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Critical Future Tech

Issue #11 - September 2021




Whether that's because we're back to work or school, for many, September is when the year (re)starts. Cleaning up is often part of a preparation process, like before starting a new cycle.

For this edition, we're joined by YData's co-founder and Chief Data Officer Fabiana Clemente, where we discuss the need to access quality data, how to account for bias and how data and algorithms can shape society.

- Lawrence

Conversation with
Fabiana Clemente

Co-Founder & Chief Data Officer at YData

This conversation was recorded on Sep. 30th 2021 and has been edited for length and clarity.

Lawrence - Welcome everyone. Today we have the pleasure of talking with Fabiana Clemente.

Fabiana is the co-founder and Chief Data Officer of YData, a Portuguese company, whose mission is to help other companies and data scientists to access high quality data to develop better AI solutions.

Hi, Fabiana. Welcome to the podcast.

Fabiana - Hi Lawrence. Thank you for the introduction. I can proceed and present a bit about my experience and also talk a bit more about YData if that sounds good?

L That sounds amazing, yeah.

F Essentially, my background is in applied math and since the beginning I always had this kind of pleasure to see insights being extracted from the data.

So this was the reason behind why I decided to pursue data science as a career. But while I was having the pleasure of working in the world of data science and extracting insights from the data I understood that it's not as straightforward as we think. There are a lot of problems. There are a lot of challenges and one of the challenges I found throughout my career was related to the quality of the data.

Of course the problems around data quality depend a lot. It depends on the use case we are tackling, it depends on the organization you are working for, if it's a bigger organization or whether it's a startup. The pains of privacy and regulations, not giving you access to the granularity or the data you need to do things in a timely manner. Or being in a startup where the data access is there, but you have no data available at least to tackle the use case you want to explore. Or the data is not in the right form of the right shape or the right inputs, because there were manual errors, there were misunderstandings on the business definition or the problem. And that of course led to a lot of problems being replicated into the data.

And essentially this was what motivated us, myself and my co-founder Gonçalo to start the company. And nowadays, as you very well described, we have a data centric platform where we are offering this platform as a way for the data scientists to relieve the pain that they usually have while trying to have the best data possible to build a model that in production does deliver value for the business.

L And to make sure that I understand, basically there's the model side of things, which is the code if we want to call it that way. And then there's the data that feeds into that model.
So you guys are tackling the part of the data where fundamentally, if the data is not good, then the model, no matter how great it is, is going to be suboptimal, right?

One of the questions that came to my mind when researching for this podcast is: is there a sort of generic approach to what is good or bad data that you can give us?

F This is my opinion and I guess this is very personal in the sense that it's definitely the vision I'm embedding in the company. In general it definitely depends a lot on the use case and mainly on the business objective or the value we want to extract from machine learning.

That's why I think it's very hard to give the ready to use solution, fully automated, to validate the quality of your data regardless of the use case you want to explore. Of course, there are a few things that you can measure and it is possible to have them run in a fully automated manner in a way that you already know if something very off is going on with your data you can tell right away.

For example a very specific case: you want to assess whether the quality of your labels is good because it's not always as straightforward that you have labels. Then that means that you have more than enough to go and pursue your use case.

So in an automated manner, you can identify what is good or not but it might depend on the use case, the label or the target variable that you want to use.

So these kind of things is where we believe some flexibility should be given to the data scientists, and we believe that data scientists should impute the business knowledge in order to get the best information possible about the quality of their data.

"There is no data quality without knowing the use case."

L And I know that good and bad data can be on multiple levels, right? You have good clean data but that is biased towards one thing or another, and you can have great clean data that is not biased, but the information within that data is bad in a sense that you could use it to target a group of people and so it's also considered maybe bad data? Again, it depends on the model that you want to apply it to.

You were saying that in a way you need to adapt how you look at data. So you have your platform, but do you help your customers? Do you work with your customers to tailor your platform somehow? Or how does that work since you sometimes have to have a custom approach to things?

F One of the things that we say, and is always important, is that there is no data quality without knowing the use case. There is always a need to combine, let's say a development of a model, even a baseline to assess whether by iterating through your data, you are getting better results on the model that you are using.

Afterwards, if you want to tweak the model that's of course recommended, but you can work upon a baseline of quality and that has to be based on a model.

Usually the tailoring that is needed is done by the data scientists as if they were developing. Of course in some cases there are use cases that are more exotic and in those ones, we do help by tweaking some tools in order to cover those use cases.

L Thank you. It makes sense. Especially at the beginning of a company, you're going to want to understand how you need to evolve and even sometimes pivot your product in order to satisfy your customers' needs, it's very interesting.

