Artificial Artificial Intelligence: In Conversation with Massimo Airoldi
15 November 2o22
Dr Ash Watson, UNSW
Massimo Airoldi, University of Milan
Watch the recording
Hi everyone. Thank you so much for joining this event today. This event is part of a new series that I’ve created as part of my role with the ARC Centre of Excellence for automated decision-making and society, and I’ve called the series artificial, artificial intelligence. Because I hope that it will explore the very porous boundaries between machine realities and imaginations. As part of this series I’m inviting leading researchers whose work I really admire and I find really exciting, who are working with critical questions about emerging technologies and their social impacts. And I’m stoked that the very first event today is with Dr Massimo Airoldi.
As we begin I would like to show my respects and acknowledge the Bidjigal people who are the traditional custodians of the land on which this event takes place and acknowledge Elders past and present.
I’ll introduce Massimo in a moment but before we get started I just wanted to share at least the first parts of a short story Massimo my route a few years ago called stealth love. You can see the first extract, the first paragraph on the screen here. This was a story that I was so excited to have published in the fiction series at the Sociological Review that I edit, and it’s really made a mark on me actually, I come back to this work all of the time. And I’ve written about it in journal articles, trying to think with some of the very creative ways that it marries scholarly concepts and literary metaphors that I just think is really rich, and really elevates both the kind of literary techniques that I love when other people use, and playing within my own work. And these great scholarly concepts that so many of us will be familiar with. So I’m going to read some of the opening of Massimo’s story before we dive into our in conversation chat today. The story is called Stealth Love and you can read the whole story on this Sociological Review’s website.
Aripiprazole: bingo! Side effects, orthostatic hypertension, and cardiac arrhythmia. Precisely what I need to pull the health tracking pillow kindly provided by our apprehensive HR colleagues and enjoy an unpredictable day trip on an otherwise hectic Monday morning. I closed the bathroom cupboard, put my mum’s old smartphone on the nightstand and kiss her sleepy forehead. I walk softly out of the door, down the steep stairs, and finally outside in the fresh daylight of Barons Court Road, London. Anno 2028. Monday is tomorrow and I can tell it. Nobody is around. I stand in the middle of the street. As an architecture student or a painter, I can envision for a moment the regular imaginary lines of roofs and pavements converging at the vanishing focal point where my Uber is expected to appear within two or three minutes. Curtains protect the windows from the early afternoon sun. Seeing a graphic elements of an experimental theatre play whose performances stay backstage. Parked electric cars follow the perspective like glitter decorations in a sacred mosaic. Carbon-free icons of a carbon-driven smart mobility. The old neighbourhood has changed, but my dusty mountain bike is still there, leaning against the wall of my family’s Row House among the tall yellow grass. Indifferent to artificial intelligence and gentrification, apparently intact, incomprehensibly, colourful so 1990s. To me Teenage means freedom, not just for obvious biological or generational reasons back in the 1990s one could skip school and have an ice cream in an amusement park with the soul magic power of a fake signature on paper. Now things are slightly different. Both amusement parks and schools hide face recognition cameras, bikes have GPS localizers, every step we take we attract by institutions and companies as taxpayers, workers, consumers, and last but not least, as partners. Soon after I met Alan for the first time two months and one day ago, unusual video recommendations made their appearance in Pornhub’s home page ,based on my brand new style of Google searching. The parabolic shoreline of Danning Beach, a half moon of light blue ocean facing a parallel stretch of photoshopped white sand and green palms, flashed in a two by one meter screen at a bus stop in Kensington High Street on a Saturday morning. Yelling micro targeted summer holidays I might be interested in. At first curious, my wife Lara sweetly became suspicious. No more compromising searches.
I’ll leave the story there and I’m going to pin Massimo. Massimo, I think your name says my name because I’ve given you my link but just so everybody knows, I’ve just pinned you at the top there. And I’m going to stop sharing my screen. Then we will start our conversation.
