Intelligence artificielle et sciences sociales | Cairn.info

Le transhumanisme : une anthologie (2020)

2020-11-24T00:00:00+01:00

Pages 1 to 3| Pages de début
Pages 5 to 14| Introduction | Franck Damour, Stanislas Deprez, Alberto Romele
Pages 17 to 27| 1. Julian Huxley ou l’invention du transhumanisme ? | Jean-Yves Goffi
Pages 29 to 42| 2. L’ultra-humain selon Teilhard de Chardin | François Euvé
Pages 43 to 53| 3. Nikolaï Fiodorov ou la voie russe du transhumanisme ? | Julija Naett Vidovic
Pages 55 to 68| 4. John Desmond Bernal : quand le transhumaniste était marxiste | Benjamin Norguet
Pages 69 to 85| 5. L’emotion machine de Marvin Minsky | Fernand Doridot
Pages 89 to 103| 6. Neil R. Jones : quand la science-fiction rêve d’éternité | Ugo Bellagamba
Pages 105 to 117| 7. Le surhumain selon Robert Ettinger | Franck Damour
Pages 119 to 135| 8. Robert Anton Wilson : ésotérisme, science et transhumain | Rémi Sussan
Pages 137 to 151| 9. Max More, aux racines du transhumanisme du XXI^e siècle | Salomé Bour
Pages 153 to 169| 10. Vernor Vinge et l’invention de la Singularité | Franck Damour
Pages 171 to 185| 11. Le rapport NBIC | Apolline Taillandier
Pages 187 to 196| 12. La Déclaration Transhumaniste : matrice fragile du mouvement | Gabriel Dorthe
Pages 199 to 212| 13. Libérer l’éthique avec Nick Bostrom et Toby Ord ? (ou comment j’ai appris à ne plus m’en faire et à aimer la technique) | Fabrizio De Filippi, Alberto Romele
Pages 213 to 228| 14. Anders Sandberg : éthique du cerveau augmenté | Vincent Calais
Pages 229 to 247| 15. Julian Savulescu et la bienfaisance procréatrice | David Doat
Pages 249 to 263| 16. James Hughes : le Citizen Cyborg, de l’humain à la personne | Stanislas Deprez
Pages 265 to 283| 17. Martine Rothblatt : transhumanisme et transgenderisme | Laurence Allard
Pages 285 to 302| 18. Humain 2.0 : le « corps-culture » de Ray Kurzweil | Marina Maestrutti
Pages 303 to 319| 19. Lepht Anonym : transhumanisme de cuisine | Gabriel Dorthe
Pages 321 to 322| Les auteurs
Pages 323 to 327| Pages de fin

Quatre nuances de régulation de l’intelligence artificielle | Contrôler l'intelligence artificielle ? | Réseaux (2022/2 N° 232-233)

2022-05-23T00:00:00+02:00

By investigating the debates around the issues of AI regulation, we found that definitional problems were at the heart of normative conflicts on the means of subjecting AI to “social control”, whether technical, ethical, legal, or political. By taking the varied significations of AI as a common thread of analysis, this article contributes to the understanding of normative tensions over its control. We propose a mapping of places, actors and approaches that shows how the debates around the control of AI are structured according to four differentiated normative arenas: transhumanist speculations on the dangers of superintelligence and the problem of controlling its alignment with human values; the self-accountability of researchers developing a science entirely devoted to the technical certification of machines; denunciations of the harmful effects of AI systems on fundamental rights and the control of power rebalancing; finally, European regulation of the market through the control of security in relation to AI products and services.

Robots vs algorithmes | Contrôler l'intelligence artificielle ? | Réseaux (2022/2 N° 232-233)

2022-05-23T00:00:00+02:00

An open letter published in 2015, signed by Elon Musk, Stephen Hawking, Steve Wozniak and Noam Chomsky, warned of the existential risks facing humanity as a result of new developments in AI. This letter constitutes a milestone in the reactivation of a set of narrative and normative resources aimed at bringing a critical debate on the progress of AI into the public arena. Successive waves of promises and warnings about artificial intelligence have occupied public debate in a spectacular way. Using NLP methods applied to a large corpus from the English-language press, this article shows that the focus on algorithms and AI has been growing steadily in the media sphere over the past five years. The corpus is structured around two semantic spaces that constitute two predominant regimes of critique, one based on the injustices produced by algorithms, and the other on fears of AI’s and robots’ autonomy. A comparative analysis of these spaces shows that they mobilize distinct technological and human agents, issues, and time frames. By developing a critical discourse on the damage caused by these technologies, the media sphere contributes not only to shaping public opinion but also to defining the forms of acceptability of these calculating agents.

