Telefónica Builds a Better World With Ethical AI
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New Horizons: Well, hi, everyone. Today, we’re here with Dr. Richard Benjamins, who is the Data and AI Ambassador at Telefónica. So, thank you, Richard, for joining us today.
Richard Benjamins: Yeah. Thank you very much. You’re welcome.
New Horizons: I know Telefónica is based in Spain and is one of the world’s leading telecommunications companies, but you also operate in many different countries. Can you tell us a little bit more about Telefónica and what makes it different from other telecommunication providers?
Richard Benjamins: So, Telefónica is a company already existing for almost 100 years. We have like three operations in Europe. It’s fixed and mobile. We have another maybe 20 operations in South and Central America, like Brazil, Argentina, Colombia, Peru, Chile, Mexico, et cetera. I think if there is one thing that I like from Telefónica, that it is in a constant changing environment and, so far, always ready to prepare for change and to do that.
New Horizons: And your focus is on big data and AI, machine learning. How does that fit into your current role as data and AI ambassador for Telefónica?
Richard Benjamins: Since the beginning of my career, I’ve been always working in the area of, let’s say, intelligent information processing. It has changed names over the years. A few years ago it was called semantic technology or semantic web. My background is actually very good for what I’m doing.
New Horizons: And why should people be excited about big data? How can it add more value to businesses?
Richard Benjamins: I think big data has become important because many organizations have realized over time that taking decisions is hard, (and) decisions were taken based on intuition and experience. But recently, with big data, people are looking towards more data-driven decisions. If you have big data available, you can take much quicker decisions with better insights.
New Horizons: What are some of the most common misconceptions or fears about AI, and how does Telefónica approach addressing people’s concerns about AI being more pervasive as we move forward?
Richard Benjamins: Ok, so, I think there are two major confusions and a number of fears. I think one of the misconceptions that machine learning is the only thing there is in artificial intelligence. Artificial intelligence is a much wider field than just machine learning. It also includes natural language processing, knowledge representation, and reasoning, planning, like we do planning; long-term planning of a business or a holiday, et cetera. AI systems currently are very good at looking at data and extracting patterns and then suggesting decisions, but they have no understanding of what they are talking about.
Another misconception is that people often confuse the AI we have today, which we call weak or narrow artificial intelligence, with what we see in science fiction movies, what they call artificial general intelligence, like we humans have. So current AI is very successful at very specific, very narrow tasks. If you bring the system one millimeter outside the scope of expertise, it completely fails, abruptly. Whereas we, we can reason, we have common sense, we can find our way. We are good at a few things but then we can do reasonably well on a very huge amount of variety of tasks, and AI currently is not doing that. So, people tend to confuse that and that leads to all kinds of speculations about the future which, in turn, leads to fears, yeah?
So a few fears are, for instance, that humanity will lose control to robots, like we see in the movies. Well, nobody knows whether that will happen. And if it will happen, then it will take a long time for it to happen. So, I would not be afraid of that at the moment. The second fear is that all those machines will take over our jobs by automating not only physical tasks but also intellectual tasks. And, to some extent, that is a fear that is justified. But, as in any technical revolution that we’ve seen in the past, many jobs are disappearing but a lot of new jobs are appearing. And usually, you can’t even predict what jobs will appear.
What will be very important in the short term is the interaction and the collaboration between people and machines. That’s something that will take a big leap moving forward. And there might be a risk in this revolution because it’s going much quicker than the previous revolutions. Usually, we have time to adapt to new jobs, new work but, in this case, there might be a number of people who will be displaced because they’re unable to catch up. Meaning up-skilling is very important in this respect.
There is also a fear that if your data is biased against certain, let’s say protected groups, and you apply that algorithm to a wider group, then the algorithm might discriminate. And that is true, and there are many examples where this has happened. The systems discriminate because they’ve learned from the data, the data is coming from the real world, and the real world is not fair. So, if you don’t intervene, then actually you can automate this undesired decision making, and that leads to undesired, though unintended, results.
New Horizons: Well, and I think those are all good things to be thinking about and talking about. And I think that’s a perfect segue into talking about Telefónica’s Big Data for Social Good department. What kind of collaboration are you working on with different organizations, such as the UN and other companies that would like to follow your lead?
