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The History of Data and AI, and Where It's Headed with Cristina Alaimo, Assistant Professor at Luiss Guido Carli University

Adel and Cristina explore the many of the themes covered in her book, from the first instance of where data was used, to how it became central for how organizations operate, to how usage of data introduced paradigm shifts in organizational structure, and much more.
Jun 2024

Photo of Cristina Alaimo
Cristina Alaimo

Cristina Alaimo is Assistant Professor (Research) of Digital Economy and Society at LUISS University, Rome. She co-wrote the book Data Rules, Reinventing the Market Economy with Jannis Kallinikos, Professor of Organization Studies and the CISCO Chair in Digital Transformation and Data Driven Innovation at LUISS University. The book offers a fascinating examination of the history and sociology of data.

Photo of Adel Nehme
Adel Nehme

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

Key Quotes

This idea of how we organize things collectively has a very close link to what kind of data we produce, how do we use them, how do we mobilize them? So we started thousands of years ago with these clay tokens, these little artifacts made of clay that were different shapes, cylinder shaped and more. They stood for something, for instance a sheep or a day of work, and they were exchanged in the Mediterranean as a symbol, a token of a transaction.

What is data? What is its link with society? How have I changed my way of thinking because of data? Data literacy, thinking through data in school, making it a matter of debate, of questioning, of inquiry. I think this is another very important point that we can do to avoid this sort of reductionist view that paralyzes society in this sort of deadlock of bad and good. We don't want that. We want to grow and decide what kind of society we want, and then work towards it. We need the participation of all to do that. That's why data literacy and participation is another key word. Who is going to participate in this debate of data regulation? Only the company and the regulators? Perhaps we need the involvement of other dimension of society, civil society. So these things are connected though, because civil society will not participate if this discussion is not facilitated in the first place and a different idea of data does not penetrate society, which means academia, which means media, journalists, this podcast.

Key Takeaways


Datification, or the encoding of life events into data, involves complex ecosystems with multiple actors and stages, requiring careful consideration of what is recorded and why.


Rather than viewing surveillance solely as a "Big Brother" issue, consider the participatory aspect where users derive benefits from services, necessitating balanced regulation to protect privacy without hindering service access.


Advocate for and implement data literacy initiatives in education and workplaces to enable informed discussions about data's impact on society and enhance collective understanding.

Links From The Show


Adel Nehme: Hello everyone, I'm Adel, Data Evangelist and Educator at DataCamp and if you're new here, DataFramed is a weekly podcast in which we explore how individuals and organizations can succeed with data and AI. One thing we like to do on DataFramed is cover the current state of data and AI and how it will change in the future.

But sometimes, to really understand the present and the future, we need to look into the past. We need to understand just exactly how data became so foundational to modern society and organizations, and how previous paradigm shifts can help inform us about future one, and how data and AI became powerful social forces within our lives.

Enter Cristina Alaimo. Cristina is an Assistant Professor of Research of Digital Economy and Society at Luiss Guido Carli University in Rome. She co wrote the book Data Rules, Reinventing the Market Economy with Jannis Kallinikos, Professor of Organizational Studies and the Cisco Chair in Digital Transformation and Data Driven Innovation at Luiss University.

The book offers a fascinating examination of the history and sociology of data, and throughout the episode, we spoke about many of the themes covered in the book, from the first instance of where data was used, to how it became central for how organizations operate. to how usage of data introduced paradigm shifts in organizational structure and culture, and a lot more.

If you enjoyed the episode and the DataFrame podcast, make sure to rate it wherever you get your podcasts. And no... See more

w, on today's episode.

Cristina Alaimo, it's great to have you on.

Cristina Alaimo: Hey, hello. Thanks for inviting me. It's great to be here.

Adel Nehme: Yeah, I'm very excited to have a chat. So, we're going to be talking about your book today Data Rules, very excited to talk to you about it, but maybe to set the stage for our chat and our conversation walk us through the motivation behind why you and Jannis Kallinikos, your co author, decided to write Data Rules.