Les narvals et les licornes se cachent-ils pour mourir ? | Contrôler l'intelligence artificielle ? | Réseaux (2022/2 N° 232-233)

2022-05-23T00:00:00+02:00

The sale of Montreal-based Element AI to Service Now Inc. in November 2020 took the Quebec and Canadian artificial intelligence ecosystem by surprise. How could the start-up achieve narwhal status—that of a Canadian company with a capitalization of more than a billion dollars—and be celebrated by the government, the media and the business community, only to be bought out a few years later “for a song”? In this article, we present the case of Element AI, including both its rise and its fall, as an ideal-typical “cyberization of power” in which regulation is facilitative, remote and able to perceive control and communication as two ends of a single feedback loop. While the quick emergence of Element AI is characterized by its quest for “super-credibility”, expressed through partnerships with authoritative actors and institutions, its collapse is the sign of a desynchronization which met with rebukes and contradictions, even from the government. We have much to learn from this shift from justification to criticism since it points towards a vacuum within the Montreal AI ecosystem, as well as its difficulty in projecting itself even into a near future.

Le principe d’explicabilité de l’IA et son application dans les organisations | « Éthique de l’IA » : enquêtes de terrain | Réseaux (2023/4 N° 240)

2023-09-21T00:00:00+02:00

The explainability of Artificial Intelligence (AI) is cited in the literature as a pillar of AI ethics, yet few studies explore its organizational reality. This study proposes to remedy this shortcoming, based on interviews with actors in charge of designing and implementing AI in 17 organizations. Our results highlight: the massive substitution of explainability by the emphasis on performance indicators; the substitution of the requirement of understanding by a requirement of accountability; and the ambiguous place of industry experts within design processes, where they are employed to validate the apparent coherence of ‘black-box’ algorithms rather than to open and understand them. In organizational practice, explainability thus appears sufficiently undefined to reconcile contradictory injunctions. Comparing prescriptions in the literature and practices in the field, we discuss the risk of crystallizing these organizational issues via the standardization of management tools used as part of (or instead of) AI explainability.

L’émergence d’enjeux éthiques lors d’expérimentations de logiciels d’intelligence artificielle | « Éthique de l’IA » : enquêtes de terrain | Réseaux (2023/4 N° 240)

2023-09-21T00:00:00+02:00

This article brings together the perspectives of the sociology of innovation and the ethics of technology. It highlights the heuristic scope of the concept of experimentation for reflection on the ethics of Artificial Intelligence (AI), in the field of radiology. More specifically, we mobilize three types of experimentation (experiments in and with technology, and tacit experiments) to describe and analyse four work environments where AI-based detection software is tested in senology and traumatology. We highlight the ways in which these devices are appropriated (or rejected), as well as the resulting ethical concerns, drawing on the above-mentioned derivatives of the concept of experimentation. AI ethics raises three sets of questions: the first concerns the steering, scope and learning process of these experiments, and ultimately their (more or less) democratic nature; the second concerns the accuracy of what the software delivers, and consequently the risks of error involved, in a context where analytical work is delegated (at least partially) to algorithms; and the third concerns the quality of the service provided and its fairness, within the healthcare system, as well as the content of professional identities. AI ethics are thus embedded in concrete situations of use and embodied in professional worlds, unlike ‘principled’ and ‘future’ ethics.

