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Datification and predictions based on correlations suggest that data– driven procedures in social sciences are interested mainly in tracing the appear- ances. Big Data science seems to rely on predicting the appearance without taking into account the explanation and the existence of the phenomena. Following Husserl, Arendt and Stiegler it is legitimate to build abstraction cri- teria to extract data from the phenomena in order to make sense of them. How- ever, it is necessary to be in control of what we are doing, and we need to check models against the effectiveness of the results in understanding phenomena. What happens when the process of abstraction happens in ways and with tools that nobody can check from outside? What happens when the results of the abstraction/technicization process have normative effects on its research objects, imposing expectations and predictions of people’s behaviour according to pre- dictive analytics? Is data science moving beyond Modern Science as we know it? If phenomena under observation rely on uncertain circumstances, there is no way to make predictions that are completely trustworthy. But if the predictions have normative effects, we have to pay attention to the uncontrolled conse- quences of such a vicious circle.

The aim of this paper is to discuss the epistemological risk that we face by building representations of social phenomena that exclude contingency and unpredictability from the frame. Datification is the new representation tool within social sciences. The objectives of data science are the classification of what have happened and the prevision of what will happen. The anticipation of the future is not confined to irrelevant marketing areas, it includes very sensible situations in which social and human rights are involved. Humans can assume the responsibility of their perspective, humans can change their mind, they can assess the variability and multiplicity of preferences and find negotiated political solutions to common problems, though their judgements are fallible. Humans possess the ‘sense of possibility’ introduced by Musil that, according to Bernard Stiegler, we can interpret as a “thought within entropy”. This algorithmic attitude towards future risks to reduce the adaptability of human beings. If we accept to base every judgement on a calculation, even when circumstances do not allow a complete and exhaustive measurement of all element of the situation, we risk misunderstanding the context, being incapable of reacting appropriately when facing external, unpredictable conditions. We might not face the radicality of contingency.

This chapter explores how algorithms produce aesthetic forms and dystopian configurations across Palestinian cyber and digital spaces. Through surveillance and erasure, algorithms operate as infrastructures of (in)visibility across social media and digital maps. While serving the Israeli system of control by making Palestinian users and contents hyper-visible to surveillance, algorithms ultimately purport to delete Palestine from cyber and digital spaces. Acting at the threshold of the (in)visible, algorithms do not only enact surveillance, but they also inform the creation of an aesthetics of disappearance. In this light, this chapter problematizes the normative assumption equating invisibility—in the form of masking or disconnection—to freedom and emancipation by introducing the concept of aesthetics by algorithms as new canon and form of ordering of the colonial space.

In this paper I will discuss Big Data as a suite of new methods for social and political research. I will start by tracing a genealogy of the idea that machine can perform better than human beings in managing extremely huge quantity of data, and that the quantity of information could change the quality of the interrogation posed to those data. In the second part of the paper I will analyse Big Data as a social and rhetorical construction of the politics of research, claiming in favour of a more detailed account of the consequences for its progressive institutionalization. Without a serious methodological assessment of the changes that these new methods produce in the scientific epistemology of social and political sciences, we risk to underestimate the distortive or uncontrollable effects of the massive use of computer techniques. The challenge is how to avoid situations in which it is very difficult to reproduce the designed experiment, and it is arduous to explain the theories that can justify the output of researches. As an exemplification of the problem I will discuss the work on emotional contagion led by Facebook and published on PNAS in 2014. Until now it was difficult to explore all the Big Data projects’ consequences on the perception of human intelligence and on the future of social research methods. The vision that there is no way to manage social data than to follow the results of a machine learning algorithm that works on inaccessible, epistemologically opaque and uncontrollable systems is rather problematic and deserve some extra consideration.

This talk aims at unmasking the idea of data as conceived in most of the literature on Big Data, as well as in some of distant reading practices. This idea is based on the view that data, particularly if it is digitally represented, keeps its purity and is not contaminated by any kind of subjective interpretation, while its unique interpretation is that provided by the algorithmic interrogation of the data. I will discuss the issue in relation to the original genealogy of digital data, preserved in computer memory. According to Joseph Licklider – originator of the concept of the ‘fund of knowledge’ as a feasible digital project – the flow of data contained in an automatically accessible repository does not exhibit any special characteristic and does not substantially differ from the object of its representation. I will argue, instead, that data exists only according to a chosen interpretation and that it is accessible only through an implicit acceptance of its built-in meaning - that is, a meaning that depends on the fetishization of quantification or of another general model in which we want to capture it. The final part of the paper suggests some new issues that need to be addressed in the new agenda of the critical digital humanities.