Richard Benjamins: Actually, I founded that department I think about three years ago, because we had a lot of corporate responsibility areas, CSR areas in the company, and they were looking for some hands-on experience with these humanitarian organizations like UNICEF, United Nations, FAO, World Bank, the Inter-American Development Bank, et cetera, to give it some more substance. So, let’s say we set up this area. And, since then, we’ve been working on several difficult problems for the planet. So we worked on natural disasters, because big events are always reflected in a mobile network as a deviation of a normal day. So, if there is an earthquake or landslide or flooding with heavy rains, you see the patterns change in our mobile network and those are kinds of proxies that we share them with, in this case, UNICEF, to help them understand better what is happening, planning the relief, and also understanding better the disaster preparedness of those areas.
We also worked on disease propagation like contagious diseases. Based on our data, we can build mobility matrixes, which tell origin and destination of populations. And we can, because this is very stable, if we know that disease breakout is in some area, based on those models, we can help humanitarian organizations and governments to help them predict where the disease will propagate faster than other regions.
A thing that we are doing recently with the Food and Agriculture Organization. In Colombia, there are many people who fled from Venezuela. But also within Colombia, there are lots of people because of climate change have lost their livelihood, and they are forced to migrate to other areas. And governments have a hard time in finding out where those people go because usually it’s happening in an unofficial way. But we can see in our network where groups of people are moving and then we can help them to estimate the amount and where those groups are such that the government can send help to those people and take care of them in terms of education, health care, food, et cetera.
And the last thing we’ve started recently is about child poverty, and that’s actually what we’re doing here in Spain. We have to cross our data with data from the government, with open data, and other kinds of data, but it seems to work. So, we have lots of projects where we work with those organizations. And there is also a lot of commercial projects with governments where we especially help them with understanding tourism and transport.
New Horizons: Can you give us some other examples of how Telefónica is leveraging AI and machine learning?
Richard Benjamins: We leverage it in three big areas. One is internally, to optimize our business. So, that’s about customer retention, churn prediction, marketing campaigns, how we plan our deployment of our mobile networks, how we manage and man our fleet of cars who go to customer premises to install routers or set-top boxes, et cetera. So, there’s a lot of things. Any process within the company that is managed by a system, and they are all today, leaves a trail and you can analyze the data and try to optimize it. So we have a range of many use cases that we do, and then we try to replicate the successful ones in other countries.
Then we have a second block, is what we call the changing the customer relationship and changing the interaction with the customer where we use artificial intelligence like natural language processing and dialogues, where we let the customer talk directly to an artificial intelligence system to solve particular problems they have. It’s not generic questions like the frequently asked questions like what should I do to cancel my contract, but it’s really the person is interacting directly with his personal data or her personal data in our systems. So, changing the customer interaction to be much quicker, more accurate, and also more scalable.
And the last part, we use AI and machine learning is what I just mentioned, the external monetization. So, that is B2B customers, where we give them access to mobility insights or to footfall insights. And this is also the part that is happening with the organizations like the United Nations, but there usually we have a special rate.
New Horizons: And that leads me into my next question is that Telefónica is one of the first companies to establish an AI principles and ethical guidelines. What does that mean? What are ethical guidelines for artificial intelligence and how are you looking to implement those?
Richard Benjamins: If you go back to the fears that we just talked about, then some of those things are within the realm of an organization, a company like Telefónica. Some of those issues are outside. To avoid those problems, we defined some principles of AI. We don’t call them ethical guidelines, because that is too broad for an organization. The principles are that the use and design of artificial intelligence should be fair. It means that it should not discriminate between protected groups like we said before. And also that the impact of the errors of the machine learning model, in terms of false positives and false negatives, should be taken into account when you optimize the algorithm for accuracy. That’s something that is not done as a standard procedure, so that’s what we understand by fair artificial intelligence.
The next principle is it should be transparent and explainable. Transparent in the sense that, if our customers talk to a machine, they should know that it’s a machine. We should not try to trick them in a conversation, thinking that it’s human but actually it’s a machine. So, that is transparent, that’s complete openness, it’s also compliant with the GDPR. And then also explainable, which means that if an algorithm takes a decision or recommends a person a certain decision that has an impact on people’s lives, then we want to understand how this decision came about. We want to understand how it works to some extent.