« Mais l’algo, là, il va mimer nos erreurs ! » | « Éthique de l’IA » : enquêtes de terrain | Réseaux (2023/4 N° 240)

2023-09-21T00:00:00+02:00

Machine learning techniques algorithmically process input data which are then manually annotated to identify their salient features for model training purposes. This click work, while tedious and often unacknowledged, actually shapes a reference ‘truth’ for the AI, which in turn largely determines the results. This article examines the ways in which this work is carried out and its effects, based on a case study of the design of a tool for the automatic anonymization of court decisions at the Cour de Cassation (French Supreme Court). An ethnographic and interview survey highlights the plurality of skills implemented by the agents in charge of data annotation. The article shows the importance of representational and moral systems in the implementation of this activity, and therefore for the functioning of AI.

La prédiction du risque en justice pénale aux états-unis : l’affaire propublica-compas | « Éthique de l’IA » : enquêtes de terrain | Réseaux (2023/4 N° 240)

2023-09-21T00:00:00+02:00

An article published by the independent non-profit news media Pro Publica in 2016 argued that Compas software, used in the United States to predict recidivism, was ‘biased against blacks’. The publication sent shockwaves through the public sphere, fuelling broad debate on the fairness of algorithms and the merits of risk prediction tools – debates that had previously been limited to specialists in criminal justice. Starting with the ProPublica-Compas case, we explore the various facets of this controversy, both in the world of data science and in the world of criminal justice. In the media sphere, the Compas affair brought to the surface the potential abuses associated with algorithms, and it intensified concerns surrounding artificial intelligence (fear of AI replacing human judgment, worsening of inequalities and opacity). In the academic world, the subject was pursued in two separate arenas. First, in the arena of data sciences, researchers focused on two issues: fairness criteria and their mutual incompatibility, showing just how problematic it is to translate a moral principle into statistical indicators; and the supposed superiority of machines over humans in prediction tasks. In the criminal justice arena, which is much more heterogeneous, the ProPublica-Compas case strengthened the realization that it is necessary to evaluate predictive tools more thoroughly before using them, and to understand how judges use these tools in context, causing lawmakers and NGOs defending prisoners’ rights to modify their viewpoint on the matter. While the data science arena is relatively self-contained, focusing on data and algorithms out of their operational context, the criminal justice arena, which brings together heterogeneous actors, focuses on the tools’ actual usage in the criminal justice process.

Les enjeux de l’IA pour la Défense de demain | L’intelligence artificielle et ses enjeux pour la Défense | Revue Défense Nationale (2019/5 N° 820)

2019-05-01T00:00:00+02:00

Artificial intelligence (AI) is a major challenge for defence in the future, about which something positive needs to be done. There are three elements that require effort: infrastructure and the creation of a national cloud, data handling, and human resources. France has great advantages, yet they require a boost.

Éditorial. Quelle éthique pour l’intelligence artificielle ? | Éthique et intelligence artificielle | Revue d'éthique et de théologie morale (2020/3 N° 307)

2020-09-30T00:00:00+02:00

L'Intelligence artificielle, technologie de la vision numérique du monde | Les défis de la justice numérique | Les Cahiers de la Justice (2019/2 N° 2)

2019-07-09T00:00:00+02:00

SummaryThe notion of artificial intelligence is described from four perspectives: its aims, its relationship to computation, its investigation methods, and the knowledge it produces. These factors are described and then a general hypothesis elaborated on the relations between artificial intelligence and information technology: the aim of artificial intelligence is to confirm the alphanumeric vision of the world deployed by information technology.

L’intelligence artificielle dans l’espace public : du domaine scientifique au problème public | « Éthique de l’IA » : enquêtes de terrain | Réseaux (2023/4 N° 240)