Cristina Alaimo: Well, you know, your show is all about that. We live in the age of data. So, we really cannot basically do anything any longer without data. We do stuff through data, by data, and with data. So we do groceries you know, we chat, we record shows we study, we work through data. So our everyday life has changed because of the presence and involvement of data and, of course, database technology.

But these little tokens, these little artifacts that circulate and make things possible differently from how things were made possible before. of course, as our everyday life changes and, there are Sort of reshuffling, I would say, of how things are done. So perhaps some things become similar, like, you know, studying and making and buying grocery becomes increasingly similar as we use data and this digital device to do that.

Also, our institution changes. So our institution change, our uh, organization change. And so this double level, I think the level of everyday life and the macro level, the level of how institution also respond to this big shift. We felt there was something that was worth it, let's say, to, to go a little bit deep, exiting from a sort of Let's say narrow view of data only as input to, large system, large model, but data on their own as artifacts of human making and instrument of communication and, tokens through which knowledge is transmitted through which we see the world, ourself and the world, and we do things.

So that's a little bit, this, this big reshuffling of everyday practices, but also restructure of institution, I would say, are central concerns of the book.

Adel Nehme: And in a lot of ways, the book does a very thorough job at looking at data through, you know, a bit of a sociological lens and a historical lens and really tries to map how data has impacted, as you mentioned, institutions, but you know, how we communicate, how we work together and how we really think about the world today in the 21st century.

I want to maybe first start at the history side of things. You actually do a really thorough job at mapping out the history of data from, how we used to record information on slabs of sand. stone to today's applications and digital technologies and digital exhausts that are produced by database technologies.

But maybe you walk us through the history of data in a bit more depth, and I would love to see what you think are pivotal moments and milestones in the history of data that has led to the paradigm shift that we're in today.

Cristina Alaimo: That's great because, you know, we sort of look back in order to look forward. I mean, our approach to history is a very sort of, instrumental approach. We want to learn from the past in order to learn how to ask questions the right question for what's going on today.

So that's very important because, of course, we are not historians, but we want to learn from history. and we do so by retracing, as you mentioned, pivotal moment I would say in the role of data within society and the links between data. like social cognition. How do we understand the world and institution?

This idea that, how we organize things collectively has a very close link to what kind of data we produced. How do we use them? How do we mobilize them? So we start with this clay tokens. this little sort of artifacts made of clay that had some shapes like they were like cylinder shaped or different shape they were stand for something for instance a ship or a day of work.

And they were exchanged in the Mediterranean as a symbol, a token of a transaction. So, and that was the first moment in which we, that we know, of course, in which as transaction exchange were recorded, they ceased to be only something that was happening in a given moment in time and in a given place. And they became something that could be extracted from that moment, thought as a social event, as a sort of objectified event, upon which something else could be built.

Practices of taxation. Because I could record the exchange between me and you, and You know, I could encode it into a token and this token could be stored. Then, you know, in a year time I could recount how many transactions we made and I could extract like a tax, you know, or something else. And of course, this was used exactly in proto bureaucracies at the time.

They were the temple and effectively taxation was born almost by that. And I would say the most important thing was born by the manipulability of concepts, very complex concepts, such as exchange, that were made objective, so to say, with a life on their own, because they could be encoded into data. The links between data and the making or the emergence of institution are also recounted in another episode where we talk about the rise, the rise of statistics, you know, which really assigned a very important moment in history because to statistic, to the quantification of collective behavior is linked government and how do we govern such behavior?

And therefore the making, if you want to modern institution of modern public institution, the state that's very much linked to statistic, which is the science almost of the state. But this is also linked to data. So, how do we gather data? How do we make those data the representation of some things that you know, we start looking at a product of society that exists only insofar I can count.

these events. So it's a really, you know, sort of a very interesting feedback loop here. I do measure society through the counting of some event and for this reason I almost make society as something representable and therefore And this, I would say, is a second important moment. A third moment, and I go to U.

S. here, is the making of corporation. emergence of modern corporation that the business historian Alfred Chandler recounts as the change when, These modern corporations had to use internal data in order to keep track of their goods, their resources. think about the beginning of last century or the end, actually.