So, to get back to the original pain points, you talked about privacy, GDPR, HIPAA and so on. Your pain was that accessing the data was hard because of all of these sorts of restrictions and protections around personal data.

So it is my understanding that, somehow, you are able to transform the data in order to speed up using it for the data scientists that work with you? Can you elucidate a little bit on how you go over this barrier?

F Of course, of course. Sometimes it's the belief that the regulations are just there telling you to not do anything. And of course security teams do prefer to go for what is, let's say, more secure and more obvious, which is just restrict the use [of data] instead of exploring it a bit more and understanding how they can still comply with the regulations and at the same time not blocking innovation or evolution of some uses.

Of course you can do a PCA or a dimension reduction algorithm in order to keep some privacy of the data. That's one of the methodologies that can be used. But when you do that, you lose explainability and understandability of what the data is telling you. The other methods that exist are anonymization, obfuscation, et cetera, where you just hide or you mask whatever is your information.

We understood that data synthesis is one of the options. Of course, data synthesis is something that is not new so it's in the market for quite some time. The difference is the technology that you can use to do the data synthesis.

I'm telling that: in order for these data to be used for data science, besides the granularity, you also have to keep for the other two main pillars: the fidelity and utility of the data, because of course you can synthesize the data, but if it doesn't hold the information and the utility of the original one, that means you cannot build machine learning use cases out of it.

So in that sense and on that scope, deep learning technology ( and not only), generative models in general are pretty good at doing this. When we are talking about, for example generative adversarial nets, they are quite interesting in a way that in a data driven manner or data derived way, they understand the behavior of the distribution of your data or the data that you are presenting to the model. And after learning these patterns, these behaviors, these correlations at first and second level, you are able to replicate this into random noise.

So you just captured the relations, the distributions, and now you apply those distributions into random noise so you can generate new data. The fact that you are using random noise and your data-driven knowledge on top of random noise, you are ensuring things that are important for the privacy aspects, which are one variability on your data.

As in everything, there is a trade-off on privacy. The more private this data is, the less utility it has or the less fidelity. So there is always a trade-off but essentially the same applies for the other methodologies privacy. And in this case I would say this is definitely one of the most data science friendly ways to ensure privacy while guaranteeing usability for the data scientists.

L A very interesting approach. I wanna ask you just some things before we move to the topic of bias. So you mentioned very accurately that, of course it is a trade off between protecting identities and the privacy and the usefulness of the output that you end up with.

So who controls this balance between both? Is it the customer or do you provide the customer the ability to fine tune that basically?

F Yeah, it has to be the customer. So in a way it's upon the users to define the privacy budget, let's say.

It is really called the privacy budget. So how much are you willing to give up on privacy or how much are willing to keep on privacy and how much utility can you get with the privacy budget? It's a trade-off and it's a visual trade-off, you can really control that to understand how much you are giving up on one in order to have the other.

L Oh, now I'm curious. How does that work? How do you set up this budget? How do you calculate it?

F So the calculations depend a lot on our evaluation metrics for example, for the utility side, especially. The privacy side and especially the privacy budget comes from also some metrics on the privacy side, but a lot from the use of techniques such as differential privacy.

So differential privacy already brings you that interesting concept of privacy budget. And essentially those metrics combined with each other is what allow us to show a bit of that trade-off.

L So they have an idea of what the trade-off is going to be.

F Kind of, kind of.

L Let me see if I can segue this into the bias part. So, in a way you guys have some metrics and some leavers on what constitutes a privacy data point, I would say? And I guess you can add more or less into this formula that determines the final trade-off outputs

So I guess you use techniques like established industry techniques to determine that. Do you also have your own sort of gut feeling? Well, not gut feeling, but your own sensibility as to what may or not constitute a privacy data point that needs to be factored into these budgets?

F A lot of what we use definitely is studied and well studied on the literature specially. Of course, some of those we have rearranged in order to be applicable for the types of data that we are researching. For example, one of those is definitely time series. It's very obvious, let's say or more obvious when you are talking about tabular data and records that have no time dependence.

But when you are talking about time series, the notion or the concept of privacy is not so straightforward. So definitely there, we had to explore a bit what is in literature about privacy and transpose a bit to the time series side. That was a challenge to be honest.

L Yeah, for sure. You guys still have a lot of ways to explore. Let me then get to the topic of bias because nowadays and for some years now, bias in data is one of those hot topics, right? " Machine learning, AI, they're doing bad things not because of the way they're deployed, it's the data they were fed that is bad. So if we fix the data we fix the output."
It's definitely one of them, one of those narratives. And, in the sense that you guys prepare data, how does this notion of bias gets into your day-to-day reality?

And let me split this into two questions. So secondly, how does that happen? But first, how important is that for you guys? This notion of bias, bias in data and in the field of AI as a whole, actually.