Hi. So nice to see you. Thanks for the invitation and the fascinating reading. I was really…
Oh good, no I’m so glad that you liked me opening with that, and it is so nice. Massimo and I, we have talked so many times over the past few years and it’s actually the first time that we’re meeting face-to-face. So, we have an audience, but also it’s so nice to do so in this format. The format for our discussion today is basically going to be Q and A. I have come up with a list of questions that I am interested in. But the session will be totally open for audience questions throughout, and in a focused way towards the end of our hour. So please do feel free to put questions in the chat as we go. I’ll specifically ask for questions at about quarter to the hour, and you can freely unmute yourself and turn your video on if you’d like to ask questions. Just do know that the session is being recorded so we’ll put it up on the Centre’s YouTube page following the session, so that other people can access it, who couldn’t be here in real time. As I said I’m so thrilled to have Massimo here today for this first artificial intelligence event. Massimo is assistant professor of Sociology at the department of social and political sciences at the University of Milan. His research interests include critical algorithm studies, computational methods, consumption, and cultural taste. And I have a great series of questions today about two of his texts in particular, and the first is a new monograph that has just come out this year that is a really excellent, called machine habitus towards the sociology of algorithms.
We’ll start our conversation talking about the monograph because it’s such a phenomenal achievement. And then we’re going to tie in some discussion of the short story Massimo wrote that I read out the opening to, just now.
So, Massimo, let me open with my first question. What is it that interests you about algorithms? In particular, where did this Interest come from for you.
So, thank you for the nice words and for having me here. It’s a real pleasure and yes, both the texts, both the book and the short story are quite about algorithms. So yes, I understand that, of course. This interest is basically stemming from my early work on digital methods. My attempts to research basically, society, by repurposing platforms, sort of tools for studying the social world. So, I was very fascinated by that and I’m still very fascinated by that. And when I started my PhD with some colleagues, some friends, we thought okay we could actually study how music classifications are changing our music consumption, is changing with platforms by start in the network of the recommendations on YouTube. The network of Music recommendations.
So he said okay, let’s map that. Let’s see how the algorithm works and let’s try to study society using this code kind of tool as a resource tool. But the more I kind of decided to dig into the algorithm of the related videos of YouTube, I started to read papers, I realized that it was quite an illusion to be able to simply map society through a tool that is nothing but neutral, and that it actively shapes basically the culture on the platform, the behaviour of users. So, I started to read a lot about the then emerging – it was like 2015, 2014 – it was then emerging at the first wave of literature critical sociological and social science literature on algorithms, and I became more and more fascinated by that. And so I started also writing about these and I ended up – I was quite lucky to end up actually here, writing a book for polity about that.
So, yes. I mean it was a really –I mean I’ve studied sociology, I’m not an engineer, I’m not a computer scientist. But I became curious the more I really approached the functioning of these systems. And also the sort of the parts of the story that are not very much told by computer scientists or marketers or platforms, about that. So, the fact that basically they don’t just anticipate our desires and predict our needs, but they shape how we behave on platforms in ways that are toxic are subtle, and ultimately aim to extract data and to keep them keep us there.
There’s such a great level of nuance in the book that I really liked, specifically on that, that I have found – I don’t know you, just articulated it so, well something that I really like about working in a centre like this, that’s really interdisciplinary. That has social scientists and designers, and we engage with artists. And there’s legal scholars and computer scientists that kind of yeah, it’s such a really complicated system and actually we do understand what is significant about those systems with slightly different ways. So no, I love – because this is a much more complicated idea in the book but you summarize it so well, that the book really explores the culture in the code and the code in the culture. I love that as a tagline.
Okay, so my next question is why the focus on the feedback, in particular. It’s a super generative focus in the book, especially the ways that you engage with border, to look at the feedback loop. So what is it that’s sociologically interesting about this loop for you?