So end of the 19th century. Think about modern railways, you know, think about all this technology coming and this big corporation, the organization expanding, changing places, changing also, diversifying also their operation, having to hire people. How do you suddenly track? All these different goods and workers and way of working.

So you need to start recording internal data. So that's, that's the idea of Chandler. And as you keep on recording what the people that work for you do, how do they do that? Where do you locate goods? physical resources, then you become a corporation. So you become a different kind of organization that can expand in scale and scope and, give rise to modern ways of doing business.

And that's also very, very interesting because once again, from history, we learn that there is a link between, institutions, data, work practices, Chandra, emergence of managers. of clans to the use of data. The administrative science, you know, is the science of data. And yes, I think that these three moments are very important.

And then of course, perhaps the last one, which is the most perhaps relevant for us is the advent of machines. And how they change basically everything within organization. So first, the advent of machine, the holler, it punch cards, and then with the, the advent of computers, as you know, digital data makers.

And this is something that really first within organization and thereafter for us, for society and large as society, has been a, pivotal moment. Data ceased to be, you know, just linked to specific practices, like, you know, archival data to the or, you know, like accountants data to accountants.

With digital technology, they become almost universal token where different things could be recorded, represented. Manipulated and also co joined, you know, like, combined. And new things can be learned from this recombination of data that up until that point were belonging to different areas.

of work, of life. So yeah, I really like fast walk through thousands of years with uh, yeah, I mean, it's so fascinating, you know, I can talk for hours. You need to stop me here and tell me, you know,

Adel Nehme: Yeah. Yeah.

Cristina Alaimo: enough, let's, let's talk about something else.

Adel Nehme: what's interesting about what you're mentioning here is that you know you trace the kind of these four pivotal moments as three pivot moments the history of data. And you're talking about kind of the age of digital technologies at the moment that we're living in where the production of data no longer and correct me if I'm wrong here, no longer serves a purpose where, your, a it just for, you know, a use case such as tracking your resources.

But now, you know, you use your phone that that data of how you're using your phone is being logged, sent to the cloud. And in a lot of ways, I think that the current paradigm that we're living in or the current era that we're living in is also ushering in a new. A new category of use cases or a new category of intelligent machines, especially when we look at kind of Intel up generative AI and how, you know, many of the AI technologies that we use today are based on a lot of the data that we've been producing. So maybe, given this perspective, right? How do you look at historical perspectives on data? How can it inform what we're seeing today with generative AI as, unlocking a new way of interacting with digital technologies because data enabling intelligent machines that can mimic human behavior like human text and, you know, make intelligent Semi intelligent sounding text,

Cristina Alaimo: Yeah, definitely. This is, I would say, perhaps someone in some year will look at this as another pivotal moment in history, so definitely there is something going on here, which if we look at it from a data lens, because that's That's also very important. Current discussion on the role of data and AI or AI, they always look very narrowly or well, most of the time, let's say, at data as input to this machine, Like huge data set or different data. They, and this intelligence machine that learn from these different data and can churn it and chunk it and produce it. Something resounding as intelligent. But if we adopt a slightly different perspective, which is what we suggest in the book, and we look at data not just as inputs to these big devices, but to artifact of human making, human means here, societal doesn't mean that someone goes there and do it.

Of course. So it like, so with their links to culture with their links. to organization. Data are produced by some organization for some scope, for some aim, with their links to way of doing things. And we start to unpack little by little all of these different aspects of data. Then we can ask the right question to the AI.

current wave. So, or at least, let's say a broader range of questions, not just the question of how intelligent these machines are. But for instance, what kind of intelligence do they propose? Or what kind of intelligence do they propose and how this intelligence infiltrates in the existing intelligence of institution, of organization?

What's the the change of paradigm here in knowing things and, and what kind of consequences does it have from the way we do things? Because if we go back and we recall what we just said about the history of data and their links with way of working, the emergence of institutions, then we look at this novel.

turning machine, let's say, and we need to ask, so what kind of institution will they facilitate the emergence of? Or, do the existing institution we have, so the existing kind of organization, are they still fit to deal with this huge knowledge change that these large language model, these, you know, artificial intelligence proposes that advance.