F I guess that definitely is one of the hot topics, but I can say that is one of the most important ones, because in the end, when you have to explain why your model is behaving like it's behaving, there are two layers that you can analyze: your model, you can understand the predictions your model is doing, getting to understand how it's behaving. But when you go and debug your model to understand why it's behaving like that, part of your explanation comes from the data.

So this is what we consider as data explainability. This is one of the most important topics that we have to explore and to monitor the behaviors we have within data, and to be able to spot potential issues for your business.

And regardless, bias is more and more a problem for the businesses and it's a problem that they have to account for. So definitely it's something that for us is core.

L So you guys help in that to your customers?

F Yes.

L Okay. Because if you prepare data on behalf of your customers and they have an issue that they need to explain, I guess they're going to go back to you and say "Hey, what's up with that data?", right?

Can you tell us a bit on how that enters your day to day operations or your strategy? What can you tell us about how that is affecting your work?

F Essentially it shapes a lot of the product roadmap. First it's not as straightforward as we might think. Let's say you have females and males and you have the same amount of females and males, but in the end, when you go and check the amounts they earn they are clearly not equal in that sense.

That's bias. That's bias on your data. And that's not obvious just by checking whether you have categories that are balanced or not. In that sense for us we have in the roadmap a big chunk of data causality which is what we are exploring for not only the part of the bias explanation, but also to help the users understanding better why certain behaviors happen when they deploy a model based on the causation that you can extract from your data.

That's how this affected for example our roadmap or our product. And in that sense, we offer the module for the knowledge and instruction of the understanding from the data. And afterwards we are also working on getting mitigation solutions to solve exactly these problems. In the first instance, we have the side of the data synthesis to help mediate the presence of bias but we are also working on different solution levels to solve different ways, depending on the bias or causation problems that you might find within the data.

L Isn't it possible that while synthesizing the data that you also transpose bias into that proxy?

F In a way it is. Let's say for example if you do have a data set and that data set has bias, your data synthesizer will mirror that existing bias within the data. But you can control that to not be replicated in your data synthesizer.

L So you need to be aware of what to modify in a way, right?

F Exactly. What to control or what to condition.

L Yeah, that is something that I wanted to ask you. So how do you understand bias because data sets are different depending on the customer, industry, the use case so I guess that there are sorts of biases that you could maybe be missing? How into this industry or the origin of the data do you need to be in order to be sure that you're not missing some sort of bias, right? Is that a valid question?

F Yeah, it is. It is. And that's why the module of the side of the bias we give it exactly as a framework that can be somehow tweaked or controlled, depending on the business logic that the user wants to impose.

Of course we have a certain level of bias. So in that sense I haven't found the perfect way to - regardless of human inputs - identify bias. And I think that for some time we will have this part of the bias detection that in a sense will depend on users and on people.

And we have to, in a way, believe that the people are doing the best for keeping the best interest for the end users that will be impacted by machine learning.

L It makes me happy to hear someone saying that bias and this whole complexity is something that can shape the product roadmap, because it means that the product indeed wants to tackle that, and it's not some sort of afterthought. But I guess for you guys, it cannot be an afterthought because the consequences can be very direct, right?

F Exactly. In our case, it had to be from the birth or in this case, in our core. But it's also fascinating. It's a world of possibilities, but I guess it was something that we have accepted when we started the company. And, and I guess we have to keep pushing for that evolution in this area and for more investment in the area of identifying bias, causality, explainability in particular, at the data level.

L Yeah, big problems are always hard to tackle.

F Exactly.

"The issue is that the data already comes with those problems because it is the mirror of society. But also what is the point of keeping society the same?"

L I want to ask you a little bit about your feelings regarding the ecosystem regarding data science in Portugal, but first let me throw you a really weird question. It's something that I've been reading lately.

Essentially, in terms of having bias into the data that is fed to the algorithms, there are two schools of thought, right? So if you observe the world and then you extract data, and then this data replicates bias, it means that you have observed the world as it is and then your algorithm will work in accordance with this observation.

So there's the school of thought that is like "use this data because that's the real world", in a way. And the other one is using data for justice in a sense, which is, "well, if that's biased and it's a bad bias, then we should use these and tweak it so that the algorithm actually produces a behavior that corrects the problem that you see from the data that you observed".

So which school of thought do you agree with?

F So that's a very hard question and I guess very hard to explain without creating some division on the opinions, I guess it's impossible.

I will tell you one reason why I don't like the first one.

This happened during COVID last year I think. So let me see if I recall this. It was in the UK or something, they had decided to use a machine learning model to do the grading of the kids at school. And they had decided this because unfortunately, due to COVID-19, it was not possible to do the final exams or something of that sort.