The third principle is it should be human-centric, which means that it should not go against the international human rights, and should not go against the sustainable development goals of the United Nations. Of course, that’s a very broad category but, in general, we do want to comply with those things. And this is also captured in our business principles, which are much broader than only artificial intelligence.
Then the fourth principle is privacy and security. It’s not specific to artificial intelligence, because any digital service that has to do with data is about privacy, and security is also important, so we kind of inherit this from our other principles we already have. We have a methodology for privacy by design and security by design.
New Horizons: Based on your years of experience, what does the future of AI look like to you, let’s say, 5, 10 years from now? What would you expect to be commonplace, and what would you expect to be groundbreaking?
Richard Benjamins: I think the implications of machine learning will increase significantly in other areas as well. It’s now still mostly in the realm of large companies. I think it will move to a lot more small and medium businesses, not because they hire clever people, but because many of those things become out of the box. There are already platforms where you’re not a data scientist, you upload your data and you can do a training of your algorithm, you can do deep learning, you can do predictions, clustering, national language processing, et cetera. So, a very interesting platform is BigML or Big Machine Learning, where you just go and you don’t have to do anything, you can upload your dataset and you can play around. If you want to use more space, then you have to pay, but you can start just to play around. So, that will happen a lot in machine learning.
The success of AI today is about supervised machine learning. There is a lot of non-supervised machine learning, which is still in its infancy. Think about a small baby, just born, doesn’t know a lot of things. And after a few months, just by observing the world, it understands many things. So, I think it’s this kind of unsupervised or mixed supervised, unsupervised learning that will probably be investigated a lot at the best universities and the big companies to understand how can you build more knowledgeable artificial intelligence rather than just an artificial intelligence that learns patterns from data without actually understanding what it learns. So, I think that’s a big breakthrough on the research side.
New Horizons: One final question. Beyond your own work, what innovations do you think are poised to impact society in a beneficial way and what challenges do we still need to overcome?
Richard Benjamins: Well, first of all, what I’ve been talking about is not only my work, it’s the work of a lot of people across the company and even outside the company. I think what still needs to be developed much more is data sharing between private companies and the governments.
New Horizons: Um-hum.
Richard Benjamins: So governments, in principle, they publish most of their data as open data in a portal where people can take it and do things with it. But only a few businesses share their data like Telefónica. If you look at, across the board, how many companies are actually sharing privately-held data with governments to improve governmental decision making, to improve the examples I gave — acute disasters in the world or even to solve the big problems in the world like climate change, like poverty, clean water, education. If they had a lot of more data available, I think governments could do a much better job in the end and turn this world in a better place and working around the big challenges we have.
New Horizons: Thanks again for your time. We know it’s valuable and we really appreciate your insights.
Richard Benjamins: Yes, of course. It was a pleasure being here to share the ideas I am passionate about, and anything that can help to turn this into something that is good for everybody is very welcome.
New Horizons: Again, we’ve been talking with Dr. Richard Benjamins, Data and AI Ambassador at Telefónica.
Richard Benjamins: Thank you for having me.
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Dr. Richard Benjamins is Data & AI Ambassador at Telefónica, LUCA where he is responsible for making Data & AI sustainable from a societal and ethical perspective. He is among the 100 most-influential people in data-driven business (DataIQ 100, 2018). He was Group Chief Data Officer at AXA (Insurance) and has worked for 10 years at Telefónica occupying several management positions related to big data and analytics, touching all areas of the value chain. His passion lies in creating value from data — business value, but also value for society: he is the founder of Telefónica’s Big Data for Social Good department. He currently works on how to make Data & AI sustainable from a business, societal, and ethical perspective. He is member of the B2G data-sharing Expert Group of the EC, and a frequent speaker on Data and Artificial Intelligence events. He is also a strategic advisor to BigML — “Machine Learning made easy”. He was co-founder and director at iSOCO (1999–2007) and has held positions at universities/research institutes in Madrid, Amsterdam, Sao Paulo, Paris, and Barcelona. He holds a PhD in Cognitive Science/Artificial Intelligence from the University of Amsterdam.