other form of online resource. The majority of these studies, which focused on how effective the use of tools or mass media were for remote learning, showed no significant improvement among students involved. Should we therefore conclude that there is nothing going on here? Not necessarily. If we want to measure not only what the students accomplished, but also the way in which they conducted their learning process, then perhaps we should question what is evaluated by these kinds of evaluations, and supplement them with other methods. In short, we should move towards methods belonging to the science of interpretation (Passeron 2001), as was recently attempted in a large-scale experiment seeking to make lessons in amphitheatres more interactive (Lamine, Petit 2014).

The objective of these studies is certainly to improve learning processes. But what means are we giving ourselves to measure what this means in reality? This question that we ask ourselves today about AI must take into account meta-studies done in the past on the same subject with other techniques.

<footnote n="8">⁸ http://www.nosignificantdifference.org/</footnote>
## 4.2. Instrumentation of social mediations

An objective alliance straddling management science, cognitive science and computer science now seems to be poised to engage in the instrumentation of social mediation. In the education sector, it claims to renew the (already old) question of industrialisation<fnref n="9" /> via the promises of algorithmisation and what it promises to bring to the sector, especially on the sensitive subject of individualisation. The relative failure of MOOC - at the very least, compared to their stated original ambitions - is in our opinion due to the promise which, on this fundamental issue, they failed to keep. How can each registrant be offered a course that is tailored to them (beyond the few standard courses corresponding to predefined profiles, apparently defined by their creators) and above all, how can we assure that registrants are followed and supported in such a way that doesn't exclude their ability to exchange information with their peers on an online forum? We have now known for a very long time that this is where the key to success of distance learning lies, a method known for its high student dropout rates, a rate more or less found in MOOC<fnref n="10" />.

## 4.3. Automating individualisation?

In the past, the American psychologist B. F. Skinner (1904-1990) sought to promote a type of industrialisation which would reconcile individualisation with massification, that is, an opening up to the masses, in order to increase the productivity of education (Skinner, 1968). "In doing so he took the opposing view of W. Schramm and the heads of the programmes, at UNESCO and in other national and international organisations who, in the 1960s and 1970s, launched radio and television networks to bring the best courses to the highest possible number of students<fnref n="11" />." (Mœglin 2016). Skinner's attempts were short-lived, despite the significant resources that the United States had at their disposal. To explain this failure, Pierre Mœglin points to two reasons: firstly, the behaviourist inspiration of this programmed teaching was strongly criticised, especially by contemporary cognitivists, constructivists or socioconstructivists in the pedagogical field. Secondly, the technical means of the era were too rudimentary to individualise it for different paces of learning, which would allow for a true diversification of content and courses.

Jumping ahead a few decades, we will look at how today, artificial intelligence is likely to revisit these two points. As far as the pedagogical approach is concerned, current discourse emphasises the notion of individualisation. Pending further exploration, Antoinette Rouvroy and Thomas Berns encourage us to clearly distinguish between personalisation and individualisation. In their opinion, algorithmisation allows for the further personalisation of an offer or service offered on the basis of needs which have not been expressed, deferred objectively with on the basis of *data* or even unwillingly-left *traces*, you might say, without the protective shield of their own awareness, amounting to determining a *profile*. In this regard, pedagogic individualisation<fnref n="12" /> is concerned with the reality of the individual in all his complexity, with the goal of making him grow and progress. Herein lies an emancipatory project which cannot be summarised as a "segmentation of the market" (Rouvroy, Berns 2013). In terms of technological progress, if such progress is undeniable, has it been enough to perhaps - finally - attain the automated individualisation that for so long has been hoped for in education?

## 4.4. A focus on adaptive learning

<footnote n="9">⁹ Industrialisation should be understood here as the result of three interrelated processes defined by Mœglin (2016): technologisation (the use of technical devices that promote the saving of human labor time), rationalisation (the adoption of organisational and managerial methods designed to promote the optimisation of technical systems), and ideologisation (the advent of a mindset that promotes the first two processes).</footnote>

<footnote n="10">¹⁰Student dropout rates for distance learning are not easy to come by, as institutions have difficulty admitting them, and they even factor into their economic models. On this issue, see Mœglin (2010).</footnote>

<footnote n="11">¹¹Any resemblance the project shares with American MOOC platforms like Coursera, Udacity or EdX from 2012 onwards is no coincidence...</footnote>

<footnote n="12">¹²We are the ones transferring this reasoning to pedagogy, the authors cited have made no such application.</footnote>
Does adaptive learning not claim to go beyond a simple profiling of students? The official Eduscol website, summarising from the Wikipedia article, defines it as "an educational method which uses computer algorithms as well as artificial intelligence to orchestrate the interaction with the learner and deliver customised resources and learning activities to address the unique needs of each learner. Computers adapt educational resources according to students' learning needs, as indicated by their responses to questions, tasks and experiments. The technology is based on various fields of study including computer science, education, psychology, and neurosciences."

Adaptive learning websites are becoming more and more numerous, both in the United States and in France, whether they are an evolution of the services of long-established players in this field (such as publishers) or new entrants in a market that is considered promising. In the United States, we can mention Duolingo, Dreambox Learning, Knewton or Cogbooks; in France, Lalilo, Domoscio, E-Tipi Learning, Projet Voltaire or PIX. They are already being researched using several cross-methods specific to HSS. Their sites are analysed according to an ethnosemiotic approach; three types of actors (EdTech entrepreneur, publisher, engineer) will be the subject of semi-directive interviews aiming to better identify the nature of the adaptability allowed by the algorithms and AI in the proposed device, the way the actors analyse the market and the economic model envisaged.

### 4.5. A new interdisciplinary seminar

As we have seen, AI is currently re-raising the topic of education industrialisation, promising to turn what was once just a dream into a technical possibility. It will thereby allow for a shift from a supplementary technologisation which does not put machines in the places of teachers, guidance professionals, librarians or other staff, to a substitutive technologisation (at least in part), especially for those tasks deemed the least creative. It would therefore entail, according to Cédric Villani (2018) the "development of an empowering synergy with AI, by expanding the space for creativity in teaching".

This - at least partial - substitution will, according to Villani, open the way for the development of a new capacity to act for teachers. The worktime freed as a result would, for example, allow teachers to document and share their teaching methods.

These questions have been precisely studied for several decades by a stream of research on the industrialisation of education, creator of the eponymous seminar which has its roots in information and communication sciences as well as education science, and whose work has given rise to many publications including the annotated anthology of texts on industrialisation from 1913 to 2012 (Mœglin 2016).

In the continuity of these founding works, the SIAFO<fnref n="13" /> seminar (Séminaire Intelligence artificielle dans la formation) was created in 2021. Its work is based on the following problematic: how likely is AI to renew a project of education industrialisation, a project which is both already old and partially underway, in ways which will require rethinking? The results of this work will be published systematically in French and English.

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