2023-09-21T00:00:00+02:00

The article analyses the publicization of artificial intelligence (AI) and its framing as a public problem under the label ‘AI ethics’, from the point of view of the discourses and actors circulating between the media, scientific, and political arenas. The authors investigate this process by considering a subject that has previously received little attention: the relationship between these three arenas, that is, the mechanisms of circulation and distortion of discourses, the conflicts over definitions, and the resulting reconfigurations for the field and for the modes of public existence of AI. Articulating quantitative and qualitative methods, the study is based on three types of data from the French media, political, and scientific arenas: a corpus of press articles pertaining to AI, covering a period of almost 20 years (2000-2019); interviews with participants in the public debate (scientists, journalists, institutions); and public reports (2006-2019). Driven by increased computing power and the availability of large training databases, the ‘AI wave’ in the public sphere is the product of converging technological, economic, media and political factors, without reflecting any real ‘scientific revolution’ promoted by the media and corporations but contested in researchers’ discourse. The article shows how the public life of AI takes shape at the intersection of competing and often incommensurable discursive registers. Moreover, while discourses on AI and its ethics are ubiquitous in the public sphere where they are emerging as a ‘public problem’ that has become a regulatory issue, outside of specialized spheres they do not really draw on a ‘concerned public’ (Dewey, 2012) engaged in collective inquiry and mobilization. Therefore, the latest ‘AI wave’ and the upsurge of ethical issues seem rather to mark an advanced economization of the field, increasingly subject to private interests, in relation to which researchers and politicians are seeking to position themselves.

Contrôler les IA | Contrôler l'intelligence artificielle ? | Réseaux (2022/2 N° 232-233)

2022-05-23T00:00:00+02:00

Pourquoi ethnographier les interactions avec les agents conversationnels ? | Ethnographies des agents conversationnels | Réseaux (2020/2 N° 220-221)

2020-05-22T00:00:00+02:00

« Tisser des liens » | Ethnographies des agents conversationnels | Réseaux (2020/2 N° 220-221)

2020-05-22T00:00:00+02:00

The article argues for a genre of AI capable of building social bonds with humans. The argument’s starting point is the two competing origin stories of Artificial Intelligence. In one, the goal of AI was to create machines that could simulate every aspect of human intelligence. In the other, it was to build machines that adapt closely to natural human behaviour. While the first story is better known, it is argued that the second would have been more fruitful, as it places the human at the heart of the endeavour. Based on this historical perspective, the article provides several examples of conversational agents that engage in this kind of adaptive social behaviour. Results of experiments with these social agents find that they do in fact improve relations between people and the systems. Additionally, they improve performance on the task that the human and the conversational agent are conducting together.

La revanche des neurones | Machines prédictives | Réseaux (2018/5 n° 211)

2018-11-02T00:00:00+01:00

Neurons spike backSince 2010, machine-learning-based predictive techniques, and more specifically deep learning neural networks, have achieved spectacular performances in the fields of image recognition and automatic translation, under the umbrella term of “Artificial Intelligence” (AI). But their relation to this field of research is not straightforward. In the tumultuous history of AI, learning techniques using so-called “connectionist” neural networks have long been mocked and ostracized by the “symbolic” movement. This article retraces the history of artificial intelligence through the lens of the tension between symbolic and connectionist approaches. From a social history of science and technology perspective, it highlights how researchers, relying on the availability of massive data and the multiplication of computing power, have undertaken to reformulate the symbolic AI project by reviving the spirit of adaptive and inductive machines dating back from the era of cybernetics.

Intelligence artificielle (2017)

