The critical need for diversity for women behind data analyticsBy Industry Contributor 8 March 2022 | Categories: news
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8 March marks International Women’s Day which celebrates the social, economic cultural and political achievements of women and also calls for accelerating women’s equality. With this year’s theme being Break the Bias, FICO Vice President of Global Analytics Delivery Louise Lunn discusses how we can break the bias in artificial intelligence by bringing about diversity in data analytics.
The United Nations International Day of Women and Girls in Science celebrated on the 11th of February threw a spotlight on achieving full and equal access and participation for women and girls in science, citing the importance of this goal in global development. The UN has highlighted that over the past decades the global community has made great strides in inspiring and engaging women and girls in science, yet there is still much work to be done.
This is the case in financial services as much as many other sectors. One critical area is artificial intelligence (AI) and how it affects financial decisioning.
There’s no contesting the far-reaching growth of AI. From loan applications to fraud prevention, it and machine learning are entrenched in our lives and has a say in the important decisions we make as well as those that are made for us. To make fair and accurate assessments, AI software needs to be reflective of the people it scrutinises and the best way to achieve this is to have a diverse team at work.
Of course, gone are the days of gender discrimination in financial decisions – it is mandated that risk cannot be measured based on gender. But to achieve the equality that is expected of financial services providers, it is crucial to make it easier for girls and women to enter the sector and further their careers, because one of the real challenges in AI is fighting the bias that can be coded into the models themselves.
All AI models are trained on datasets, and these datasets frequently have coded into them a level of bias. In fact, FICO Chief Analytics Officer Scott Zoldi says, “All data is biased.” It’s up to the data scientists to correct for this, and that is why it is so important to achieve more diverse teams building AI.
Recognising that we need diversity in innovation and teams is the first step. In many cases, AI learns from data generated by human actions. Left unchecked by data scientists, algorithms can mimic our biases, conscious or not. However, we can mitigate those biases by including people across race, gender, sexual orientation, age, and economic conditions to challenge our own thinking views. By bringing in people with different thoughts and approaches to our own, analytics teams will see a quick improvement in their code.
For any girl or woman thinking about data science as a career route, the opportunities are immense. Data scientists are a new breed of analytical experts, responsible for collecting, analysing, and interpreting extremely large amounts of data. These roles are an offshoot of several traditional technical roles, including business domain expertise, mathematicians, scientists, statisticians, and computer professionals. All these different jobs fit into the disciplines of a data scientist.
The insights that data scientists uncover should be used to drive business decisions and take actions intended to achieve business goals. While executives are smart individuals, they may not be well-versed in all the tools, techniques, and algorithms available to a data scientist (e.g., statistical analysis, machine learning, artificial intelligence, and so on). Part of the data scientist’s role is to translate business needs into algorithms.
The magic is also in the data scientist’s ability to deliver the results in an understandable, compelling, and insightful way, while using appropriate language and jargon level for their audience. In addition, results should always be related back to the business goals that spawned the project in the first place.
I would argue that if you accomplish diversity in your teams, you’ll make improved AI because your teams will be better at spotting bias and correcting for it. Different backgrounds drive more creative thinking, and more diverse teams tend to improve a company’s ability to solve problems. That’s just as true in data science as it is in other fields.
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