Okay, yeah. I mean of course yes, sociology doesn’t use the notion of feedback loop that much, despite there are many sociologists influenced by cybernetics that are also much more competent than me with the ideas of cybernetics that emerged in the 50s, 40s, 50s with people like Norbert, the winner. Basically, my fascination for the idea of feedback loop started with some of the early articles about algorithms that were already and these critical articles about algorithms such as those by David Beard or Shane Leopold, or others, that already started to talk about this kind of close commercial loop by which algorithms process, and the information and shape also, the environment where they act. But the fact is that the more I read about that and the more I got into cybernetics with the works of winner, the more I realized that actually the notion of feedback loop can be used to explain a lot of things that we kind of take for granted in sociology. Meaning, the starting point was to describe for me, a bit of what I just mentioned. Meaning the fact that algorithms do not simply learn from us. They don’t just actually learn from our data and process our information, but they also shape how we act in the world. How we behave on platforms. So, for instance the YouTube – the famous related videos algorithm of YouTube which is my first encounter with the close encounter with an algorithm, worked back in the days when I studied it. Because now of course it has changed. It’s more like based on deep learning. But back in 2015 their recommendations were basically based on quite simple criterion, which was two videos are related if many people watch them in the same session. If they are co-viewed by many people. A collaborative filtering logic, as it’s told.
So, basically the source of information was like information about how people viewed videos right, about viewing patterns. But at the same time, I read other articles that say that about 70 percent of video views on YouTube depend on related videos. So the problem here is the related videos algorithm bases its functioning, it’s prediction, on viewing patterns. But it’s also the main source, the main driver of viewing patterns. This is a feedback loop. This is a feedback loop that has externalities that cannot be predicted a priori very much. Despite of course, the world of computer science of algorithms is all about prediction. But the sort of cultural consequence of the presence of myriads feedback loops like this one in our society, all the time – not just in consumption but also in the professional world, in work, in many contexts. We are confronted with this mutual shaping between algorithms and users. And yes I think this notion of feedback loop was very helpful for me to describe that, and also to link it to broader sociological questions that are there since more than a century. Like what is the relation between individuals and social structure? Between agents and society? Because we make society from below, but we are made by society, all the time. We are not just constrained but we carry, we reproduce society with our action. And so what is that? Yes, in the book I call it a sort of second order feedback loop. Society has always been recursive – probably the recursivity that is, an inner feature to some extent of the way in which algorithms are embedded in society, can help sociologists to see that better. I mean to identify these factors and to problematize them, as I try to do in the book, with a little help from Pier Bourdieuof course, who didn’t speak about feedback loops but I think again, it’s very much, his work is very much about that because that’s what the relationship between the individual habitus and society is about. Is really this mutual shaping, this mutual learning, mutual influence that is there, I think.
Yeah, as I said, I love your use of Bourdieu in the book and it’s funny that you said your background of looking at music and the digitalization of music and algorithms, because yeah some of some of the sociological work that I love best and you know you shouted out Dave Beer and there are a few other British sociologists whose work I really love on digital developments more broadly, that also engage with ideas like habitus but come from this interest in changing music cultures. I think it’s such an interesting trajectory that gives such a nice texture to the kind of conceptual framing that you use and I mean, hopefully it’s evident enough, I strongly recommend this as a read, I can’t recommend it enough.
Something else that I also love about the book is the way that you use art as an example. I wasn’t expecting this when I opened the book and read through the introduction and how you return to it in the conclusion. What is it about the example that you open machine habitus with, that’s so evocative for you? Maybe talk us through the example for those who haven’t read the book, and then and then why you like that one so much.
Yes thank you for asking about that. The example in question is called the Jakos. Sort of the translation would be of this acronym, would be neighbourhood, open source artificial intelligence. It was an art project that was developed by – lead by two wonderful human beings, who are Salvatore Laconesi and Oriana Persico. And unfortunately Salvatore Laconesi is not with us anymore. And he was a great thinker, a great engineer, philosopher, artists, and an amazing mind. And he was famous also for other artistic works that challenge for instance the Black Box character of Health documents. Basically this work called the La cure lakura was about that, so I do recommend everybody to look up the work of Salvatore Laconesi. But in the book I was particularly interested, and I thought it was a great example I think, about the case of jakos. Because this system, this neighbourhood artificial intelligence was actually a simple sort of chatbot basically, a simple software based on tensorflow, based on neural networks, that was able to conversate to talk with people and learn from what people say.