The slides included in this document were created to support the presentations prepared for the session held on Wednesday, 10/Jul/2019 4:00-5:30pm as the Panel 11: Society, Media, Politics, Engagement, “Twining Digital Humanities and Humanidades Digitales: A Set of Actual Experiences from the South”, chaired by Amelia Sanz at the Pandora Zaal room and at the Digital Humanities 2019 conference, DH2019, in Utrecht, the Netherlands. The slides are shared here as a single PDF for open documentation and dissemination purposes. Please note that the slides were not designed as stand-alone outputs. On their own, necessary context will be missing. Please explore the references and/or contact the authors for further context.

The presentation at DH 2019 was in the panel of the DH of the south and it is relative to the risks of biased algorithms that deal with NAtural languange processing techniques. the talk discusses the possible risks of a blind use of such tools without being conscious of the prejudgements that are embedded inside the code of the algorithms and the data on the training set corpora.

The aim of this paper is the discussion of a genealogy of the concept of digital memory in order to understand connexions and differences with the human memory, with which it shares the name. The digital definition of memory implies some reactive transformations also on the idea of human memory itself. The definition of digital memory within the history of ideas of computing between Turing and Licklider allows us to individuate the genealogic structure that produced the definition of digital memory as a representation of human memory. However they can’t be more different. We know that digital memory reliably stores any information (except in case of malfunctioning), but can retrieve it only if there is a precise enquiry that asks to sort out the expected stored data. Human memory instead is selective in itself, imprecise and unstable as many psychological studies can demonstrate, but at the same time it supports almost all the creative human activities, exactly for the serendipity of its continuously new associations between different elements. The use of the same word for the two elements has deep consequences also on the representation of the functioning of human memory, because some of the digital characteristics have a retroactive impact on the perception of memory in human beings. The final observations of the paper discusses the big expectations about Data science with reference to the possibility of substituting scientific modelling with data correlations. The trust in data power is one of the dangerous outputs of the mistaken identification between human and digital memory. This paper proposes a critical genealogy that can explain the reason of this mystification.

In this paper I will discuss Turing’s ideas about mechanical intelligence and suitable methods for simulating intelligent behavior in machines with special regard to his position after World War II. My main thesis is that Turing considered communication and interaction between human beings and machines a crucial asset for ensuring intelligent performances on the part of machines. Accordingly, Turing was also interested in Cybernetics research on information technologies and interactive communication. He attributed a remarkable value to language as a communication tool rather than simply a representational means, in contrast with the established thinking in classical logic researches, which were strictly intertwined with the debate on the foundations of mathematics. Alan Turing and John von Neumann were both fascinated by the Cybernetics relational approach to the study of adaptive and intelligent behavior, and were not inclined to focus exclusively on a computational approach in the search for machine intelligence. In this paper I will mainly focus on Alan Turing’s position. However, I will also examine some of the approaches proposed by von Neumann, emphasizing points of agreement between the two scientists on many research ideas and projects.

L’obiettivo di questo articolo è tracciare le linee di una genealogia del concetto di memoria digitale per comprenderne connessioni e differenze con la memoria umana, con la quale condivide il termine. La definizione della memoria digitale comporta delle trasformazioni anche sull’idea stessa di memoria umana. La ricostruzione della storia delle idee nell’informatica da Turing a Licklider ci permetterà di individuare la struttura genealogica che spinse a una identificazione memoria digitale e umana, che non potrebbero essere più diverse. Sappiamo che la memoria digitale trattiene con precisione tutte le informazioni che vi sono immagazzinate (a meno di malfunzionamento), ma le può ritrovare solo a patto di ricercare precisamente le informazioni prima immagazzinate. La memoria umana invece è per sua natura selettiva, imprecisa e instabile come mostrano molti studi psicologici, ma sostiene gran parte delle attività creative umane, proprio per la serendipità con la quale è capace di associare in modo nuovo i diversi elementi. Nonostante le difformità tra i due concetti si usa la stessa parola, cosa che si ripercuote sull’idea stessa della memoria umana e sul suo presunto funzionamento. La parte finale dell’articolo si occupa delle grandi aspettative sulla Data science, non solo relativamente alla capacità di conservare delle strutture di cloud computing, ma anche di analizzarli per trovarne correlazioni che potrebbero sostituire il funzionamento dei modelli scientifici. La fiducia nel potere dei dati è uno dei risultati pericolosi dell’identificazione della memoria umana e digitale che si spiega a partire dalla ricostruzione genealogica di una presunta digitalizzazione della memoria., Soglie del linguaggio. Corpo, mondi, Società

