Silvija Seres: Hello and welcome to learn dot tech. My name is Sylvia Seres and today we're going to talk about tech for good and A.I. and   data and so on. My guest let me see if I can get this right. Jennifer Clausell-Tormos I am a wonderful lady from Spain, an entrepreneur whose company is called Developed Diverse welcome.

Jenifer Clausell-Tormos: Thank you.

Silvija: I was very lucky to catch you while in Norway at the end of  accelerator time with Catapult. Correct.

Jenifer: Correct

Silvija: We like to talk about what you do and why you do it and why do you think Catapult is a good partner for you to do that. Before we do that could you please tell us about yourself

JS: about myself. Yes I am. Jennifer as you as well said and I am the CEO and founder of Develop Diverse. My background is a research scientist. I have worked in tech over the last 10 years in academia and in industry and what they mainly have done is developing tech platforms for addressing new drugs…

Silvija: …pharmacy or...biomedical.

Jenifer: Biomedical. So developing tech platforms for biomedical purposes. So basically to accelerate the process of getting new drugs for different types of diseases.

Silvija: So basically you research on clinical data and then or how I don’t have no idea how that works

Jenifer: is quite unrelated to the topic of my company or my current tech company. So what I was doing there was to mini authorized experiments into their micrometer or nanometer level so that we could accelerate experiments to experiment more high throughput with this called so molar volumes and faster 

Silvija: Very good. And you did this in Spain.

Jenifer: No actually not. I did it in France.

Silvija: OK.

Jenifer: I did my PhD in Strasbourg so I spent five years working on this project.

Silvija: and your PhD is in computer science or biotech or how does it sort

Jenifer: is in biochemistry and building tech platforms like hardware.

Silvija: So lab on a chip in Norway?

Jenifer: Right. Yeah precise. Not everybody knows so yeah life on a chip.

Silvija: Very cool. So these are basically small labs that we eventually also can swallow. If I understood correctly once we get there. With a good digestion maybe so but these things might make it possible for people not to have to go to hospitals to do things like blood tests etc.

Jenifer: For instance…

Silvija: …if they could make this democratized.

Jenifer: precise yeah.

Silvija: I understand that this is one of the areas where people expect enormous growth going forward. The whole kind of chemical side of computing and you leave that super lucrative space in order to work with diversity.

Jenifer: Yes that's right. So I met myself working in tech for 10 years. I experience the lack of women in leadership, the lack of women as peers the lack of speakers in conferences and combined with the expensive inequalities in the workplace of myself and all of other colleagues. This made me take that decision of quitting my job. I am using my skills in tech to build a tool that will help speed up the process of getting diversity in tech and top management.

Silvija: So you have a philosophy behind this in a way. Well first of all, let's talk a little bit about the tool. It's A.I. for discovering biases and I mean why do we need this and how do we do that?

Jenifer: So it's simple as people like to call it a spell checker for biased language. Everybody relates to that much faster. So it will scan your website any tech you can upload or even an image and you will detect the bias language or biased content. It will be highlighted in a color depending on the type of bias if it's gender bias and ethnicity bias or age bias and will propose an inclusive alternative when hovering on the color word.

Silvija: Why is this important?

Jenifer: Well this is very important because today we many companies lack talent they lack diverse talent. They do not attract it and one of the big reasons is because they are not talking inclusively. They don’t communicate in an inclusive manner.

Silvija: And they are selling very stupidly when they try to attract women.

Jenifer: Yeah they do not manage because when they tend to advertise a job a job ad in their male dominated field they tend to use lot of masculine wording meaning words that are more attractive to man and discourage women from applying and they do it unconsciously because they were grown up we grew up like that certain stereotypes are embedded in our culture and our brains and we think that. A male stereotypic job should have people that can copy those words and women. In this case when I'm talking about male-female women have not been raised with words such as ambitious rolling up your sleeves or having to be confident of strong skills so then women do not relate to it and it's not that they are not ambitious is not strong or whatever whatsoever.

Silvija: they don't see themselves as the person for their job even though they have the skills because it's described in a way that basically makes them scared .