And this system, differently from the AI’s, from the machine Learning systems we are used to, wasn’t trained on sort of abstract data sets like Google news or Twitter, or like global big data sets. It was simply left as a sort of tabulary, without a specific training that’s shaped its behaviour. Except for the data that came from the people of a specific neighbourhood of Rome called the Tor Pignattara, a multicultural neighbourhood, a little – quite disadvantaged neighbourhood of the capital of Italy. And actually it was very interesting that the project was really an artistic project, meaning you had the these simple system going around in the streets on a stroller with the tablet, to be able to talk with actual people in the streets. And it was very interesting for me because it was an example that could allow me to say better I think, and more clearly that these systems are not just news from technological systems, are not just artifacts. They are somehow socialized. This system didn’t – by interacting with the people from that neighbourhood, they didn’t just learn how to talk generically, how to conversate, how to speak Italian or some other language. It learned that with the social connotation of that context, of that environment. It learned it with the nuances of that specific part of the word, of that specific social background of the people in that neighbourhood that is very different for the social background of people in another rich neighbourhood of the city for instance. And so this for me was very, I think useful, also for my own – not just as an example but really as a way to think about this book and the idea of much machine habits. Because I think it’s an example of how really these machines, these machine learning systems, they don’t learn just generic things, they learn from data that were the imprint of the social world. And it’s not just the neighbourhood of Tor Pignattara, that is a sort of physical geographical context, but it’s my Discover weekly, it’s my Spotify data, that is as we know from Bordeaux, from the sociologist, they are not just unique. They are not personal. They reflect the specific, a specific position in the social world. A specific position in the social system. And I think that part of reasoning was really miss – despite very rich and an interesting literature on a critical algorithm studies, that’s sociological reflection was a little bit missing. Because it was basically monopolized by the very important discussion about bias in AI. But I think that sociologically speaking, here in the case of Jakos for instance, it wasn’t, they weren’t just the biases that might derive from being trained in that specific social context that interests me as a sociologist. It’s more really, the culture really. The specific culture that enters the code. Which is a bit like, I mean if we would be interested as sociologists just in bias. It’s like if societies would study only prejudices or stereotypes in humans, but we don’t study just that, of course we study much more futile things that explain the culturally often human behaviour. And I think the study of the role of machines in society really can benefit from a more cultural perspective, that is able to go beyond the quite redactive notion of bias and to see more in general how culture shapes the behaviour. Not just of humans, but also these new kind of machines that are socialized to some extent. And that have these habitus, as I try to explain in the book.
That’s a great lead actually, to my next question which is about reading practices. As you said, I’m sure everybody listening is well familiar with the landscape of literature around emerging technologies, is massive an increase like really, really growing at quite a rapid rate. And a lot of different disciplinary perspectives form part of this landscape and it can at times be quite difficult both to find the kind of gaps as you said, in looking at kind of computer studies literature that dominates through discussions and in a strong focus on things like bias. There’s also a lot of kind of different conceptual histories that we can bring to studying emerging Technologies and they definitely illuminate different parts of our relationship with these devices. Do you have any particular approaches to reading?
I mean, I’ve been reading about these since a while. I mean that there are really – I got fascinated about algorithms in 2015 and since then I try to stay quite dated on that topic. But you’re right, the field is enormous, it’s growing. And then it cannot be confined in a specific disciplinary boundaries which means that yes, especially at the beginning of the -before writing the book I needed to read a lot of stuff. I mean my Zotero is like years. And of course a lot of stuff on this stuff is for instance, from computer science, which is a really different language and different field.
So my practice were kind of, okay, let’s look at on the one hand specific journals that you were really about, that were writing a lot about these topics. Especially in the social science. I mean you have books like a new media society, information communication Society but also International Journal of cultural studies. Theory culture and Society – you have many journals that have dedicated a lot of special issues for instance, on this subject and that was a bit my starting point. And of course it was a bit of snowball kind of reading, meaning okay, let’s look at the references. And a bit of Google Scholar searching, because of course also I relied inevitably on algorithms also to do my own work.