Nel 2012 in tutto il mondo è stata celebrata la figura di Alan Turing, il genio britannico che ha cambiato radicalmente la storia della scienza e della tecnologia con i suoi sorprendenti risultati. I suoi lavori furono rivoluzionari nei campi più diversi, dalla matematica alla teoria della computabilità, dall'informatica all'intelligenza meccanica, dalla criptanalisi alla biologia matematica. Tra i suoi risultati più notevoli la soluzione negativa del problema della decisione, l’idea di Macchina Universale, la decodifica del codice Enigma durante la II Guerra Mondiale, il progetto di una delle prime macchine elettroniche a programma memorizzato chiamata ACE, una evidente influenza sul progetto di macchina elettronica di von Neumann, la prima ipotesi di strategie per l'intelligenza meccanica in Gran Bretagna, il test per l’intelligenza delle macchine che porta il suo nome, alcuni importanti risultati in biologia matematica. Il presente profilo cerca, sia pure nei limiti di spazio consentiti, di dare conto dei più rilevanti risultati della sua ricerca tentando anche una ricostruzione culturale del contesto nel quale essi maturarono.

Una storia dei principali centri di cibernetica in Italia, con particolare riguardo a napoli genova/pisa e milano. Le origini della nuova scienza si sviluppano nei laboratori italiani in particolare nella realizzazione di macchine cibernetiche e nella creazione di una teoria di funzionamento dei centri nervosi. E' interessante osservare che la ricerca italiana in quel campo negli anni '60 e '70 del secolo scorso era assolutamente ai livelli di quella internazionale. I nostri ricercatori erano chiamati in prestigiosi centri di ricerca esteri e molti convegni organizzati in Italia furono cruciali per lo sviluppo internazionale dei progetti cibernetici.

In questo intervento viene brevemente tratteggiata la figura di Steve Jobs, analizzando criticamente il suo contributo alla storia dell'informatica contemporanea e le radici culturali che hanno nutrito il progetto di Apple.

In this paper we will illustrate some of the technological, ethical and political issues, under discussion at present. These were raised by Wiener from the 1940s on, and include the responsibility of the scientist in war, decisions on the exploitation of technological innovations, copyright issues raised by communication technologies, the social control made possible by digital devices, and the inspirational role of research in some critical areas of social development. We will try to obtain some insights into these issues from Wiener’s viewpoint.

This paper has a twofold aim. I wish to illustrate a new perspective of logic that Alan Turing discussed in public and private communications after the results he had obtained in the 1930s. In particular, I shall underline his controversial relationship with the strictly Hilbertian approach to mathematical logic. I shall also show an often forgotten vision of machine intelligence proposed by Turing himself in a Report on Intelligent Machinery written in 1948, where he was more explicit than usual about his experiments concerning machine intelligence, and described a stimulating idea about unorganized machines and their potential intelligent behaviours

La rappresentazione della conoscenza nel web è basata sull’associazione, i collegamenti e l’interazione tra i concetti, mentre le tecniche di information retrieval, comunemente usate per la ricerca e il reperimento delle informazioni da parte dei motori di ricerca di maggiore successo, implicano un modello diverso di catalogazione del contenuto: il database. L’organizzazione delle informazioni secondo questo criterio ha il vantaggio di facilitare l’attribuzione di senso all’informazione in relazione alle nostre richieste, ma si espone al rischio di interpretare l’informazione in maniera completamente arbitraria. Sarebbe quindi di cruciale importanza l’ideazione di nuovi metodi per il reperimento di informazione che siano più in linea con la struttura topologica del web. In questo articolo discutiamo alcuni dei problemi cognitivi sollevati dall’uso massiccio dei motori di ricerca per acquisire informazione e proponiamo alcune prospettive di sviluppo in questo settore che utilizzano paradigmi differenti per l’organizzazione e l’accesso al contenuto del web.