So I think that's My answer to your question is like, let's open up the discussion of data avoiding these narrow routes of data as input and opening up data as artifact and then starts asking, what's the role and what's the big changes here.

There is another more technical aspect too. Of course, here, which is the process of datification, the process of working with data changes with this machine. They propose entirely new data practice. Entirely new way of manipulating data, entirely new way of accessing data and, tracking this data work, accessing this data work.

What does that mean? So again, if we look at data work, not just as sort of a technological process, but more as a sociological also process. These machine are in society, they, they are part of our society. And then there is a constant feedback between society and technology. Then, you know, also this is interesting.

So engaging in a bit more detail with the kind of changes in data work the operation of the data operation, this machine do and ask question around. the practice connected to it, the kind of cognition, social cognition linked to it, the professions that will emerge or will just cease to be relevant because of that novel kind of data work, archive, like, We learned from history at some point data was not any longer, scribbled on paper and all of these archivists that were working for organization, they were no longer working for organization because there was no need, but a new class of clerks emerged, manager.

A new class of manager emerges. So you see, it makes all many of the question we ask ourselves today. will we lose jobs? We will a little bit more complex and perhaps a little bit more grounded in the complexity of our society.

Adel Nehme: you know, I would love to pick your brain on on how you think exactly or how you predict society can change or evolve, you know, based on this current paradigm of AI that we're living in. But you mentioned something called datification here, which is actually a very central concept of the book. maybe first to set the stage, what is datification?

Cristina Alaimo: Well, as simply as I can, as simply as I can, I think is the translation, the encoding of events or episode of life into data. So it's this kind of idea of representing, recording and representing something of life into data. data tokens into data. So that's, I think, the simplest way I can go , in order to, that's of course then that's just the beginning.

But I think, yeah, that's a basic idea of it.

Adel Nehme: And you mentioned there's a bit of an ecosystem when it comes to datafication that you expand upon in the book, from a sociological perspective, how organizations approach and maybe expand on the observations that you've had from the book while studying this ecosystem of datafication.

I'd love to understand here a bit more.

Cristina Alaimo: So, yeah, because of course this recording is not, the simple recording is not as simple as it sort of may sound. And this is very much linked to the fact that data are not just, input or, but they are the product themself of a very complex process that has several stages, a life cycle, and several actors involved.

That's the ecosystem you mentioned. Many different actors, but also many different links to our social reality and economy. So perhaps, An example will work better here in order trace the boundaries or the characteristic of this ecosystem. The programmatic advertising ecosystem. So some, one of the example we use in our book and the programmatic advertising is the automated exchange buying and selling of advertising space online.

This is done through auctions. Auctions that last very, very long. little like 0. 03 millisecond. So, and they happen every time we open a newspaper online. There are several ad space that needs to be assigned. These ad space trigger a data object, a very complex system. This data object embeds some specific data about who is opening the page, the price of the ad, blah, blah, blah.

And thousands of platform bids for this space. The auction happened. Ads are assigned to space and I see my newspaper with my well placed ads. Now, all of this, it's an immense complexity of stuff that happened, all through data and data objects that coordinate the exchange. But the interesting bits here is What is it that exactly is sold there?

No, that's, that's the interesting bit. That's the social sort of science of data or, you know, asking questions. Because, you know, marketing always and advertising always have this problem of measuring the attention of users and, and therefore putting a price. On a space for an ad and at some point there was the conviction, you know, the idea that this could be solved via data and digital technologies.

We have more data about user and user behavior and therefore we can be more precise. But there is a lot of work still going into defining what is it exactly that we can measure and how can we put a price on it. So for instance, we can measure click. There is not yet any way of going beyond the screen and really looking at what I'm doing.

So I need to use proxies. I can measure, for instance, the level of the screen eye level, I can measure the click, I can measure impressions that are defined as the likelihood of users seeing an ad. All of this gives rise and depends upon a huge ecosystem of companies, actors, that measure.