2017-04-27T00:00:00+02:00

Pages 3 to 8| Pages de début
Pages 9 to 10| Définition | Jean-Gabriel Ganascia
Pages 11 to 16| Introduction | Jean-Gabriel Ganascia
Pages 17 to 23| « Alan Turing est l’inventeur de l’intelligence artificielle. » | Jean-Gabriel Ganascia
Pages 25 to 29| « Désormais des machines passent le test de Turing. » | Jean-Gabriel Ganascia
Pages 31 to 35| « L’intelligence artificielle n’est pas une science. » | Jean-Gabriel Ganascia
Pages 37 to 42| « L’intelligence artificielle est une idée neuve. » | Jean-Gabriel Ganascia
Pages 43 to 47| « Les Japonais sont les champions de l’intelligence artificielle. » | Jean-Gabriel Ganascia
Pages 49 to 53| « La recherche en intelligence artificielle est menée par les GAFA. » | Jean-Gabriel Ganascia
Pages 55 to 58| « L’intelligence artificielle pallie les défaillances de notre intelligence. » | Jean-Gabriel Ganascia
Pages 59 to 65| « Nous passerons bientôt de l’intelligence artificielle faible à l’intelligence artificielle forte. » | Jean-Gabriel Ganascia
Pages 67 to 73| « Il n’y a rien à craindre avec les ordinateurs, il suffit de les débrancher. » | Jean-Gabriel Ganascia
Pages 75 to 80| « L’intelligence artificielle reproduit l’activité de notre cerveau. » | Jean-Gabriel Ganascia
Pages 81 to 84| « L’intelligence artificielle n’est pas naturelle. » | Jean-Gabriel Ganascia
Pages 85 to 88| « Les ordinateurs raisonnent de façon binaire. » | Jean-Gabriel Ganascia
Pages 89 to 91| « Les ordinateurs ne se trompent jamais. » | Jean-Gabriel Ganascia
Pages 93 to 99| « Les ordinateurs sont invincibles aux échecs et au go. » | Jean-Gabriel Ganascia
Pages 101 to 106| « Le “Deep Learning” révolutionne l’intelligence artificielle. » | Jean-Gabriel Ganascia
Pages 107 to 115| « Une machine ne peut pas être créative. » | Jean-Gabriel Ganascia
Pages 117 to 122| « Les machines n’ont pas d’émotions ni de conscience. » | Jean-Gabriel Ganascia
Pages 123 to 127| « Les machines n’ont pas d’intuition. » | Jean-Gabriel Ganascia
Pages 129 to 132| « Avec l’intelligence artificielle émotive, nous confierons bientôt les personnes âgées aux robots. » | Jean-Gabriel Ganascia
Pages 133 to 136| « Les voitures autonomes sont programmées pour tuer leurs passagers. » | Jean-Gabriel Ganascia
Pages 137 to 141| « Le “Big Data” menace la démocratie. » | Jean-Gabriel Ganascia
Pages 143 to 149| « Les “robots tueurs” remplaceront bientôt les soldats. » | Jean-Gabriel Ganascia
Pages 151 to 155| « Il faut donner des droits aux robots. » | Jean-Gabriel Ganascia
Pages 157 to 162| « Nous ne sommes pas prêts pour le tsunami technologique qui advient. » | Jean-Gabriel Ganascia
Pages 163 to 167| « L’intelligence artificielle n’a pas tenu ses promesses. » | Jean-Gabriel Ganascia
Pages 169 to 173| « Les robots nous mettront tous au chômage. » | Jean-Gabriel Ganascia
Pages 175 to 180| « Demain, nous serons les esclaves des machines. » | Jean-Gabriel Ganascia
Pages 181 to 186| « Il n’y a pas ou plus de débouchés professionnels en intelligence artificielle. » | Jean-Gabriel Ganascia
Pages 187 to 191| « L’intelligence artificielle constitue un danger existentiel majeur et inéluctable pour l’humanité. » | Jean-Gabriel Ganascia
Pages 193 to 197| « Grâce à l’intelligence artificielle, nous téléchargerons nos consciences et deviendrons immortels ! » | Jean-Gabriel Ganascia
Pages 199 to 202| « La machine est l’avenir de l’homme. » | Jean-Gabriel Ganascia
Pages 203 to 205| Conclusion | Jean-Gabriel Ganascia
Pages 207 to 211| Glossaire
Pages 213 to 215| Pour aller plus loin
Pages 216 to 216| Pages de fin

L’intelligence artificielle n’existe-t-elle vraiment pas ? Quelques éléments de clarification autour d’une science controversée | Varia | Diogène (2020/1 n° 269-270)

2021-08-16T00:00:00+02:00

For the past ten years and with the big data movement, artificial intelligence has achieved a high profile. Political and economic actors have found an interest in actively promoting artificial intelligence, while researchers from all scientific backgrounds are increasingly orienting their work towards this object of study which, quite often and wrongly, appears new to them. This collective commitment is necessary because of the immensity of the human, social and economic stakes associated with the development of artificial intelligence. The problem is that it is accompanied by media hype that tends to confuse the debates and the work. A striking example is the fact that today there are several well-known specialists who express the idea that artificial intelligence does not exist ! So that, even in the scientific community, it is sometimes difficult to identify what artificial intelligence is. This article aims to lift the veil on this mystery.

