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Critical Future Tech

Issue #14

It's been said that all things come to those who wait. But waiting does not mean inaction.
Patience and perseverance separate those who can from those who wouldn't, albeit it probably all depends on what you're waiting for...

This edition brings two people from Feedzai - Director of Research Pedro Saleiro and Director of Product AI Anusha Parisutham - to discuss the dynamics between AI research and product, the challenges of productizing responsible AI and its possible future in our lives.

- Lawrence

Conversation with
Pedro Saleiro & Anusha Parisutham

Director of Research            Director of Product AI

This conversation was recorded on Nov. 12th 2021 and has been edited for length and clarity.

Lawrence - Hi everyone and thanks for joining on another conversation in Critical Future Tech.

Today, I have two people joining me from a company named Feedzai, a Portuguese company specialized in minimizing risk in all things financial. We'll know more about that in a moment.

They are Pedro Saleiro and Anusha Parisutham.

Pedro is the Director of Research at Feedzai, specifically of the group focused on FATE, standing for Fairness, Accountability, Transparency, and Ethics in AI. A lot of big terms that I hope we can better explore.

Anusha is the Director of Product in AI at Feedzai. With well over a decade of experience as a product leader in global companies, she's currently leading Feedzai's mission to prevent financial fraud, all of that powered by AI.

The discussion that I hope we can have today is: how is the dynamic between researching something that is more and more important — that is, responsible and transparent AI — and how do you then market that and how does the market actually respond to that value creation?

So, first, thanks for being here and taking the time.

Maybe I will start with Pedro. Can you tell us a little bit about FATE? It is a research group within Feedzai, geared towards responsibility and transparency and ethical AI. And I'm very curious as to knowing what that consist, what do you guys do and how's your day to day life? I'm super curious, can you expand a little bit on that?

Pedro - Sure! First of all, thank you for this opportunity to share a bit of our work in this space.

I'd say that it's very interesting how this group started. Pedro Bizarro, whom you already interviewed in the past and I think in that interview, you kind of set the stage for how it started...

But basically at Feedzai, because it's operating in a highly regulated domain, which is financial services, since the early stages of the company (around 2013), they started researching and including some sort of explainability in their machine learning models to detect fraud and other financial risk.

And explainability is a little bit like smartphones: you always need to keep up and keep innovating because it's a very complex problem. How can you make AI more transparent and explainable to humans? So that humans can make better decisions, they can audit and understand these systems.

So they were constantly innovating on that but around 2016 the ProPublica stories, the Cathy O'Neil book "Weapons of Math Destruction", there was all of a sudden a growing awareness of potential, non positive consequences of AI. Especially affecting end subjects, people directly.

And in a financial services domain you are often making decisions that directly affect people's lives: from blocking credit cards or denying access to a bank account, or decisions about credit and lending.

This is really impacting people's lives. So as AI gets widely adopted and there is this AI revolution, you also become more mature about the impact of AI. And back in 2017, I was a postdoc at the University of Chicago and we developed the first open source tool for auditing bias and fairness in machine learning models [the Aequitas library].

And I was very surprised when I got a message on my LinkedIn from Pedro Bizarro, someone that I knew because he was a co-founder of Feedzai, but I had never ever talked with him. And he was asking questions about Aequitas and that was really unexpected. I had recently moved to the US and I was not expecting that a startup from Portugal would be interested in what I was doing about AI bias and fairness and audits.

So we had an initial chat and we kept in contact and later, Pedro challenged me saying that he had the budget to start a research group on fairness and explainability and other responsible AI topics.

This was kind of a very unique opportunity. You think that you can only just work on these topics in academia or in the government sector and public sector.And all of a sudden a private startup caring about embedding this type of functionality in their products, in their practice, was really an unique opportunity. And also to come back to Portugal and make an impact.

So that's how it started. But I think I've been talking for a long time now, I think I can ask Anusha — who is leading product AI — how this is evolving across the company.

Anusha - Thank you for setting the stage Saleiro and Lawrence, thank you for having me here.

So one thing that is very important, for not just embedded AI in products, but to be responsible, to have responsible AI, is to have a shared vision across the organization. And at Feedzai we've been fortunate that our co-founder and Chief Data Scientist was the one who started this.