That invent proxies that invent ways of verifying that this measure are reliable that give pages to the reliability that enter in getting more data in order to have more elements to increase the likelihood. that exchange those data. So this is what we mean by ecosystem and the ecosystem of datification, not just actor that exchange data, but after that are almost created out of data needs, the need of verifying what an impression is certifying.

What an impression is. And if you think about how this is linked to existing practices, for instance, an impression has not been invented by digital data, but what ex was already existing and how digital data change it. So I think that this idea of ification and data ecosystem is an idea that allow us to see that making data.

So the ification is not a straightforward process. So it's always a process that involves decision making, links to how things were made before and how can we translate it now that we have digital data, involves different actors regulation and governance, and increasingly so, involves the not straightforward thinking of What is it that we sort of, select in order to be recorded into data and for what purpose and how that works for us?

And then as you multiply this question across all the actors of the ecosystem, you can see that this is an immense complexity because each actor will have its own idea And then there is technologies and layers of technologies. So yeah, that's very interesting because it allows us really to sort of disentangle a little bit this monolithic idea of data and instead articulate it across actors, practices, and I would say also time.

Adel Nehme: Yeah, that's really interesting. And actually, this description of the digital ad ecosystem if I connected back to our earlier discussion on the advent of large language models, I would anticipate that the introduction of large language models into this ecosystem would completely change the nature of of the ecosystem, right?

So let's say instead of measuring clicks and measuring impressions, a chatbot asks you a question and you're able to answer it. And the system is able to react in real time based on, the sentiment that you have in your answer, maybe the voice and tone of your voice if you're actually recording an audio instead of answering in text.

How do you anticipate potentially, the advent of data producing AIs or AIs that are able to understand these nuances on an individual basis to impact or change these systems as you look at them or these ecosystems.

Cristina Alaimo: Perhaps can say a couple of things. The first is that no matter what kind of technology and sophistication this technology has, there is always a decision within, made somewhere, and repeatedly so, on what is it that I want to select in order to define a specific product. specific sentiment or a specific reaction in user, this is not straightforward.

So that's very important. We should not fall into, you know, the trap of thinking that as technology has evolved, therefore it becomes, let's say objective. No, there is always a set of decision that have to be made on. but there is always a decision of selecting these other than that and making it your data on top of which you do then your work, so that's very relevant.

However, and you were absolutely correct in saying so, as of course, there are shifts in technology and the use of data and the links that can be made among different data. Of course, all this ecosystem of production also change and it's unavoidable because, you know, new actors will emerge that will be better placed using these technologies or packaging this technology into apps.

Let's not forget. that this is what we are looking at right now. We are living in the sort of amplification, I don't know, of like, you know, these intelligence technologies and that's, we still, it's slightly different from what we have seen so far, and there are some commonalities, but it's still something that we are seeing now.

at a very larger scale. and there are other ways in which, and we were mentioning before, data will be worked and used. Now, it seems that some of the characteristic of this data work is that it's hidden from us, even in ways that are more conspicuous than before. I mean, Programmatic is already over complexity, which is so high that it's very hard to track all this data flow, the different stage of data work, but it still can sort of being done right there.

You can investigate. I did it protocols or, the data object specification. You can still have an idea what's going on in terms of data work behind. the ecosystem. What about this AI technology? What kind of access we will have into this data work? How can we account for what's going on, this decision making embedded in data work?

Well, these are questions that are kind of new, or at least these models, they pose it in a kind of different scale, I would say. And they are urgent questions because We cannot account for a process if we cannot see anything about that process. And this of course has some some consequences and implications.

Adel Nehme: Speaking of implications and urgent questions as well, you know, one thing that the book touches upon quite a lot is the concept of digital surveillance, Which is, you know, something that's been really spoken about in the past 10 years, especially with social media the Shoshana book, The Age of Surveillance Capitalism is a really great book on this topic.

topic. You know, your book proposes different perspective on digital surveillance, right? So, you know, oftentimes the narrative on surveillance is that there is this big brother in the form of large tech companies observing and monitoring the digital exhaust we leave in the world, Maybe could you expand on how you view surveillance?

digital surveillance and the surveillance issue and how you tackle it in the book.

