Category Archives: Technology

“If a computer is right 99% of the time, I wouldn’t want to be the 1% case”

A few days ago my FB memories reminded my of the time I discussed Artificial Intelligence on Swiss National Radio during a segment called “Artificial intelligence: between fantasy and reality”. The program was in French, and I’ve always wanted to translate it. Now seems as good a time as any, so no more procrastinating.

The title of this blog post is drawn from the interview and alludes to the fact that if you have been misjudged by AI you could have a hard time rectifying the situation – because algorithmic decision makes it difficult to know whom or what to hold accountable. When reading, please keep in mind that this is drawn from a spoken, non-scripted discussion originally taking place in another language. Furthermore, it’s from one year ago, which is why there is no mention of recent AI initiatives such as AI Now or Ethics and Governance of AI. While it was not my best interview, and there is so. much. more. to say about AI, I might still have managed to get a few major points across… What do you think?

The interview (excerpts)

Picture: Roomba

– Moderator: Artificial Intelligence is a reality we talk about more and more often. AI or the ability of a machine to argue like a human or even better. And as often, some are gleeful about it whereas others paint a darker picture of the future, even predicting the end of mankind. Well, let’s calm down and study the question more calmly. To do this we’ve got two journalists, Huma Khamis and Didier Bonvin, welcome. And we’re with you, Anna Jobin. You’re a sociologist and doctoral candidate at the Laboratory of Science and Technology Studies (STSlab) of Lausanne University. Anna Jobin, to start, what is your implication, your link to this “artificial intelligence”?

AJ: As a sociologist I’m interested in the social aspects of technologies, including AI. My own research centers on how humans cohabit with complex algorithmic systems, something we do already. And this is the link: complex algorithmic systems – which are one sort of AI.

– [Mod] So you link the general population and science? Do you try to understand and interpret them for us?

Well, in my opinion science and the general population are not two distinct entities. It’s a symbiosis with many questions about the use, but also the distribution and creation of these technologies.

Switch to Huma Khamis, who does an excellent job recalling the history of well-publicized applications of AI, from Deep Blue to AlphaGo and YuMi, and reminds everyone that most of us carry AI in our pocket in form of a smartphone. She ends by mentioning Ellie, a robot detecting depression largely based on face recognition technologies.

– [Mod] Anna Jobin, is this real progress? What do you make of this? Would you say we could do better, are we late at this point?

Of course, as has been said, there have been mindblowing advances within the last years. For instance in calculations – they have always been done, but there has been progress in doing them with computers, merging them with technologies, new materials that have only been used for decades… Secondly, there has been an automation of these calculations, an automation made possible by these computers. And as a third ingredient I’d point to data, no matter whether they have been generated by sensors and integrated in the system subsequently, or whether they represent “available” digital traces generated by our activities.

– [Mod] At what moment did we go from automated calculations to things like emotion recognition? Has there been a border, at one point, that has been crossed, or have we made real progress after years of stagnation?

It is an ancient human dream to reproduce that which makes us human. However, one mustn’t forget that what we consider being human has changed over years, decades and centenaries. It is not the first time that we think the essence of humanity is located in the brain, but even this time it is rather novel.

Huma Khamis and Didier Bonvin discuss Ray Kurzweil, his theory of “singularity”, and what makes us human: feelings? imperfections?

– [HK] So Anna Jobin, you’re part of the Laboratory of digital cultures and humanities, do you think this AI will be able to generate a culture and feelings of its own? And to evolve as we evolve with our imperfections? Will it be able to create imperfections?

AI is already creating its own culture if we look at Netflix and its algorithms of suggestion and classification. But it’s always in symbiosis with humans, which is why I think the idea of the “cyborg” is much closer to reality than a neat distinction between mankind and machine. A distinction that is rather recent and considers both as two clearly separated species by, notably, elevating machines to a species on its own. This of course paves the way to “robots rise up and fight for their survival” – which in and by itself is a very interesting vision of things…

But if we speak of the future, what I’m actually interested in is why we speak in a certain way about the future. I think our visions, fears, utopias and dreads reveal more about us today than they do about the future.

– [HK] Speaking of dreads and fears, we spend a lot of time trying to save human treasures, for instance in Digital Humanities. Is this an emergency because we will disappear?