So you have that executive function, sponsorship and messaging, top-down. And I think that makes a big difference to align teams across the organization on the importance of responsible AI and the importance of having that responsible AI thinking as part of your design. So just to add to Saleiro, to the question you asked, I think that having the top down executive sponsorship and alignment has been very critical and crucial for all the work that has been done in that area at Feedzai.

"You have fairness, you have accountability, transparency, you have the explainability part. And in addition to that: it still has to be performant and it has to be cost effective to put the solution in place."

L And just to complement, it goes in line with what Pedro Bizarro had told me. I was like "of course they have a culture that is more oriented towards that" when one of the co-founders is himself engaged and responsive and understands the need to approach these new technologies with some caution and some respect.

When you hear about ethics, how do you bring ethics into companies? It's many times from the ground up: "How do I sell it to my leaders? How do I tell them that this is important?"

In your case — and that's the lucky part I guess — it was more of a top-down sort of approach and it's just a great marriage with the work that some of you already wanted to do, right?

So of course you guys are in an industry that has a high impact on people's finances and possibly everyday life. It's interesting. You really need to be able to explain, maybe to a regulator or someone, why your product works in a certain way and that's why it's also important to be able to have that capability.

A Maybe I can start that discussion and Saleiro can add to it.

We talked about how selling AI internally, within Feedzai, was a given because the sponsorship was top-down. But you still need to sell responsible AI outside of the organization. As part of the product to sell that value proposition. And I think selling that outside, whether it's to regulators, financial institutions, risk leaders within organizations... Basically, it starts with how you sell anything, right? It's articulating " what is in it for me" or the value to the person who's going to use it, the buyer, the evaluator, or anyone who's involved in putting that product in place in an organization. And what makes selling responsible AI harder is demonstrating that value to other organizations. You have to start with defining what we mean by that, because it can be interpreted differently by different organizations.

You have fairness, that's one dimension of it. You have accountability, you have transparency, you have the explainability part. And in addition to that, something which we don't talk about that often: it still has to be performant and it has to be cost effective to put the solution in place.

So these are the things which the person buying the solution or trying to use the solution cares about and you sell responsible AI by talking to that and explaining how it clearly brings an impact to what they're trying to do.

P There is not just one flavor of explainability. So one thing that we are doing almost daily, working together with product — myself and Anusha and others involved — is defining different user journeys and where explainability can play a key role in the way these different personas interact with a complex and very sophisticated risk management platform powered by AI, but also uses other components, not just machine learning models.

But how can different users grasp what is going on? What's the behavior of the model? Why is the model making a decision for a specific case, but also in a global way? How is it behaving? How can we make the data science process even more performant and efficient and build better models by providing explanations that allow the data scientists to be better and more efficient on their job. But also the analyst, that is, our human in the loop that is making the final decision (about fraud), or is often outreaching to the end customer of a financial institution.

How can these analysts get good insights and context on how to approach the end customer? How to really make the final screening. Is this actually a crime? If it's not, is it really a legit kind of behavior or not? All of these nuances, we are working on making explainability part of this thought process for different personas.

So it's something that when we often see online very good intentions on describing explainability and talk about specific methods like SHAP and LIME and others, it seems like there is just one explainability but in fact, what we realized in early stages is that it's much more specific and needs to be embedded in these different journeys. And people from UX, people from engineering and so on. Different requirements and different tasks. Different goals and different ways of evaluating explanations.

L So how does it work? From what I understand there's more under the responsible umbrella, right? There is accountability, transparency, fairness, that's the way that you have structure (sic) your acronym and I guess maybe to some extent your team, I'm curious about that.

But how does it start? You start with a problem. Who defines that? Is it the researcher? Is it the product owner? How do you define: "this is the challenge we're going to tackle. This is the initiative and this has stemmed from..." Where, right? Where did they come from? Because Pedro Bizarro told you so? Or because it's being discussed in research circles?

Can you tell us a bit how that happens? How do you define the priorities on what you're going to work on?

A Absolutely. Maybe I can start by outlining the process we follow and then how we take it from there into actually productizing it.

We get inputs from multiple sources. Product and research work very closely together. We interact on a daily basis, we review ideas on a regular basis. So it's a collaborative effort and you're looking at the market. You're looking even outside the industry to see where people are making strides in responsible AI, that's one aspect of it. Product is also interacting with the markets and so we get ideas on where we can make this experience better and bring more responsibility into the embedded AI capabilities.

So ideas flow in from different sources, that's one thing. And then what we do is research is really looking far ahead. Research is actually future-proofing this area because they are looking two, three years down.