Intelligence artificielle | Culture numérique (2019)

2019-03-07T00:00:00+01:00

Ethnographies des agents conversationnels | Réseaux (2020/2 N° 220-221)

2020-05-22T00:00:00+02:00

Assistants vocaux, chatbots textuels, robots humanoïdes : la diffusion croissante d’agents conversationnels dans la société fascine, effraye, soulève des questionnements d’ordre philosophique, éthique, juridique, technique, politique et moral. Présents à travers une diversité d’artefacts, ces agencements technologiques sont capables de produire des paroles ou bien des écrits pendant des interactions avec des humains et de simuler des compétences humaines, des rôles sociaux ou encore des formes de relations sociales. Depuis le milieu des années 2000, nous assistons ainsi pour la première fois à des rencontres inédites « grandeur nature » entre des formes de l’intelligence artificielle conversationnelle et des utilisateurs ordinaires dans leur vie quotidienne, en dehors des murs des laboratoires. Les agents conversationnels ont d’abord rejoint les rangs des produits offerts par les plus importants acteurs du numérique : Google, Microsoft, Apple ou Amazon proposent des enceintes connectées pour l’environnement domestique capables d’interagir vocalement avec les utilisateurs. Mais ces agents ne sont que le fer-de-lance d’un marché plus vaste, qui comprend également les robots conversationnels textuels (chatbots) prenant en charge différentes interactions de service et les robots humanoïdes. Utilisés massivement dans l’assistance technique et commerciale, les chatbots peuvent par exemple accuser réception d’une requête d’un client, repérer le problème posé et rechercher la solution dans des bases de données. Si les services rendus par ces robots conversationnels les rapprochent d’autres applications web, ils s’en distinguent néanmoins par l’utilisation du langage naturel. Enfin, venant s'incarner dans des organismes artificiels matérialisés – les robots humanoïdes – les agents conversationnels se sont enrichis de modalités diverses, par exemple les pointeurs ou les possibilités de mouvement permettant de désigner un objet dans l'environnement ou encore la capacité à détecter la présence de personnes, de reconnaître des émotions et d’exprimer des simulacres émotionnels. Aujourd’hui, les premiers robots d'accueil ou d'assistance fabriqués par cette nouvelle informatique « affective » commencent à passer les limites des laboratoires pour entrer dans les musées ou les situations commerciales. Comment penser les conséquences sociales d’une telle diversité à la fois de formes technologiques et des situations sociales dans lesquelles l’IA conversationnelle vient s’inscrire ? C’est ce défi que le présent dossier de Réseaux se propose de commencer à relever en rassemblant un premier ensemble en français de travaux empiriques sur les interactions avec les agents intelligents. Comment interagit-on avec des agents artificiels ? Quelles relations les personnes développent-elles avec ces machines parlantes ? Quel sens et quelle place ces machines prennent-elles dans notre vie quotidienne ? Comment reconfigurent-elles nos activités à la maison, au travail, dans l’espace public ? Comment nous affectent-elles et quelles formes d’attachement peuvent-elles susciter ?

Les enquêtes réunies dans ce dossier de Réseaux montrent que la recherche en sciences sociales gagne à se saisir empiriquement – avec le regard ajusté aux pratiques propres aux démarches ethnographiques – de la diversité des contextes, des interactions, et des projets auxquels les trois principaux types d’agents conversationnels sont aujourd’hui associés. Pour penser la complexité des infrastructures technologiques associées à ces applications de l’IA et leurs conséquences sur notre vie en société ce numéro propose de s’appuyer sur les résultats d’enquêtes portant à la fois sur la conception, l’appropriation ou les interactions avec ces différents agents. Résolument empirique et descriptif, le parti pris de ce dossier est également pluraliste en accueillant une diversité de manières de faire des ethnographies de l’IA. Réunissant des travaux ancrés dans l’ethnométhodologie, l’analyse conversationnelle, la vidéo-ethnographie, la linguistique interactionnelle, la sociologie économique ou l’anthropologie sociale il témoigne de la fécondité des approches observationnelles pour prendre la mesure des transformations technologiques et sociales contemporaines à l’échelle des pratiques.

Coordonné par Marc Relieu et Julia Velkovska