Jenifer: Yeah. They do not feel they belong to that place. They don't feel they can relate those skills to those objectives.

Silvija: Can I ask if you are there. So I really you know we talk a lot about A.I. and ethics these days and we want to make ethics into our A.I. tools but really I don't think there is a global ethics. I mean there are some who believe this is where we need philosophers really. There are some universal ideas of good and bad but other than that at the next level of detail you have the different religions that can't agree. You have the different cultures that have very very strong differences in the way that they understand both biases and values. Do you see a very big difference between different countries and cultures when it comes to the sort of bias you're trying to eliminate?

Jenifer: Well we have not been looking into every different continent. So we have been mainly focusing within Western , Europe and western like countries but within those that I just mentioned there is an overall same pattern of what the socialist stereotypes are. 

Silvija: What are they?

Jenifer: Well that the women are more suited for conversations that are related to soft skills like caring relationship oriented than men are more oriented and more suitable for positions that require being individual and objective oriented and being bold and so on. And those are the typical stereotypes fit in gender. But as well we add that agendas regarding age like older people maybe are not suitable for working in an Agile environment. And this is not the case. So those are stereotypes we grew up with. And they affect how we perceive language.

Silvija: and. Have you applied this anywhere?

Jenifer: You mean your companies have used it.

Silvija: Yeah, develop diversity. Do you have customers ? How do you roll it out?

Jenifer: Yes we do have customers. We have universities on board then as well as tech companies on board. They are the ones that are the most interesting or interested in that because universities lack women into management for instance. Also diversity overall but especially women. They even have trouble getting females into their school programs or university programs. So they are really interested in that and tech companies lack talent overall. So they are actually missing a local talent which is women and other diversity minorities. It has been shown that three out of four women that are educated in tech do not pursue tech positions and nine out of 10 minority people from other minority groups that study tech will not pursue a tech position. So, we are helping tech companies be interested because they are missing on their local talent and that is why our tool can help them there. Plus diversity benefits that's on top. It brings more innovation and creativity and more productivity which has been clearly shown to have an impact on the profitability of the companies so companies want this. And those two specific segments are the ones front runners right now.

Silvija: I asked you about what you think are the most interesting controversies in what you do. I mean we all agree that there is language bias and I think you know it's not very constructive and still we have two wonderful ladies in Norway that wrote a book about gender bias. This is a lot of publication and the many people are surprised many people don't seem to have caught on to all of these concrete examples and when well-known experiments as you were saying that you find that the biggest controversy is that actually this becomes a political thing when you're pitching your startup for. Tell me what you're thinking there.

Jenifer: Well it has been a bit actually frustrating when you tried to pitch your purpose of why what drives you building that company and suddenly you are told this is political. Why once being a woman or being a transgender this would be political and not just normal. And so it has been frustrating on how to pitch the potential of the tool or. The potential of the problem you’re solving when you are getting the feedback on that level.

Silvija: How do you work it?

Jenifer: How do I work it out? Well I am very open to discussion so I always try to ask customers as many questions as I can so they can understand their point of view and what exactly they mean. And then I try to work around that. So we actually succeeded in our demo day in the Catapult accelerator. We managed to make a pitch where everybody felt included. And it was not political. We were very glad of that because that the way people listened to our talk and do not feel that they are being challenged or that they have to want again to hear this feminist like perspective.

Silvija: I was just smiling because I was. Even after all this you know me for stuff and so on. I was recently on a big conference given by a software company. I was one of my key nodes. There were several other women but then at the conference the facilitator gets on the stage and starts making jokes about PMS and God knows what and he was just sorry. Why did I suddenly come into you know. So I think people have many of these things where even making jokes that you know are perfectly fine in one setting become. I mean that's not OK anymore. And I think you need to break up some comfort zones with what you do.

I think your tool is necessary and useful simply because I think many people have absolutely no understanding that the language they use and they insist on being able to use is not okay anymore.

Jenifer: I just…

Silvija: …basically just saying that I think those people who say this is political and we don't need this tool probably don't realize that they are doing it themselves.