So yeah, I tried to navigate this big field. Some books were very helpful too, I mean the great work of Noble on algorithms of Oppression, Virginia Eubanks for instance, also on automating inequality. And I can – and of course also the other great Scholars, O’Neil you have many – especially female Scholars that I would say were very helpful in framing the social relevance of algorithms in a very clever way. And also to be able to explain something very hard to explain, which is machine learning in simple terms. So, all this knowledge was very useful. And fortunately I had myself a little bit of kind of technical competencies about algorithms myself after a while, because I did my, I used a little bit also myself – machine learning classifiers to process big data which is like my methodological passion, let’s say. The computational social science and more in general. And so of course, I try to put all these things together, I don’t know if I was a successful or not, but I tried to really map that. And also my students were very helpful, to be honest. Because I had the privilege to teach about this topic since a long time. And now I’m back to I’m working in Italy at the University of Milan, as you said. Before I stayed, I lived in France for four years. I was working at the early on Business School in Lyon, and I had the possibility to teach critical courses in a business school which is already let’s say, a success. And also critical courses about platforms and algorithms, which made – I mean I guided students in their group works, in their own researches. And this gave me a lot a lot of interesting hints and stories and readings that were possible. So thanks to the students and of course also to the colleagues that I have, the amazing colleagues that I have now here, and I had that also were a very good source for new references and new things to read. So, it’s yeah, I mean it’s complex. It’s complex.
No, I love, especially hearing about that incredibly generative research and teaching relationship. I think it was definitely successful. I think your efforts have definitely been successful as is evident in the book.
Before we talk about your fiction writing, I have one last question about the monograph. But from here on my questions are about writing practices. So, how did the book begin for you. How did Machine Habitus come together?
Alright so, you know – or now that I have been fascinating about algorithms since already many years. But in the meantime, basically my main job between 2014 and 2017 was do my PhD, to survive my PhD. And my PhD was about Bourdieu of course because that explains why I had the possibility to read a lot of works of Bourdieu. It was basically a study called the digital distinction. I tried to map using YouTube data, the practices of distinction by YouTube users in Italy. So, that’s why also the YouTube algorithm, that’s why the computational methods, etc. But the interesting thing is that that gave me the possibility of – in the time – to read Bourdieu quite at length. At the level of depth that now with another full-time Academic job is hard to achieve, I think. And so that’s the Bourdieu part of the story. And at the same time I had this passion for the social implications of algorithms. I wanted to write a book about that since a while ago already, but I would, I didn’t know how to write it. The idea was okay, let’s see social structures and technostructures. So I wanted to write about basically how these systems contribute to reproduce society.
So, this idea of techno-social reproduction that I try now to describe, I try to describe it in machine habitus, in the book. But they didn’t have a sort of a story, right. It went to – a way to put it in an incisive way, I was a bit confused. And then at some point, I remember still one morning I wake up like at 5am, I don’t know why, I just wake up and started wandering my bed. I was certainly thinking to sleep again but I didn’t succeed. And at some point it was like an illumination and then I thought okay, it’s like I have to do something. I mean this culture that drives algorithms and machine Learning systems is a bit like a habitus. Because these systems don’t have consciousness, don’t understand really. They are not aware of what they’re doing or what they are classifying, or what’s the meaning of a word. It’s just a pure practical as in producing term practical reproduction of patterns, that still have a social source, social origin, that is culturally shaped. And reflects a specific part, as I said of the social world, the specific social classes, specific education. A specific ethnic origin and whatever. And so, I thought okay good, I mean this could be a metaphor for talking about that.
So basically, I wrote a book proposal. I sent it and it was to my great surprise when it was successful and I think the fact that in the end the publisher had also translated some books of Bourdieu was helpful in the success of the proposal. So, I think I was able to capture a bit of like an interesting let’s say, to propose an interesting angle by mixing some one like Bourdieu, that never spoke about technology basically, with this very contemporary and a bit hype, let’s say, topic.