They are trying to tackle problems, which are not yet top of mind. Because if you're tackling a problem which is top of mind, you're probably too late. You want to think ahead. And that's what research is doing. They are really looking ahead.

But what we do with this close collaboration is we work closely with research to see the results of experimentation. And Saleiro can maybe give a particular example of one initiative, which they are working on. We still touch base on a regular basis, internally. We bring other stakeholders internal to the organization, people in services, people and presales who also have insights into the customer experience part. Even during the experimentation stage, right?

Since we are so closely aligned, we know when it's ready to be productized. And so where experimentation meets productization is when you bring the human into the picture.

So you experimented, you've seen great results, you're building the tools, but when you bring it into the product, you have to bring the human into the picture and like Saleiro pointed out, you need to think about the different personas who are going to be interacting with these capabilities. Their different journeys and how you influence that experience. It's like product management 101. You put your customers and users, right, front and center. And so responsible product management, which is putting responsible AI capabilities, embodies best practices of product management also.

But what makes responsible AI more challenging is: these different personas can come with a spectrum of skill sets. You're talking with really top-notch data scientists at Feedzai. You're talking about highly skilled data scientists at customers. You're talking about citizen data scientists. You're talking about business users who have some data science knowledge or data literacy. You're talking about analysts who are completely on the business side and you have like the auditors, the regulators.

So when you put a responsible AI capability — and explainability is a good example — it's not enough if your data scientist is able to explain the outcomes of the model. Your fraud analysts should also be able to explain. And not just that. It can impact your consumer experience because if your transaction gets blocked and you call the bank, the bank should be able to explain why, what happened.

You're not just impacting a data scientist journey and experience or a fraud analyst journey and experience. You're in fact impacting your end consumer experience. And it's how you can tell this complexity into very simple, intuitive layman terms. And that's a long-winded answer, but I think you get the message here.

And Saleiro please add to it.

P Actually, I think it was very complete Anusha. So I'd say that I'm totally aligned with what Anusha said.

To complement it, it'd be just adding one thing which is: we may think about these as something different or something that is embedded in all these journeys. It's not that we [researchers] come and say: "we are going to change the product and this will be very different", no. People were already consciously developing products with good intentions, with specific concerns in mind and even the user experience and all these things about explainability because we are in a highly regulated domain.

So it's more about: how can we add that extra differentiator factor? How to embed these different dimensions or principles of responsible AI in an already ever evolving product? Because we are constantly improving the product as part of the journey of building great products.

It's really about these different teams coming together with different perspectives, different requirements. Someone brings in a specific aspect that we may need to prioritize or study or experiment as Anusha was saying. And we in research we are, you know, what are the problems that we need to tackle far ahead? And what are the opportunities in terms of research that we can bring into them, into the product?

"When we talk about fairness it's how we distribute errors in a way that we are not damaging particular population groups that are often already under strain because of some social economic background or specific context."

L It seems like a very interesting feedback loop between the research and the product realms. And of course your answers have generated more questions, at least for me.

One of them it's a sort of a very practical question, which is about experimentation. You mentioned experimenting on the research that you developed and I'm curious because how do you experiment in an environment or in a product where let's say a mistake could potentially be damaging to the customer or to the customer's customers, right?

I'm kind of curious if you can share how you approach experimenting, right? Do you have a couple of customers that trust you and they're willing to just try it out? How does that work?

P Maybe I will start from a research perspective and then Anusha will complement on specific clients and so on.

So first there's something that is more kind of a clarification. Often there is this perception that because we are using AI, we should have 100% correct decisions. And that's not the case. Neither for human decision-making nor for AI powered decision-making.

That's the first thing we should realize. That we are not going to make 100% correct decisions all the time. Neither the systems nor just the humans. So what we want to build is really a great product and an AI that is as accurate as possible in predicting financial crimes.

And in the process of building these highly accurate systems that are cooperative, where you have a component that is the AI making a prediction but also a human that complements and reviews, and interacts with this AI.

Our goal is: how can we leverage the expertise and the strengths of the AI and the expertise and the strength of the human and create a combined system that, working together as one, has better performance, better fairness and increased efficiency in terms of operations for different financial teams. Because when we are talking about really large financial institutions — and we have some of the largest banks in the world as our clients — we're often talking about hundreds of analysts. So it's really big operations teams.