Jenifer: That's actually totally right. That's because it's so unconscious they do and they are not they have not ever experienced. So most of the type of the audience that answer that way is because they have not they are not part of any minority group. They have never experienced any type of discrimination. So they have a hard time relating to me. The problems I'm solving with these tools and therefore it makes it difficult for them to perceive what is actually the problem. So the question is do I only then pitch to the people that relate to it or so they switch their pitch and then show just the numbers that can relate to the revenue and to pain of those companies. And then this helps. But what we did in order to crack the code for giving a good pitch on demo day was to interview our startups in the co-working space. In Catapult mainly the main men there were the men colleagues and we asked them for certain. So when have you ever felt discriminated against? Could you maybe mention a bit of it. And that's where we realized what we already knew that most of them have never experienced discrimination. They couldn't remember they even asked me, can you give me some time to brainstorm. When I was last discriminated against and I felt really I can tell you directly it was just two days ago last time I did. So this is actually not their fault I have in order to be able to pitch and they understand the message we need to understand that they cannot easily relate to that. So we need to help them to put themselves in our shoes to be able to relate to that. And this requires more effort more work and that's where we are trying to you know to become better at to be able to bring the message through and not being pinpoint as

Silvija: too revolutionary.

Jenifer: Yeah you could say

Silvija: you are being funded or supported in one way or the other by Catapult.

Jenifer: Yes that's right. One of their benefits among other things joining Catapult is that they give a precedence investment in our company so that they say help us to you know to catapult us to the next level of the future.

Silvija: into the future. You see, you're in Norway now. I think the second most gender equal country in the world according to a World Economic Forum. I think there any advantages or disadvantages with that.Maybe we've solved it already here. What do you think?

Jenifer: There are advantages and disadvantages of being in the Nordics when trying to build that company because we are indeed based actually in Denmark. So advantages of being in Norway or in Denmark is that they are all aware of the need for gender equality or diversity overall. And this is good because they listen and they are open to solutions. The tricky part comes because there are already top you would say in there in the ranking. They think we have already solved the problem and this is unfortunate because it's not true. They have got far with awareness but they didn't get far enough to convert that into numbers into what it is into if you like to divert it to numbers in top management in companies in the tech. Still if we look at the actual numbers none of those countries go to about 35 percent of women in Borch at all. So they came through far but not far enough and being so much in top makes them question this sometimes.

Silvija: It has some costs as well. I mean every change has them right. So say a little bit about how do you use artificial intelligence and machine learning for finding this bias.

Jenifer: Yes. So we use a technology called natural language processing. That's actually one of the core technologies that is part of A.I. you could say as well. This helps us identify the words independent of context so will help us to detect the word with the right context with the right meaning. So that we detect words that are bias text in that context and not in any other context. Another way of using these technologies is by to build a predictor. So once you have a very good data set that you have curated or annotated and you say you know those are bias then it can help you forever and every new text that comes in you will learn and will be able to detect new patterns that you have not seen before.

Silvija: So basically it can help people with new text and by marking as you said the language that's potentially biased to reword it.

Jenifer: Yes. And also it helps. Like language evolves and words are the word bias ten years ago are not gonna be bias the next within the next ten years. So the technology of machine learning helps you to keep up-to-date with how language evolves. So it's not that there's to ensure that you are. Yeah you are up- to-date and don't keep on the stereotype that are already gone. Stereotypes are difficult to get rid of but slowly over 10 years.

Silvija: they get worn out.

Jenifer: Yes. So that's why we use machine learning as well.

Silvija: Very good. And one of these stereotypes we talked about gender but you talk to those about age so all the young people being able to get the new jobs and so on. Is that something that we can wear out?

Jenifer: Yes. What do you mean by that we can…

Silvija: …remove?

Jenifer: Well we can if we become aware of what worked and actually affect people because of their age it affects both we hire young people for positions that we believe men I mean older people are not eligible for instance when you are. You want to go for the newest technologies or Innovation. You tend to think that.

Silvija: Has to be somebody under 35.