Humans have always aimed at documentation, from oral tradition to writing to printing et cetera. Now that these great tools of information storage are available, that we try to make use of them for archiving and for digitizing our heritage does not seem like a surprising step. Of course they imply questions about the ways in which a format imposes its particularities on the content, but that’s not what we’re discussing today. What seems much more important to me regarding dreads and fears are – without going down to road until the end of human kind – the forms of autonomy within systems that learn “by themselves”, without forgetting that they have at one point been programmed to learn, so there has been a human intervention at the very beginning. […] There have been decisions about, for instance, the process by which the system will learn, or the parameters that will be taken into account for the learning. Although we might not have access neither to the exact process of learning, as is the case in deep learning,  nor to the justification of the results, there have been definitions and human values influencing the system at the very beginning. However, the problems begin if we don’t have access to the process of justification. Let’s imagine a robot will let us know that, according to its calculations, it would be unreasonable to undertake a medical intervention. Because, by taking into account your age and what you contribute to society through your work, a certain medical intervention might simply not be worth it? … Who are you going to discuss things with? Are you going to argue with a robot, a machine? Or a doctor? And which of these options are you more comfortable with?

Follows a discussion about the the Turing test and the chatbot, Eugene Goostman that had been announced to have passed it, before experts quickly denied its “victory”.

– [Mod] What do you think about this Anna Jobin? There’s debate…

The Turing test is very interesting and it has sparked a competition in the development of chatbots, which is great. Then again, it is a small test within a very limited area: conversation, and to be precise: linear conversation, which goes question/answer and so forth. It’s a very limited form of human interaction. If we look at artificial intelligence let’s start by asking the question about intelligence and what we actually mean. Logic intelligence, linguistic intelligence – but is there creative intelligence, emotional intelligence, inter- or intra-personal intelligence? Et cetera. And yes, there is great progress in very specialized areas, and scientific intelligence…

– [Mod] Several areas progress at the same time.

… yes, but to combine all of these and proclaim that the sum of these parts makes a human is, I am convinced, the wrong conclusion.

DB mentions the Open letter on AI and how Stephen Hawkins thinks AI could bring the end of mankind.

The point you’ve been making about being worried that there will be a threat 50 or 100 years from now [in form of a robot uprising]… it’s still rather hypothetical and I suggest we leave it to Hollywood and science fiction authors. However, there’s the much more recent issue of weapons such as L.A.W.S., lethal autonomous weapon systems. These have been very well created by humans. At one point it is a political issue: what do we want to do with these possibilities – no matter whether we call it “AI”, or “technological power”, or whatever. It is a questions for humans, why do we want to use it, what do we want to develop. We’re all impressionable by a robot, and well, a bi-pedic, advancing on two legs…

– [Mod] … you’res speaking of Google’s Atlas robot. It walks on its own on snow, and if pushed it gets up again.

Yes, and that really is impressive technologically speaking. However, let’s not forget that Boston Dynamics is also in the military business, and even if Google makes promises about its use…

– [Mod] … it will only be used for the love of humanity.

To balance things, HK underlines areas where AI is used for good, e.g. the medical domain, care, etc.

– [Mod] Your last words, Anna Jobin?

I’d like to take up what Huma Khamis said. The potential exists, but it is up to humans to make up their minds what they will use it for, it is used for good? But also: are predictions based on the correct model? Meaning: in which area might it be useful to predict the future based on the past, and whether, for instance, statistical evaluations are the right model. If a computer is right 99% of the time, I wouldn’t want to be the 1% case. How are we going to deal with these question with regard to potential harm, with regard to transparency of the process, and with regard to responsibility?

– [Mod] Anna Jobin, sociologist and doctoral candidate at the Laboratory of Science and Technology of Lausanne University, thank you for accepting our invitation.

[French] Algorithmes: entretien et suggestions de lectures


Un magazine grand public paru cette semaine m’a cité dans le cadre d’un dossier sur les algorithmes. Intitulé “Les algorithmes veulent-ils notre peau?”, il ne donne pas de réponse définitive à la question posée mais aborde le sujet sous plusieurs angles en donnant la parole à des spécialistes de différents domaines.

L’article a été rédigé peu avant les élections américaines mais son sujet ne pourrait guère être davantage au coeur de l’actualité: parmi d’autres thématiques brulantes (telles que notamment la responsabilité des médias et de leur approche journalistique, la fonction des sondages, les facteurs sociaux qui favorisent l’extremisme et l’autoritarianisme) le résultat surprenant de cette élection présidentielle a également attiré l’attention sur le rôle potentiellement joué par les plateformes en ligne et leur gestion algorithmique des news, vraies ou fausses.