So that being said, what we want to work with when we talk about fairness is how we distribute these errors in a way that we are not damaging particular population groups that are often already under strain because of some social economic background or specific contexts.

We are talking about location. We're talking about age, we're talking about gender, we're talking about ethnicity and so on. It's really about how can we make sure that it's not just enough to have high error rates across groups. No, you want to get really good user experience and good consumer experience.

You want to minimize errors across all groups. Not just for the majority. Not just for the sake of just blind performance. We want to be really high performance, so ideally almost near perfect decisions. But because it's not possible to be perfect all the time, we want to balance these errors across different groups so we don't have specific minorities that get more affected and we are basically creating feedback loops that will exacerbate inequalities that are already out there.

We really want to be sure that risk management can be done in a way that does not promote inequality and promotes great consumer experience, reduces friction and attrition and then end consumers are happy and feel safe and banks are very efficient and highly performant.

That's something that is more kind of a thesis or a mission that we at the company we have in this perspective of responsible AI.

In terms of experimentation we should talk about it. Often we go directly to clients. Often we can team up with internal teams. But I would say that now probably is the time to pass to Anusha to give our perspective on this kind of collaboration.

A Absolutely. But you set the stage for me because you said you have to get expectations aligned and straight right off the board, and I think that's very important.

Now, to your point Lawrence, what we try to do is identify customers who are willing to be our design partners in this experimentation stage because we have a shared vision. They want to be responsible. They want to address fairness, accountability, explainability.

So we have a shared vision. But then comes the expectations part because, you know, there's uncertainty around some of the AI capabilities so you need to do a POC. You need to do more work with real data and for that, you need to collaborate with the customer.

So finding the customers who can be those design partners or early adopters who can help you with that experimentation and then setting expectations on what we are trying to achieve, what KPIs they are going to hit, but also what risks are involved with that. And then, along the way, we make sure it's a very transparent, observable process. I think that's one thing which is key.

When you see hesitance from customers it's because they cannot see the risk. And because you don't see, you can't mitigate or you can't put controls around that. So we are responsible by design, not just in products we build, but even the way we operate.

As part of this experimentation, even with an internal team, we share results of the outcomes of experiments. We share the good, the bad. We share where we can improve. And I think having that open, transparent conversation, not just internally, even with customers, helps build that trust and so they are more open to partnering with us to bring some of these new capabilities.

"When you talk about the responsible AI umbrella, fairness is one part of it. But accountability, transparency and explainability, especially for organizations in regulated industries, have always been there."

L Thank you for sharing both. And from what I can tell, it seems that you guys have a pretty healthy product culture. That is super-interesting.

What you mentioned at the start of your answer Anusha is something that I actually wanted to ask you guys. You look for customers or partners that already have a sort of inclination, like they want to do things right, they want to be more transparent themselves. So they want to use systems that are also transparent and responsible.

This conversation about fairness and ethical AI is quite recent if you look at the history of AI as a whole, right? It's maybe 15, 20 years old and then the last 10 years, more and more. And so has that made your job easier in selling these sort of products or do the industry, your customers, still need to be educated as to why this is important and why they should care? Or is it easy for you guys? What is your opinion on that?

A Maybe I can start with that Saleiro. Education is absolutely needed and I'll tell you where you need more and how you can lead to that. So again, when you talk about the responsible AI umbrella, fairness is one part of it, which is relatively new. But accountability, transparency and explainability, especially for organizations in regulated industries has always been there.

I'll give you an example from my past experience in financial services. I've been responsible for front office, back office applications... and these are not AI but you still have accountability there. An accountant will not sign off on the balance sheet if they are not able to explain the numbers which go into it. I was in capital markets so when we have a bond issued and we calculate the coupon payment as a fiscal agent who's validating that, there are counter-parties who are validating that.

So there's accountability there, there's explainability there and there's transparency there. This is something a lot of organizations are used to, some from the get go.

Now, when you embedded AI capabilities, that brought complexity to it. Some of this explainability and transparency took a back seat because of the complex nature. And fairness was not part off because they didn't have that problem to worry about.

But when you approach this education, when you talk to customers, you kind of have to approach it from areas that... they've been doing this forever. They are responsible for their balance sheets. They're responsible for things coming out of their financial applications.

So when you bring AI into the big picture, you kind of have to show them the way to continue to do that and challenge vendors who put AI models in place but can not do explainability. So I think that's the education part.