So, basically then I had a one year and a half to write a book. And I signed the contract in September 2019. I spent like the first half of my time reading, reading, reading and taking notes. And then at some point I realized that okay I need to start writing. If not I mean I will never meet the deadline.
I was already thinking about excuses for procrastinating but I must say I’m a bit ashamed of the fact that the Covid lockdown in Europe was quite helpful and in general and it allowed me to almost focus full time on these crazy book. Which means that in some moments, like before the deadline I remember spending like 12 hours per day writing the final chapters, and so then I mean yes, I had some corrections to make of course, but the book was there. And it’s been a relief then to see it out. I mean I still don’t believe, I cannot believe that is there and I can touch it with my hands, on paper.
Well, let’s go from your um 5 am waking dreams about Bourdieu to your incredibly evocative technological pillow and day trips of your short story Stealth Love. How did this story come about?
So it was published in 2019, but talk me through the writing process and where those creative ideas really came from for you?
Thank you. Also, thank you for trusting my work and getting it out in that great place which is the sociological review, and the fiction section of the sociological review. And so I mean this story came out – and I always liked to write in general, but I never tried really to write a fiction in English. But at some point I found I thought I was confident enough to try. And again, I was in that process of thinking about algorithms a lot. About platform society a lot, with the students and in my research. And also I was really thinking about the sort of panel character of these platform surveillance. The fact that really we are already in a dystopia to some extent, simply we don’t realize it probably, very much. And also I started thinking about okay, these algorithms of course, they process very personal information about people. Very private information about people. And so they also threatened to expose this information. For instance about a person cheating on his partner or her partner. Or about some very private passions and opinions that are then manifested by the outputs of a personalized advertising on a platform, or a recommendation on PornHub for instance, as I mentioned in the book, in the story. And so I try to put these things together in this hort story about these academic sociologist turned into marketing professor, which wass also my role at the time, and that tries to meet again, this woman that he met briefly by chance in one of the rare moments where these techno social order of London in 2028 – which is now getting closer – one of the rare moments where this order made of let’s say, a total control on what everybody is doing from workers that have health tracking pillows that tell the employees whether they can go to work or not . To drones in the streets with face recognition cameras, and even bikes have GPS. That are tracked by institutions by companies. So it’s a bit like a sort of, of course a dystopia, but that relies on some elements that are somehow already there. And this main character tries to escape this sort of algorithmic prison for one day and tries to do whatever it can in order to again fool the health-tracking pillows by taking the pills of his mother, his bedridden mother, she’s sick. And try to get on track, the old bike that he was using when he was a teenager, that one of the few remaining bikes without GPS tracker. Trying to navigate the streets of London without the GPS, that is something anthropologically impossible. Because of course in this context, in this imaginary society, everybody, nobody is able to do it. To do anything without algorithms, without a sort of automated recommendation or input. And it’s also, as I write in the story, the story is also linked to the idea of algorithmic culture, that strip us for instance, described in his words, ‘this idea that we are not just experiencing the fact that our behaviour is shaped by an automated recommendations for instance, but it’s our own broader sort of – it’s also by political power. The one of algorithms and of prediction, this culture of prediction that surrounds society and capitalism, in particular. Which is really – we are not anthropologically changed by the fact that we are so used to relying on these devices that we can basically do anything without them. And that’s also one of the ideas of the story. And it’s also this illusion of freedom to an extent that is quite a pessimistic story, as you can tell. But it tries to, I mean, I was also reading about these – when I was writing the story I was reading this article about this French Jailbreaker that was able apparently to, some of these partners were able to hijack an helicopter and let it land in the court of the prison in order to make him escape. And that happened in France, really, and for me that was like a sort of metaphor of like how is it possible to escape from here. I mean you need to have a very complicated plan and dangerous plan, in order to escape from a society that tracks you all the time. And it expects you to behave in a specific way based of what you have done in the past.