Cristina Alaimo: There are many problems linked to data, data work and these big platform companies. And certainly the idea that they track, you know, user behavior and the extract value of it is a very complex one. Now, the current idea of surveillance to us is very much based on a means and relation. Like, they do that because they want to surveil us, like kind of thing.

Which is like the big brother, let's say, Alla Orwell, right? but once this is a little bit limiting, and limiting on the side of users, not on the side of the company. And I will explain why. If you put, you know, user in this sort of double bind, in these things that they cannot go out from, because either you are surveyed or, You have no choice, then there is very little that we can make as a society.

But if you instead start looking at surveillance as a sort of byproduct or something more complex, they're involved also the participation of users. I mean, we are not Stupid, right? At least I, you know, right? We want, we go there, we use this technology because we get something out of it. And there are services and there are, you know, intelligence machines that answer, facilitate our job.

They let us do things in different ways. they make us company, whatever. So then it becomes a little bit more complex because surveillance is not any longer the means to an end. It's not, I'm not being surveilled because I want to be surveilled, because they want to surveil me. Because there is a more complex let's say production change that involves also the tracking of user behavior.

and if we look at it, at this with good and bad, we don't want to discount the bad. Let me be clear. We're not saying, Oh, guys, that's all very beautiful. There's nothing that is going on here. No, but we are saying we cannot go backward. We can only move forward. We have these technologies and we want to use them.

So how do we instead account for these practices of tracking and regulate them but still allow user to do what they do and what they wanted to do through these technologies and this company to work because we want them to work. Otherwise we won't have these services. So then our idea is to open up this deadlock, let's say, between Surveyor and Surveys, and instead try to understand a little bit more the positive thing and protect user and user rights, but also user access to service.

But at the same time, regulate and govern other practices that have been, and we know now not so, let's say, respectful of user privacy, fundamental rights, and manipulation of user behavior. This should not be allowed, that's for sure. We are not discounting any of that. But perhaps we say, let's give also to user a more active role.

in this and try to find out how can it be even more active with the participation also of, governmental agencies and regulation and even a user, bottom up civil society that also sort of participate and, and collaborate in solving while solving at least, but making this issue, bearable.

More malleable for our society.

Adel Nehme: maybe on a tactical level, if you can provide some examples, how do you view that working out in practice? we're not going to be able to solve the issue of digital surveillance regulation today, but I'd love to see how does that translate to, to real world examples of how we can, you know, improve the state of digital surveillance regulation.

Cristina Alaimo: this is of course a very big question, so I'll try my best to think through so what the book, for instance, may suggest is opening up a debate on these themes, because that's what we want to do in the end, you know, the idea that data are not just input, and therefore they are not just mean to are not tracking, tracking devices, but much more.

It means that if we translate these ideas and we apply it also to this problem of surveillance, we're sort of opening up this problem of surveillance and we start asking question of what kind of data, for instance, or what kind of practice linked to this data lead to this, or what kind of data stage we can regulate in order to avoid the pitfall of this and that.

So regulation is the first, and governance and regulation is the first idea that I should say it's very much needed. And perhaps we are already late, we are moving, perhaps in a very disorderly way, but at least we are moving to address this issue. But here is the thing, if we regulate data as tracking device, we don't go that far.

the remedy may be worse than the cause, let's say. If we regulate data in this narrow sense, because they aren't. They aren't only tracking device. They are much more. then, the social science of data, the data lens perspective. Let's see exactly what it needs to be regulated and how.

By accounting for all of different, for all these different links of data with society, remember the everyday life of people and institution as well. And let's open up a debate on how can we do that without throwing away also what we have conquered, the positive side, without risking giving even more power to this big platform.

Because that may be also a risk involved in regulating badly. data. But a second more very important point I would say is data literacy. So the literacy, you know, and this is a kind of thing we should start talking a little bit more about. I'm not talking about, giving at school coding classes, which may be useful.