Then when you come to the fairness part, I think that's where you need to go that additional step of educating how fairness can actually impact. Fairness could impact not just the bottom line, but also the top line. Because if there is a group which is disparately impacted then it's not just from a cost point of view but that's a group you're not servicing yet. So there's top line impact, bottom line impact as well as a brand impact — a reputation impact. I think it starts from areas that have been doing a great job already and then bring them to the newer areas to understand how that impacts their overall accountability.

P Yeah that's really on target Anusha. I'll just add a little bit on the education part that has to do with functionality maturity when talking about journeys and also about efficiency in operations and so on.

So it's not enough to just say that: "you may have a risk, you are not aware of the risk, we will just create awareness". That is a good first step. But what we are trying to do is to close the loop by not just creating awareness, we are already working on "how can we make sure that the journeys for different users are ready and this capability embedded in a way that you can already mitigate that?".

It's not just about measuring or auditing. We are also embedding functionality that allows these data scientists to not have an additional kind of "cost" for embedding fairness in the processes or in building models. Because often really large organizations might be wary of: "how much would this cost to fix? What would be the impact?" And that's where we start making a decisive role. Because it's not just about creating processes. Processes are very important. We need to bring awareness and measure in the angle that Anusha mentioned for something that they are used to. And we need to educate them on: "this is a new dimension or a new risk that you also need to assess". But also how can we show you that you can even differentiate from your competitors in a way that, by design, you can deploy models that are equally performant and fair. And we are showing that in the research that this is possible.

And there is something that is common sense or common in data science or for someone in a data related field kind of coffee shop conversations.

There is this notion of there being a huge trade off. And it sacrifices a lot of product performance to get fairness and what we are showing by doing experimentation and building advanced AI tools is that it's not enough to just use off-the-shelf things out there.

We are developing in-house these specific skills in analyses and capabilities. We developed something that is Fairband that is an example of how we can automatically find performant models and mitigate unfairness at the same time or mitigate biases at the same time, automatically embedded in the data science loop.

So, this is a differentiating factor. That is also appealing for these types of clients.

L Now as you answered and as you went further, I was thinking it is interesting, maybe even funny in a way that it's the regulated industry that may innovate and lead transparency and fairness in AI because they actually need it fundamentally, right? Because they are regulated and because they have higher scrutiny, let's say.

So what I want to ask is: could it be from the FinTech AI that some advancements go forward. And I think you guys are a perfect example of pushing the boundaries with Fairband, right? Could that sort of research then be adopted by other industries or other companies deploying AI, for instance?

P Yeah, absolutely. So if you look at the EU bill on AI risk assessment, that was presented for discussion earlier this year, the EU approached this in terms of regulation of AI as defining high risk applications.

If you traditionally think about highly regulated areas or domains, they are domains that are high risk by nature.

Think about healthcare, think about insurance, about hiring, law enforcement and financial services. That's how the EU commission is looking at it. We first need to start with high risk and then naturally what gets adopted in high risk will then be adopted in other, not so high risk domains.

And I think that's what's probably going to happen but I think Anusha should also complement this.

A Well, absolutely. I agree I think the regulated industry will pave the way. And that's because for some of these topics, there might not be urgency" around it. People could question it saying like: "why do we have to do it now? Others are not doing it."

So definitely the regulated industry and not just AI specific regulations.

GDPR is driving that from the data privacy point of view. It will impact, again, how we use AI — how we use data for AI. And similarly in the US if you think about it, if you have to do business with a financial organization, or even if you have to do business with the government, there are some regulations outside of AI regulations that you have to adhere to.

And if you have to do business with organizations in regulated industries, you're kind of bringing that responsible AI into the fold automatically. And so I think the regulated industries will pave the way and will then be followed by other domains.

L Thank you. I think it makes total sense and personally, I'm just happy to see Feedzai being one of those leading companies in that sense. Having said that... yeah, go ahead.

A I just wanted to add one thing — and Saleiro said this — I really want to emphasize that. And to go back to one of your earlier questions of like, you know, education and awareness.

I'm happy to say that Feedzai is actually walking the talk. We're actually showing and not just saying "you have to do this and create awareness". We're actually coming up with solutions, which will help organizations solve this in a cost-effective way and in an efficient way. I just wanted to emphasize that.

