Wisdom From The Women Leading The AI Industry, With Laura Stoddart of Experian DataLabs

I’d say the lack of diversity in teams writing code would be my top concern in this regard. The assumptions we make as programmers are based on our lived experiences. If people writing the code only reflect a narrow part of society how can we truly best serve everyone?

As part of my series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Laura Stoddart.

Laura Stoddart is a Data Scientist at Experian DataLabs. Laura is a physicist turned data scientist who works at the Experian DataLab in London. Her recent work includes ethical AI, and using emerging datasets to evaluate risk. Laura also volunteers her data science skills to good causes such as Bankuet and enjoys helping others through mentoring. In her spare time she likes to propagate plants and cycle.

Thank you so much for doing this with us! Can you share with us the backstory of how you decided to pursue this career path?

I was interested in science and mathematics at school and wanted a career in one of those fields. I studied physics at University, and I did a master’s in particle physics, but it was during my final year when I started to learn about machine learning and the impact it was having on the world. I found it quite exciting, especially how quickly it can make a difference. A new algorithm comes out and in one or two years it’s been used in many different applications, in many different companies. Whereas, in academic research it’s a bit slower, so the research can sometimes be over hundreds of years. For me, that was my moment of ‘oh wow, this is something that’s progressing at such a fast pace, it’d be really exciting to get involved’.

I originally joined Experian as a data analyst, held that position for a year and a half, then I asked about the DataLabs and machine learning work through an internal presentation. As the research and development arm of the company, Experian DataLabs is continually addressing the disruptive forces within the market that elicit the fusion of raw data and artificial intelligence.

I approached the team and said something like, ‘Hey, I saw your work. I really liked it. Is there any way I could come and meet the data scientists in the lab?’ About a month later, the team offered me a job. I now get to work with world-class scientists from the most prestigious schools in the world.

What lessons can others learn from your story?

For me, I don’t look for positions that already exist because I believe that’s limiting your scope. A lot of the achievements in my professional life are because I’ve been a bit bold, and I’ve pursued an interest or goal, even if it’s not offered. I believe you should take advantage of learning within your own company.

I would also say, pursue your interests outside of your career and see what jobs or positions you can craft your interests into. I learned about AI and ML through Academia, leading to a personal interest, and it all came together for me when I saw it applied in the DataLabs.

Can you tell our readers about the most interesting projects you are working on now?

In the DataLabs, I’m currently a junior data scientist. I’m involved in the programming on the prototyping and on the creating of models. We are focused on breakthrough experiments with data to create a better tomorrow for our clients and consumers.

My role involves thinking about what algorithms can be used and in which way. For example, if an algorithm has been approved we ask how quickly we can implement it into a product. We’ll create a model; we’ll collate our results and then we’ll get to see the reactions to finding a solution to the problem.

None of us can achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

Growing up, I spent a lot of time doing DIY projects with my grandad. I did the typically ‘masculine’ things such as fixing things and building furniture. I think that doing this kind of thing at a young age helped me build essential skills for learning mathematics and science at school and has shaped who I am today.

What are the 5 things that most excite you about the AI industry? Why?

First, I’d say the rapid changes in the industry. There is so much research going on in this field, both from academics and from the industry, they’re really married together in order to meet the fast-pace.

Second, I think when you talk about AI within a business or to clients, instantly you pique their interest.

Third, it’s becoming more widespread that AI and ML solutions are being used every day. For example, it is used when we use something such as Google maps or when we shop online.

Fourth, I would say AI is integrated into our everyday lives. For example, if you can create an algorithm that can help somebody get credit who previously couldn’t, you can have a real impact on the world.

So, fifth and finally, I’d say the most exciting thing is when data scientists can put our work in a real-life context. At Experian, we’re helping people be able to buy homes, cars or fund their children’s education. The things that we do and the impact we have, it can be life changing for people.

What are the 5 things that concern you about the AI industry? Why?

First, I’d say the lack of diversity in teams writing code would be my top concern in this regard. The assumptions we make as programmers are based on our lived experiences. If people writing the code only reflect a narrow part of society how can we truly best serve everyone?

Second, using algorithms blindly is another concern. For me, as a data scientist, it is important to understand the algorithms I use, to be able to investigate why issues that might arise are happening.

Third and related to that, is assuming or believing a dataset is a true representation of the real world. Data can be biased because it is collected by people! Are we creating products for everyone or just the people represented in our initial dataset?