Pour son dossier dans Femina, le journaliste Nicolas Poinsot m’avait posé sept questions dont seule une petite partie des réponses s’est retrouvée dans la version finale faute de place. Il m’a gentiment donné la permission de reproduire l’entretien dans son intégralité, que vous pouvez lire ci-dessous. La question de l’influence des plateformes numériques sur la politique actuelle n’y est pas abordée, mais en vue de l’actualité il me semble bon d’ajouter quelques propositions de lecture à la fin de ce billet.


– Quels domaines de notre vie sont concernés par les algorithmes?
AJ: Dès que nous utilisons internet, un outil numérique ou simplement un appareil automatisé, nous interagissons directement avec des systèmes algorithmiques. S’y ajoute l’influence indirecte des algorithmes, par exemple le fait que nous habitions un monde de plus en plus optimisé pour une gestion algorithmique, que nous en fassions usage ou non.

– Observe-t-on une augmentation de l’usage de ces algorithmes depuis ces dernières années. Et si oui pourquoi?
AJ: Oui, clairement, et c’est lié à la numérisation. Il convient d’en distinguer deux caractéristiques principales: d’un côté, les algorithmes numériques permettent d’automatiser un grand nombre de tâches et processus à coût relativement faible. De l’autre côté, il y a l’optimisation: grâce au traitement automatique des données numériques ces dernières peuvent être récoltées, stockées et exploitées de manière exhaustive et très ciblée.

– Quelles sont les évolutions et les excès possibles avec le “deep learning”? Continue reading

Technology, innovation and society: five myths debunked

Recently, I held a lecture about the digital transformation for the franco-swiss CAS/EMBA program in e-tourism. The tourism industry not being my specialty, and the “social media” aspects having been thoroughly covered by colleagues,Media Technology old and new I had been specifically asked to convey a big picture view.

I chose to address some overall issues related to ICT (information & communication technology), innovation and society by debunking the following five myths:

  1. Ignoring the digital transformation is possible
  2. Technological progress is linear
  3. Connectivity is a given
  4. Virtual vs. “real” life
  5. Big Data – the answer to all our questions

Each of these points would deserve an treatise on its own, and I will not be able to go into much details in the scope of this article. I nevertheless wanted to share some of the links and references mentioned during my lecture and related to these issues. If you prefer reading the whole thing in French, please go to Enjeux technologiques et sociaux: cinq idées reçues à propos du numérique, which is the corresponding (but not literally translated) article in French.

Myth no. 1: Ignoring the digital transformation is possible

While discussions of online social networks have become mainstream, the digital transformation goes way beyond social media. It is about more than visible communication. It is about automation, computation, and algorithms. And as I have written before: algorithms are more than a technological issue because they involve not only automated data analysis, but also decision-making. In 1961 already, C.P. Snow said:

«Those who don’t understand algorithms, can’t understand how the decisions are made.»

In order to illustrate the vastness of computation and algorithmic automation I mentioned Frédéric Kaplan’s information mushroom (“champignon informationnel”), my explorations of Google Autocomplete, as well as the susceptibility of a job to be made redundant in the near future by machine learning and mobile robotics (cf. this scientific working paper, or the interactive visualisation derived from it).

Myth no. 2: Technological progress is linear

This point included a little history including sociology of knowledge and innovation studies.

Continue reading

[French] Enjeux technologiques et sociaux: 5 idées reçues à propos du numérique

Exceptionally, this article is in French. English speaking readers might want to head over to Technology, innovation and society: five myths debunked.

Cet article esquisse mon intervention dans un module de formation EMBA / CAS il y a quelques jours. Le but était de sensibiliser les participants aux enjeux des technologies de l’information comme sources d’innovations majeures et de les rendre attentifs à quelques enjeux sociaux des TIC. Afin qu’un tour d’horizon aussi vaste soit un tant soit peu digeste, j’ai décidé de le présenter en cinq chapitres qui démontent certaines idées reçues à propos du numérique:

  1. Il est possible d’ignorer le numérique
  2. Le progrès technologique est linéaire
  3. La connectivité est un acquis
  4. Il y a le virtuel et il y a la “vraie vie”
  5. Les “big data”: la solution à tout

En voici ci-dessous la présentation, et ensuite quelques phrases explicatives avec liens/références.

La présentation:

Idée reçue no. 1: Il est possible d’ignorer le numérique

Le domaine du numérique est souvent considéré uniquement dans une perspective communication/marketing, une perspective parfois réduite aux seuls sujets des sites web et des réseaux sociaux en ligne. Et alors qu’il est possible pour une entreprise notamment de se passer d’une page facebook en toute cohérence avec sa stratégie, il n’en est pas de même avec la dynamique et l’évolution numérique au sens large. Ce parce que la révolution numérique ne concerne de loin pas que les “social media”. Elle comprend toute sorte d’automatisation algorithmique. Une citation parlante à ce sujet a été dit par C.P. Snow en 1961 déjà et je l’avais reprise dans un billet précédent (en anglais) il y a deux ans et demi:

«Those who don’t understand algorithms, can’t understand how the decisions are made.»