L I totally agree. And that's why I've been so annoying in sitting down with you and just digging a little bit further on the mindset and thank you for sharing about the product-research relationship. I find it very interesting and the whole culture thing.

So I know we've been talking for around 45 minutes now. I have one final question before we part ways. And it's a bit of an open question.

You guys are lucky to be in a company that values, well, these sorts of values, right? And actually wants to deliver a product that will have a positive impact on society and customers and so on.

What would be your advice for technologists, product managers and whomever you want that are within companies — technological ones — where this conversation is not happening for one reason or another? What would be your advice to those people that want to bring that to the table, but they don't know how to do it?

A I can go first. It's a great question. I'd like to say at Feedzai we've been fortunate but that's not the case for a lot of other organizations and even for Big Tech, because we are hearing this in the news. So I would say from a product management point of view, AI product management is responsible product management. It has to be that way.

So for product managers who embedded AI capabilities into the product, it is important for them to make sure that products can be trusted, are responsible, fair, and are explainable. And it is also their responsibility to educate bottom up. When it's not top down. So to educate their leadership team, their management, the need for that.

And again, how do you sell it? You sell it based on what's in it for them. You know, it impacts the top line, bottom line. It impacts reputation. What is the opposite of that? Sponsor is responsible. You don't want to be that. So I think as an AI product manager, AI product management responsibility comes with it.

And if it is not top down in the organization, you should take it on yourself to actually educate others in the organization and do that bottom up evangelization of not just the product, but the need to be responsible.

"For product managers who embedded AI capabilities into the product, it is important for them to make sure that products can be trusted, are responsible, fair and explainable."

L Fantastic! Pedro, do you want to add something more on the technical side of things?

P Without overselling Feedzai, first I want to really just highlight again what you said in the beginning, that we are fortunate, absolutely. And I mentioned Pedro Bizarro in the beginning, but I was a month working in the job and I had a meeting with the CEO, Nuno Sebastiao, and he said something like: "this is a pillar and we want to stand out in this space as the ones that, when you think about all these responsible AI principles, we want customers to think of Feedzai".

But of course, if you work in an organization that doesn't look to these principles in that way, I think it's not a situation in which you should feel your hands tied. There are things happening outside as well.

I believe that in a few years, we won't be talking about responsible AI. AI will be responsible and all these principles will be embedded in the way you build AI, in the way you build tools.

So I just want to end with an optimistic tone in the sense that I think it's a question of maturity of the AI.

I think we already passed the awareness stage and we are really going towards the more solution and adoption space.That's where we are moving towards. So I think there are lots of tools out there, even Microsoft has a responsible AI tool kit. So if you are a data scientist, there are already tools that you can start using so that you can start doing your own role in your job and you start looking through these analyses.

Also it's just not enough to think about these as problems, it's more of an objective. So when you're doing your models, when you are building your products, don't think of these from a negative perspective of: "we don't care about that", but more about "let's make these an objective".

So let's make sure that in our requirement analysis, when you are building a model, let's not just evaluate performance, let's also report fairness. If there is an issue, let me try to use the tools.

And I'm a believer that when we go to leadership and come with, you know problems but also solutions — and I'm not saying that you need to have solutions for everything, but if you start measuring you, start making these a KPI. So I'm definitely a believer that if you start measuring things and show this to leadership, they will start optimizing for it as well. And I've seen these in different areas, that organizations really want to strive for perfectionism. So they really want to build great products. And I believe people have good intentions in general. So if you start with it as an objective, I'm very sure that people will optimize for this as well.

"I believe that in a few years, we won't be talking about responsible AI. AI will be responsible and all these principles will be embedded in the way you build AI, in the way you build tools."

L That's a great ending.

So essentially the trend is there. We are moving towards a world where it's not even a matter of whether or not it's responsible. It is embedded in the industry and everyone's work. And in order to get there, it's all about the small actions that we can take individually in order to bring that into our daily work.

I totally agree. It's a great way to end this segment. So thanks a lot for sharing, really. And I think it's also with those small moments that other technologists, product managers, designers, whomever can be inspired and realize that it's possible. I mean, not everyone is lucky to have your culture, but it's possible.

And there are many examples of people pushing things forward in that sense.

I want to thank you again Pedro and Anusha for being part of this. And I think we'll probably talk [again] in the near future.

P Thank you, Lawrence.

A It was a pleasure.

You can connect with Pedro and Anusha on their LinkedIn here and here respectively.

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