Jenifer: Yeah. Or when you want to build a dynamic culture or a flexible culture suddenly you don't fit the older people and you do it unconsciously and when people are reading on it they actually get discouraged. But the same happens when those companies ask for. We want you to have 5 years experience and 10 years blah blah.

Silvija: Is it really necessary?

Jenifer: Yes. And then they discourage people with the potential with a motivation with the knowledge which we are not quite not apply only because they exaggerated a number of years of experience or they even use terms like mature where they are gonna like what do you mean by mature. Maybe you when you're talking about a senior and junior which relates to actually your experience but not your age.

Silvija: Yeah.

Jenifer: So there are many different way of relating to what you want on a job without talking about the actual age.

Silvija: I think language matters more than we realize it's interpreted very differently by different person. But it's just showing respect to other people who might read something else and what you write might make you a much better communicator as you say

Jenifer: Yes. And other type of biases that our tool is also detecting and we are soon going to upgrade it as well is in terms of disability or sexual orientation. And yeah because those are two terms that we actually in Catapult one of the evenings we decide to make a game my colleague and me we make we make a small sentence about an email that could be easily sent could an example have been sent in a company and there were terms like “what about let's go out for a Friday bar or bowling”. You could think oh let's have a men's night out or so then I'm like yeah let's without our wives or without our partners. So this this unconsciously talks about forget that there are more relationships than only heterosexuals forgets that disabled people might not be able to access games such as bowling or other people that do not drink alcohol Friday the bar is highly related to alcohol. So I would also help those companies to become aware of these terms that will relate to other groups that we don't even think about.

Silvija: So, you design some of your thinking around a book called gender equality by design by a lady called Iris Bonnet. Or tell us a little bit about what she's thinking.

Jenifer: Well the big inspiration of this book has been that they have shown they have proven how gender inequality happens. Just by the way we used to run things. So as simple as they show what they like about that look, they show research articles explained in a very easy way for any audience to understand and give you explanations of things that are simple as the image on the wall. Depending on who is in this image is going to affect the person who is in the room while giving a speech or depending for instance what this will affect is that when a woman has. Later Hillary Clinton presented this paper. The woman tends to have a better pitch and more quality compared to when there is no image or when there is an image of a man. This is one example and give you a trick like how by design by changing the order then you would be questioned in an exam for a schoolteacher for high school students or how you write the bill to induce people to pay the bills to pay taxes. Some people don't pay taxes depending how you design the actual page is going to help people to get more motivated.

Silvija: To do the right thing.

Jenifer: So it's called a behavioral list by the whole behavioral design. So they prove how by for instance by having quarters in a very low level can actually change the perception of the expertise of the qualification of a leader just by having quotas and so they show how much research has proven the existence of inequalities and how actually you can learn how to treat that in a very simple way. So that's why this was. It’s a very good way for me to show how gender inequality is among other type of inequalities that are present in our everyday life. And we don't realize.

Silvija: So Jenny, you have a quote for our listeners that you would like to leave as a little gift.

Jenifer: Yes. The quote says if everyone's thinking alike then no one is thinking of a quote by Benjamin Franklin. And where the quote says is that if we expect to do well we relate to that in our company because if we all work on high end people look alike. If we all have a team that looks alike we'll never be able to innovate. We'll never be able to perceive the needs of our customers who will not be able to perceive the actual work as inequality is. And we will not be able to expand marketing wise if we are talking about the company's perception. And besides that, I am missing out on different perspectives and ideas. And yes.

Silvija: very cool. If people are to remember one thing from our conversation what would you like to be?

Jenifer: Yes I would say that is that people. I would say that people remember how language has strong power. Language can affect a person to feel part of the company and to feel part of a group but as well can make this person feel not part of that group. So I would like the people to remember and think maybe twice what they say because he's gonna impact the person who they have next to them.

Silvija: I think that's very important to remember. I think we all know how our thoughts affect what we say but I think also what we say affects what we and others think and we forget that sometimes. Jenifer Clausell Tormos. Thank you so much for coming here with us in Lorn.tech and helping us remember the power of language.

Jenifer: Thank you very much. What a pleasure.

Silvija: Thank you for listening.