Perhaps not that much now with AI because we know that this sort of will be also taking up and automated. But data literacy, what's data? You know, what's the discussion we are doing right now? What's their link with society? How have I changed my way of thinking because of data? I mean, data literacy, thinking through data in school making it um, a matter of debate, of questioning, of inquiry.

and I think this is another very important thing that we can do to avoid this sort of reductionist view that people paralyzed society in this sort of deadlock of bad and good. We don't want that. We want to grow and decide what kind of society we want, and work toward it. And we need the participation of all to do that.

That's why data literacy. Participation is another keyword. Who is going to participate in this debate of data regulation? Only the company and the regulators? Hmm, perhaps we need the involvement of other dimension of, you know, society, you know, civil society, of So these things are connected though, because society will not participate if this discussion is not facilitated in the first place, and a different idea of data does not penetrate society, you know, which means academia, which means media, journalists, which means show like this, which means many other things.

Adel Nehme: Yeah, I couldn't agree more on the last element, especially on data literacy, because you want a citizenry that is engaged in the conversation. And here, data literacy is an essential lever to be able to get people engaged specifically because another aspect of the book is that you talk about the blurring of life's boundaries because, you know, you mentioned that data has become such a central point of our lives that there has been a blurring of boundaries between the different spheres of our lives.

You know, for example, the economic and social spheres, the work in private spheres of our lives, right? So maybe walk us through your thinking here in a bit more depth, and why is it data that is foundational to this blurring of the spheres?

Cristina Alaimo: Absolutely. And that's a very important point because it's like, perhaps a more thorough reading of what someone would call surveillance, Because it's this that we are talking about, has our, our social interaction has become the resources through which economic value is produced.

But this is a huge thing. It's not that because I'm tracked then, So what happened when this, when this is the case, what happens to our society? If I engage in conversation and this conversation is the basis for the production of economic value, what does that mean? How can I see maintain the basic, the fundamental dimension of society.

For instance, worker, employees, and employer, or contractor relation, or the division between work and private life with private life and public life. All of this is rediscussed as data allow us to do many things in similar way, and to produce. value out of it. I mentioned before, you know, buying groceries, remember, and reading or studying or learning, or even, you know, entertaining and learning.

So all these things, when they are done through digital devices, and this is the answer to the second part of your question, how data are the basis of this blurring of boundaries. When these activities and practices are done through digital devices and via data, They become, there is a sort of them, right, or we start doing things in similar ways.

They are all recorded in data or represented via data. And as data represents so many things in the same media, in, with the same methodologies, techniques, technologies, and process, it means that all these things that were also separated in their representation, they can now be combined to come up with new things.

An example perhaps may work because that's also very interesting in the sphere of institution, these two micro and macro that we always sort of, consider in, in our book. If we think about everyday life, we immediately perceive it how, because we all live through it. We work at home you know, we study on the, on the telephone.

So there are all this. reshuffling. But if we think about institutions, economic institution, and that's also the other part of the book, the implication of this blurring of boundaries for our economic institution, how can we understand this? So an example is perhaps the work of TripAdvisor, or if you think about any sectors of the economy that before was a very well defined sector, or was rather operating within the boundaries of a sectoral knowledge, hospitality industry.

Which meant they were doing just some things, booking of hotel or selling of holiday packages or whatever. And now all of a sudden, because of this possibility of extending the knowledge and recording, action and interaction, they can leverage on a number of things they were not belonging.

or do not belong traditionally to this hospitality industry. So they can entertainment, they can provide restaurant booking, they can provide, you know, services in other content making or service in other area. like, Uber or, you know, so also the institution change because of the blurring of boundaries, not just our everyday life.

And this dominance of platform and ecosystems somehow responds to that. They are better fit to deal with this sort of heterogeneity of knowledge that can be re transcribed. drone, let's say, and used to provide services that do not any longer belong to one specific sector as we understood it.

Adel Nehme: Yeah, well, that's really fascinating. And, you know, as we close out our conversation, to look back, well, you know, what is the path forward on regulation, right? Especially in a lot of ways, we're moving to a paradigm where machines are actively participating in the datification process.

And where AI can really accelerate a lot of the dynamics that we've been talking about today. What do you think is the path forward on regulation?