Illustrant quelques enjeux d’automatisation algorithmique, j’ai mentionné le “champignon informationnel” de Frédéric Kaplan, mes explorations de Google Autocomplete, et les calculs de la “probabilité de remplaçabilité” d’un emploi (provenant d’un working paper scientifique, transformés en visualisation interactive) grâce aux avancées dans les domaines du machine learning et de la robotique mobile.

Idée reçue no. 2: Le progrès technologique est linéaire

Pour ce point, une petite plongée dans la sociologie de la connaissance et de la technologie:

Continue reading

Google’s autocompletion: algorithms, stereotypes and accountability

Google autocompletion algorithms questions xkcd

“questions” by xkcd

Women need to be put in their place. Women cannot be trusted. Women shouldn’t have rights. Women should be in the kitchen. …

You might have come across the latest UN Women awareness campaign. Originally in print, it has been spreading online for almost two days. It shows four women, each “silenced” with a screenshot from a particular Google search and its respective suggested autocompletions.

Researching interaction with Google’s algorithms for my phd, I cannot help but add my two cents and further reading suggestions in the links …

Google's sexist autocompletion UN Women

Women should have the right to make their own decisions

Guess what was the most common reaction of people?

They headed over to Google in order to check the “veracity” of the screenshots, and test the suggested autocompletions for a search for “Women should …” and other expressions. I have seen this done all around me, on sociology blogs as well as by people I know.

In terms of an awareness campaign, this is a great success.

And more awareness is a good thing. As the video autofill: a gender study concludes “The first step to solving a problem is recognizing there is one.” However, people’s reactions have reminded me, once again, how little the autocompletion function has been problematized, in general, before the UN Women campaign. Which, then, makes me realize how much of the knowledge related to web search engine research I have acquired these last months I already take for granted… but I disgress.

This awareness campaign has been very successful in making people more aware of the sexism in our world Google’s autocomplete function.

Google's sexist autocompletion UN Women

Women need to be seen as equal

Google’s autocompletion algorithms

At DH2013, the annual Digital Humanities conference, I presented a paper I co-authored with Frederic Kaplan about an ongoing research of the DHLab about Google autocompletion algorithms. In this paper, we explained why autocompletions are “linguistic prosthesis”: they mediate between our thoughts and how we express these thought in (written) language. So do related searches, or the suggestion “Did you mean … ?” But of all the mediations by algorithms, the mediation by autocompletion algorithms acts in a particularly powerful way because it doesn’t correct us afterwards. It intervenes before we have completed formulating our thoughts in writing. Before we hit ENTER. Continue reading

Social media – a general sociological approach (1/3 – structure)

Of course there are many ways sociology can contribute to a better understanding of what is happening online: the field is vast, and so is the number of experts and studies. This blogpost has become a series and is – more or less – an English translation of a presentation I have recently given in French, picking up a few of the theoretical frameworks which illustrate the impact of social media on the way we do business… and on our lives in general. Continue reading

Those who don’t understand algorithms…

Don’t be scared if you don’t know what an algorithm is. This article is for you, so please read on.

If you know what an algorithm is but mainly from a mathematical viewpoint, you may skip the following paragraph, but please read on below, too.

About algorithms… and human action

Picture ‘Lamp Flowchart’ by Wapcaplet, via Wikimedia Commons

In a nutshell, an algorithms is the standardized function by which an action is executed – the important word being “standardized“. Because: the action to be executed is defined very clearly, and the function must state unambiguously in what circumstances and under what conditions this action has to be executed (or not).

This may sound very theoretical, but we all have already been confronted with a multitude of algorithmic processes.

Retrieving money from a cash machine is a typical, rather simple example: the machine has a certain number of predefined “actions” it can do (ask for your PIN code, hand out a certain amount of money, show account balance, swallow your card etc.) and its actions depend on your input, which are “conditions” for the machine.

Of course every action that is computer-based is algorithmic, i.e. implemented within different “layers” of programming, all boiled down to the basic electronic signals 0 and 1.

But no need for computers: actually, every procedure guided by a flowchart is algorithmic, too. Everything that is standardized. Everything that is automated.

“Algorithm” means no room for interpretation. And no choice. Continue reading