So, it’s also this path of dependence, really, that that is at the root of our algorithms work. And it’s at the root of the interdependence between algorithms and humans, that in the story of course is exaggerated but is not that, I think that distant from what we are experiencing today when we need to pick the right music for an evening or to find your way into a city with Google Maps or Ways. I mean, we are already there in some ways. Hopefully not in this, in such a dystopian way in the in the next years. But yeah, we already did that, I think.
Yeah, it seems like the story is Utopian but a plays with possibility and chance as you said, in a in a really exciting way actually, that kind of – what I love about it is the ways that it puts into tension these dystopian and utopian ideas that we have about the promise of technology. I love how the helicopter story features in the short story, in the conversation between the characters. And also the really amazingly done dream sequence. And something that I really love about that chance moment that you talk about, I encourage everybody to read the full story to get all of the details, summarizing it doesn’t do it justice. But there’s a beautiful moment in the story. It’s very cleverly written, where the main character is out at lunch and the internet goes down, basically. And it leads to this chance encounter that carries the rest of the story. And the way that you’ve written that scene, there’s such an interesting way that you pace that scene. There’s a really interesting pace to the way that you describe, you know, that kind of – not as an individual frustration but as a kind of, I don’t know, like a significance in its collective experience. Which I really like, so my next question is, and I’ll take this moment to encourage everybody to think if you have any questions that you’d like to ask Massimo, and use this time now to put them in the chat or raise your hand and I’ll come to you after this. What is it that you enjoy about the form of fiction. Why do you write it? What is it about kind of, the form of the genre of fiction that you like, for doing this kind of thinking?
Thank you all, I really appreciate the fact that you like that part, and this story so much. And okay, yeah. And let me also add something – that idea mentioned in the previous question which is, I didn’t use the word Serendipity. And I think that’s crucial. Meaning that chance, right, that space for a chance is something that is even formally discussed in papers about the design of algorithmic systems. For instance, the problem with music recommendation is that you need to provide the user with some familiar content, but also there must be in some way, some space for serendipity. So, from there, and I think that the problem here is really that what is threatened by the algorithmic consumer in general, is really this serendipity. It’s really this possibility of deviating from a pre-established path and encountering something different. Something which is basically live to some extent, one could say. But I don’t want to sound too much nostalgic, I like technology myself, but of course – on a side note.
Regarding writing practices, okay. So, I’m I always love to read and that’s the reason why I also like to write, when I was especially, when I was a teenager. And when I was younger I tried to write a little bit of fiction myself and then of course I kind of stopped writing fiction because I needed to concentrate all my efforts in learning these specific literary jargon that is academic writing. But I think that still, the fiction is so, I mean, writing for any story is an evocative power that unfortunately the standard academic writing cannot have. And that’s a defect that we have with our team in general, and with all the artistic forms that I think can carry some ideas in a much more radical and a much more effective way, than in a sort of standard academic culturalist prose.
So, of course writing has always been very important to me and also because I like a lot to read, I would like to have more time to read, but of course that’s why I try to myself, to write a little bit. So, that’s it. And also writing, I think that the cool thing about writing fiction is this always sort of psychotherapy for free, meaning because you see, you can use it really as a tool to process your fears, your ideas, and to see them grow, develop, in ways that otherwise would not be possible, I think. And so I’m saying things because I’m not a professional writer, unfortunately. But yes, that’s what it means to me. It’s a very important practice and I’m really sad that unfortunately it’s 99 percent monopolized by writing papers, but that’s okay, that’s life.
Well I’m very, very much hoping that I can convince you to submit some more fiction to me, because I’d love to publish it. It’s really a great story. It does such clever things. As I said at the start of the session – marrying literary metaphors and scholarly concepts in a way that I’m like now continually trying to figure out how to creatively bring into my own practice, which I just love. We have a question in the chat from Michael Keane and Michael, I’ll just read out your question.
