There has never been a better time for women to be part of the technology industry. Powered by advancements in computing and AI, there are now huge opportunities to solve interesting problems at a global scale, writes Louise Lunn, Vice President at Global Analytics Delivery, FICO.

The key thing for women looking to take the first step into analytics or for those looking to develop their roles is to try working in different areas within the field and seize any opportunity to acquire a new skill or programming language. Stay determined, work hard and never be afraid to voice your opinion.

Diversity and inclusion are important considerations in many fields, including data science.  A diverse team can bring a wide range of perspectives and experiences to problem-solving and decision-making, which can lead to more innovative and effective solutions.

Why is there often bias in AI?

AI, in its simplest form, is a program that is capable of performing a task that requires intelligence. It learns from data, and datasets are prone to bias, no matter how much they may appear to be balanced or universal. To counteract this bias, organisations need to have greater diversity in teams building AI.

As with any new operational branch, organisations need a team to manage how AI will be deployed, measured and managed. The businesses that are doing this in the right way are looking to build diverse teams.

These teams will make better AI-based models because they will be better at spotting potential bias, both in the data and in the results. Working with people from a wide range of backgrounds will also drive creative thinking and innovation.

Encouraging diversity in the field of data science can help to address the issue of underrepresentation that has historically affected women.  While progress has been made there is still a gender imbalance in a lot of science, technology, engineering, and maths (STEM) fields. While the difference in numbers is larger in engineering, we still need to get more women into AI and I believe this begins with education and raising awareness of AI at an early age, followed by employers fostering more inclusive work environments.

Communication and problem solving are key in AI

We also need to start conversations about the qualifications required to work in STEM-based positions. Women need to look at these courses but then also consider incorporating aspects of programming skills, statistics, big data technologies, and architecture framework.  Communication and problem solving are key in AI, as are hands-on opportunities. I did a placement unit at university and it provided me with great insight into the field and what I would need to work in it.

Organisations need to actively promote diversity and inclusion creating a more inclusive and equitable work environment for all employees.  Having an organisation that is representative of the diverse communities and populations it serves can be important for building trust and attracting new talent.

If the topic of AI excites you, I recommend joining networks on LinkedIn that explore AI’s application. Ted Talks are a great source of knowledge and inspiration, and there is so much available on social media. Those interested can follow industry leaders online and actively engage with this energetic and supportive community.

We need diverse teams to reflect our diverse population

The pandemic increased the financial services industry’s dependence on AI and it is essential we develop diverse AI teams and build responsible AI models. Organisations can make life-changing decisions for their customers, but to create AI code that can do this fairly, it must be written by a group that reflects the diversity of those lives.

A key ability required to be successful in analytics, and which demonstrates the need for diversity in the field, is intellectual curiosity. AI needs people who will challenge and change how things are done. And this is best achieved with a mix of vantage points. A group of people from the same demographic, who were brought up in similar ways, and attended a select group of universities, will ask the same questions and approach problems from the same angle. A diverse group will see the many ways to solve the problem, and work together to find the best solution, whether it is a hybrid of multiple opinions or a single viewpoint.

Creating new roles with AI

The deeper we go into analytics and machine learning, the more functions we can draw from AI and the more roles we have to fill. This will raise the competition for talent to a new level, especially when it comes to building a diverse team. Organisations across the world will happily battle for top recruits and as businesses become more global, they will need a strong team from diverse backgrounds to guide their analytical programmes.

There are already so many positions to fill in an analytics team.  These vary from data scientist, data architect, data analyst, to data engineer. And within these roles you’ll find a whole host of specialties, such as:

  • The Algorithm Guru – understands the variety of choices for the breadth of tasks.
  • The Architect – ensures that the infrastructure can manage large-scale datasets and ensure things run fast!  Strong computer science and software knowledge.
  • The Data Modeler – building the models.
  • The Deep Diver – analyse the data/models to extract key insights.
  • The Storyteller – is needed to articulate the insights (from the innovative analysis).
  • The Cat Herder – keeps everyone together and on track with where they should be.

I feel grateful to be in a position to attract and retain the stars of the future in analytics and software. I enjoy giving people the opportunity to grow and develop, and then watching them go on to achieve great things. My role allows me to cultivate engagement, meaning, and motivation with my team and clients as we solve problems through data and analytics.

AI is an exciting and important field to work in. Some of the world’s biggest problems will be fixed by AI. This is a succinct way of illustrating the need for diversity in AI. To solve the world’s big problems, the team must reflect the world, and not a select group of it. At FICO, we have women working in analytics throughout the company, and a network called Women @ FICO that supports women playing a bigger role in the major decisions made at the business.