So what they thought was, "okay, let's be fair and grade everyone with a machine learning model, based on the data that we on students". It sounds good and sounds theoretically possible. Of course you are already understanding the problem that comes from here.

Essentially what happens is you use the data from students, but of course, some things that have impact in your life are not easily translatable into data for models to understand.

So what happened was students from schools that are more problematic got the worst grades. Of course the more problematic the school, the worse the grades. But of course you have outliers there and you have students that are amazing and they have students who were always bad at school, but for some reason, in the last month, they had better grades, which means that something changed in their life and they started doing their work in a different manner.

But the fact is that data was used as it was collected from the real world.

L "As is", right? So as it [the world] was.

F And we see that this was not fair and not even true because what I understand from that line of thought is that if you are using the data as it is from the real world, you are not going to penalize anybody because in reality, you will just stay the same.

So that's why I don't like the first one. The second one I like better, but I think there is a fine line between what is correcting the data, what is fair and what is justice.

I think it's very hard. You also put your life hands in the hands of judges to define what is fair and what is just and we see they commit errors. And I think there are no, you know, perfect solutions.

But I think we will commit a lot of errors in defining what is justice for the data. We will make a lot of assumptions that will be wrong and prove to be damaging. But I think we have a long way to go in order to identify what is, you know, correcting the problems in data collection or the problems of the world, because the problems will exist. And the issue is that the data already comes with those problems because it is the mirror of society. But also what is the point of keeping society the same? So you want this one, but I think we have a long path.

L Well, I am definitely on the second option, even though I totally agree that who determines what is fair and where is that limit and who changes and how is that changed?

I guess there are two. One is a very technocratic sort of, "let the algorithm show you what is the thing? But then it's non-human, right? And the other one is like "no, actually technology must be used to improve human lives and if the outcome is just things are as they are then inequalities for instance, will never be changed because the algorithm cannot know how to do that, right?

So technology must serve humans. And so I'm definitely on "team human" in that case.

That sort of conversation is interesting to have, of course not practical for your day to day work, but it is interesting to think about that because in the long-term, all of those small choices and ways of looking at things will compound. And that's why I'm talking with data scientists and technologists, because we are the ones that end up actually doing that with our hands, you know, like programming stuff so it's cool that we think of the future, right?

F Exactly.

L Let it be for the good at least.

I wanted to know what is your feeling in terms of these conversations happening? The sort of high level topic that we were just having now in Portugal?

Is that something that you end up seeing as being a topic of discussion with your peers or with your colleagues or outside of your company? Or do you think it is too early or uninteresting? What is your opinion on these sorts of topics in the Portuguese tech scene?

F I think that in the Portuguese tech scene we see more and more of these topics coming up.

In some cases I can understand why these topics might not be, you know, on top of your mind, it's not the core of your business.

You have so much to deal with, but I'm also happy to see a lot of the big companies and also specially on the startup scene that do show this is a concern, this is a reality, this is something that we are investing in.

And we see that of course ourselves, at YData, we are assuming that role, but you also see companies like Feedzai doing the same and others in the market, which is very good to be seen.

And I think that definitely has to come from the company itself because the data scientists are learning from the culture of the company. It's where they are working. And the more the companies motivate these kinds of discussions to show this is important, although it's not core business, this is something that we have to think about.

This will create a certain ethic on development that I think whenever a data scientist is designing a solution, we'll think about those when developing or designing or implementing. And that's why these discussions are so important and we already see those happening. I just think we have to see them more often.

L Yeah, I agree. It's not the sort of thing that you want to be thinking... Well, I would love to be thinking about this all day, but I don't get paid for that. But it's definitely conversations that I think we need to start having on a more frequent basis and that's why Critical Future Tech is here to also push forward these sorts of conversations.

Fabiana, thank you so much for sharing everything that you just told us today. I'm really happy to have had this conversation. I really enjoyed your answers on that curveball question that I sent you. I hope you enjoyed it, thank you for your time.

Let us know where people can follow you? Where can we keep up with YData's work?

F Lawrence, thank you for inviting me for the podcast, it was really amazing. These initiatives are always important in our country's tech scene.

Regarding where you can find well at https://ydata.ai, but we on Twitter and LinkedIn.

But I really recommend my personal Medium which is @fabiana_clemente, where you can follow our recurrent data science topics and where we explain more about data synthesis.

You also have the Synthetic Data Community, our GitHub which is open source. And stay tuned because we have also an open source [project] to be released about data quality.

L Allright guys, you heard it. Stay updated because some goodies are coming out.

Fabiana thank you so much and I hope we meet again and talk again one of these days.

F Of course. My pleasure.

L Bye.

F Thank you. Bye-bye.

You can connect with Fabiana Clemente on LinkedIn, Medium and Twitter.

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