The algorithmic society can arguably be traced to a radical cultural movement. In the 60s and 70s many young people dropped out of the exploitative consumer capitalism of the day, then some of them envisioned the freedom of the internet. Do you sense that we are now seeing a new cultural movement. Dropouts from the algorithmic society because of its dystopian problems?
Great question Michael.
Thanks Michael. Yeah, it’s a very good point. This is a big Paradox of the internet, really. And of many technologies, digital technologies. The problem that has been emphasized by some scholars that are more competent than me, for instance, on drop dropouts versus people within platforms or disconnecting, is that since as Bateson says, information is a difference that makes a difference. And even the fact that you’re not the producing data, is still information, to some extent. Meaning that even disconnecting to some extent, produces some information that is valuable to the platform. And even furthermore, I don’t think that it’s such a common – I mean we all fear a lot of things. We live in very apocalyptic times, but still one of the most reassuring habits is like scrolling your social media feed. And even if we might want to think of us as able to stop using them or to conduct a purely rural or different life out of technology, we rarely succeed in that because the problem I think, is also related to the fact that these tools have been designed also by some probably some engineers that in the 70s and 80s were very libertarian and optimistic about the future. These systems have been designed to be very addictive as the infinite scrolling design feature of platforms. They are more like slot machines than the sort of information tools, or social networks. Which means that the problem I think here – I’m going a little bit out of topic but I think it’s important to note here that I don’t want to be a sort of loutish, or I don’t want to say that technology is the problem here – the thing that I would like I try to point out also in the conclusion of the book, is that the problem is not technology. The problem is the culture that is embedded in technology, which in the case of algorithm and the example of the Jakos that they make in the beginning of the book is I think, witnesses that. Because a machine learning system can be a tool to draw new relations in a community. It can be used, as it was in the case of Jakos, as a form of collective memory of the of the neighbourhood, that was able to kind of unite the different stories, the different sayings, the different imaginaries of people sharing a similar context with the same context. With the same problems. So, machine learning can be an amazing technology and we see that also in many applications in health or in the sciences, etc. The problem is the fact that these tools are used really, in ways that are I think not acceptable, that should be criticized more openly and more often. But we are a bit apathetic about that. Like we just stand up, okay maybe I write that on Twitter, or yeah, I just scroll a little bit more and like some other content.
I see another comment here from Michael. Maybe to drop out is a radical term, but people are reducing the digital footprints somewhat. So, yes. Maybe yeah, despite I mean, I think it’s hard we should not reduce – the traces that we produce and are processed by algorithms are not just the traces that we live on social media. So, for instance in the book I speak a lot about digital platforms because unfortunately it’s the context that I know better. And recommendation systems are the type of machine learning algorithms that they know better. But the fact is that we produce data all the time. We produce data every time we move in the streets with the smartphone, without doing anything. We produce data anytime we enter a shop, potentially, the more of these shops are equipped with new technologies, tracking the movement of our eyes or whatever. Or cameras are everywhere. So, it’s really hard to reduce your digital footprints because existing is already living a digital footprint, nowadays. So, we need to eventually think about how to imagine new ways, not just to reduce your digital footprints probably, but to change and challenge the system, what is done based on these digital footprints, to some extent. Which is currently either kind of controlling citizens or repressing revolutions, or selling products in a sort of planet that is dying for climate change. So, I mean, sorry for the not very optimistic start. In my case it is like eight, nine AM, so it’s going to be a great day.
No, I think that’s a perfect note to wrap up on. Very realistic, yeah, a very realistic examination of our relationship to technology with those important slivers of hope through art and storytelling, and kind of meaningful community engagements.
Everybody, please do thank me in joining – please do join me in thanking Massimo for this great session today. You can use a little clap reaction, yes Do feel free to turn your camera on and give us a wave as we log off, it’s always nice to see people’s faces who have joined us. Thank you so much for your time.
You can find Machine Habitus by Polity Press, and you can read Massimo’s short story Stealth Love as I said, on the Sociological Review. We’ll leave it there. Thank you so much everyone and we’ll talk to you soon.