Cristina Alaimo: Look, there are some staff that need urgent attention from regulators. And I think that is already something that we should discuss. I mean, we cannot regulate everything at the same time. And sometimes Specifically looking at the European Union, you have like the feeling that we want to regulate everything at the same time, but certainly there are some urgent matters to address.

One is AI, because, of course, and I was mentioning before this use of data without telling us I mean, what kind of data, how are they used? I think this is, this should be solved and should be solved by governance and regulation. I mean, it's very relevant. As I mentioned before, if we look at data as a complex artifact that allow us to understand the world in certain ways that allow us to understand ourselves to do things, we cannot accept the issue that we have no.

accountability whatsoever on how data are made, processed, manipulated, and used. Then we need to look into that and to regulate that and to govern these processes, I think. So that's the most urgent issue here. And of course there are then different approaches to the regulation of AI and the different ways in which AI application then are packaged.

And for instance, the different risks that they pose. I think this is also a discussion that should be opened up. Another perhaps important issue that is not that much debated and should be, I think, debated more is the whole discussion of ecosystem that we made, Which belongs also to the AI And that was your question.

We are seeing all these ecosystem of data production changing because of the advent of AI. So how do we look into that? How do we make sure that there is enough access for economic actor that there are, practices that are in line with what we need or what we want in terms of market practices or production practices.

So this is a whole discussion around the ecosystem AI that perhaps. needs to be opened up. So I think these three aspects that I mentioned, the data work and the discussion and governance and regulation around data and data work in terms of AI, the discussion about the risks that certain, you know, AI products or application have for certain, Perhaps also disadvantaged, communities, that's also very important.

Some communities are more risks than others for certain applications. And third, the whole discussion around the ecosystem of production. this AI, which has a huge bearing in terms of what decisions are made, why those decisions and not others, as we mentioned for you know, the gaze of data. I think these are three interesting aspects.

Some, how we have started already debating them, but what the book advocates, I think, is to open up a little bit more the discussion around this area, considering data, not as just narrow input that needs to be sort of, either sealed or shared or whatever, you know, or ported or not, but rather think about data governance regulation by thinking of data as complex cognitive artifacts that are at the center of the making of our institutions, of our way of seeing the world, of our way to communicate with one another.

If we do so, I think really we can and this is my wish tackle or address, complex issues in complex ways. So the complex issue of regulation in a complex way, not by simplifying and risking, a simplified approach to it, because it's a very, very, very important issue for everyone and for our society.

Adel Nehme: Yeah, I couldn't agree more. And I think this is a great way to cap off our episode, Christina. Before we wrap up, Christina, any final call to action to share with the audience?

Cristina Alaimo: Read a book. I think, I think that may be one very sort of instrumental call to action, but there it is. It's already out.

Adel Nehme: it's

Cristina Alaimo: And by the way, we have this fantastic image on the cover uh, Roger Ikeda, which is a music, electronic music composer and artist. So we have very happy also about that. But no, I think the call to action is very much around the issue we've been discussed so far.

So this idea of data literacy, opening up a discussion around data that is not limited to. data is an instrument of technology, technological instrument, the input to device. We don't need to school people on how to code if we don't give also the instrument, the space, the capability to discuss, It's the more fundamental aspect of data that we've been talking through this episode.

I think that's a very important call to action, which then also links to a different idea of data and perhaps also of society. We want an open society, we want to progress, we want to have our technologies in place, but also we want not to risk to delegate to technologies important decision on what we want to be as society.

And I think that's very much around how do we define the data. Definitions are important. How do we think about data? So in the book, we advocate for a social science of data. So that's my wish that we can open up the debate around social science of data with the participation of many.

Adel Nehme: Yeah, I couldn't agree more. Hopefully, hopefully soon we will have a codified social science of data discipline. But in the meantime, we're doing the best that we can. Thank you so much, Christina, for coming on the show.

Cristina Alaimo: Thank you so much for inviting me. It's been a pleasure. Big pleasure and thank you so much for your questions and for engaging with me in a conversation about data rules.

Adel Nehme: Likewise. Thank you so much.



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