A fourth and common concern is using models because they are “cool” rather than being the best for the job. Yes, data science can be really cool, but we always need to consider the best option for the end goal. This includes maintenance costs, ease of monitoring and longevity.

Lastly, not monitoring algorithms over time. We might choose the best algorithm for the job at the time of creation, but things can change as we have access to more data or patterns change over time. We should always be open to changing our approach as things progress.

As you know, there is an ongoing debate between prominent scientists, (personified as a debate between Elon Musk and Mark Zuckerberg,) about whether advanced AI has the future potential to pose a danger to humanity. What is your position about this?

What can be done to prevent such concerns from materializing? And what can be done to assure the public that there is nothing to be concerned about?

As data scientists we strive to not only understand the statistics behind the algorithms we are using but how they affect the end user. We explain what our models are doing and check our decisions are fair. We challenge each other to make sure that assumptions made in code or a product design do not negatively impact a certain group.

In order to make sure we ask the right questions, we ensure our teams are representative of society, and engage end-users to ask about their opinions and experiences.

How have you used your success to bring goodness to the world? Can you share a story?

A project that stands out since I’ve joined the team at DataLabs is a project centered on fairness of machine learning algorithms and decision-making. At Experian, we believe in providing fair and equitable assessment for consumers. It’s fulfilling to be part of a company that values this and recognizes the importance.

As you know, there are not that many women in your industry. Can you share 3 things that you would you advise to other women in the AI space to thrive?

First, I would say the first step is acknowledging the lack of diversity within the data science and programming industry. This presents objective issues in our programming. The decisions we make in our programming are based on assumptions and experience. Without a diverse worldview in writing code, models can miss people in the wider society.

Second, there’s a great book I’d recommend called The Invisible Women, which highlights how different parts of society can be discriminated against, and not just women. The book examines those groups that are just completely not thought about, not actively discriminated against, but just never thought of, because they are not in the data. When you’re collecting data, if you’re not collecting gender and only aggregated data, you’re not capturing the differences between people’s experiences.

Lastly, I went to an all-girls school and studied the “nerdy” subjects like mathematics and physics. I never saw that it was not “the norm” in society for girls to pursue these subjects until I went to University. Only a fifth of people in STEM subjects in my year were women. Changing this it will be a mixture of targeted outreach to girls and changing perceptions at schools.

Can you advise what is needed to engage more women into the AI industry? ​

Personally, I’m involved with a few different networking groups involving women in data. It’s inspiring to see not just data scientists, not just engineers, but data practitioners of all gender identities.

My university has a great alumni program where I mentor students interested in data science. They ask what my day-to-day is like, what projects I’m working on, programming languages and technical skills they should learn, and any advice on a potential career path. I had that when I was a student, so it’s fulfilling to advise the other way around.

I would say having a mentor is essential, especially someone more senior in the field. A good mentor doesn’t necessarily have to be somebody who you’re directly working with, it can be somebody from academia or a completely different company. It’s incredibly helpful to have an outside perspective.

Another opportunity that has not been fully realized, is with company job descriptions. In recruiting, companies need to ask themselves if the language appeals to one gender more than the other. The language used can have an impact in an applicant’s decisions. They can see certain character traits or qualifications or soft skills, and think, ‘Oh, that’s not for me. I’m not usually described in that way.’ Words or characteristics can deter certain gender identities from even applying. Companies can use software to eliminate this.

Engaging more women in the industry fosters the type of culture we have at Experian, where we can listen and learn, and the more diverse your team is, and the different opinions is what makes it engaging.

This culture of diversity and inclusion has enabled Experian to grow and evolve while remaining at the forefront of innovation.

What is your favorite “Life Lesson Quote”? Can you share a story of how that had relevance to your own life?

My favorite quote is “If not you, then who? If not now, when?” originally attributed to first-century Jewish scholar Hillel the Elder, and repurposed and referenced by many others through the years, including speeches by Presidents John F. Kennedy and Barack Obama. I apply this saying to my own life by putting limitations in context and reframing my perspective.

We can sometimes create barriers in our minds of what we can and cannot do, especially career wise. Lots of these doubts will be based on our experiences in the world, but why let these expectations limit us? A shift in perspective can free ourselves from our own limiting judgement.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

I imagine a world where people are free to pursue careers, observe hobbies and express their gender however they like, free of judgment from other people’s assumptions. If we could just be rather than being worried about what others might think.

This is one of the reasons why I enjoy working at Experian, the company has created an environment where people are comfortable bringing their authentic self to work, regardless of differences or backgrounds.