Mitek on AI, women and how to eliminate bias in the industry

Mitek’s Chief Marketing Officer, Cindy White explains how bias in AI can impact women and how education is the key to solving this problem

Tell me about Mitek, your role and your responsibilities there?

I am Mitek’s Chief Marketing Officer, responsible for leading the company’s global marketing, brand, communications, product marketing, customer acquisition and partner programs.

Before joining Mitek, I was Vice President of Marketing at FICO, where I developed a deep interest and expertise in fraud prevention. I have spent a total of 20 years in technology, most of that time at Microsoft.

At Mitek, we are passionate about enabling equal digital access for all good customers.  As our technologies allow organisations to verify an individual’s identity during digital transactions to limit the impact of fraudsters, we are conscious that it’s also about unbiased access for honest users too. Regardless of gender, race, class or any other factor, we should all be able to enjoy a seamless entry to any digital experiences today.

How do you think AI impacts women?

First, we must note that technologies are not capable of intrinsic bias. However, the design process behind the technology could introduce discrimination, and this is how it can impact women.

AI algorithms that display unintentional bias against women can prevent them from access to the same digital experiences as men. Many women end up being denied access to digital services for daily functions. 

The odds of seamless access to digital experience are, at the moment, stacked in the favour of white men. This shouldn’t be accepted by any organisation using these technologies. Why should some identities work better than others? 

If we continue at the same course and speed, software companies will unintentionally support biases against gender equality.

Cindy White

How do you think we can create a more fair and equal playing ground in how AI technologies impact women?

The first step is to educate ourselves on the topic so we can push for change and accountability. 

As I’ve mentioned, biometric technologies are not actually biased as they are not making any decisions based on human values.

Bias and inequality in biometric technologies are caused by a lack of diverse demographic data, bugs, and inconsistencies in the algorithms. As a result, the responsibility to be inclusive lies with the technology leaders driving this innovation to train AI better.

It is also critical to have a diverse team during the design process. The range of background, experiences, and cultural knowledge keeps biases in check.

This sort of innovation is hard work, but it will pay off. When organisations can explain how a decision was made, they can better respond to customer issues and continue to eliminate bias. 

How have your own experiences as a woman impacted your perspective on AI and the industry?

My experience in the tech industry has deepened my resolve to push for equity within my team, the business and even in the technology we build.

Women have shown they can strive in this industry time and time again. Take the UK’s female codebreakers in WWII, and similar tech achievements in wartime by women in the US. As women, we should be confident to step out of the shadows and be bold with our capabilities and talents.

But this is much more than an individual venture. Businesses should consider that diversity is the key to being a success story.

When it comes to diversity in identity technologies, there is also an urgent business need. Gartner recently found that an overwhelming majority of companies see minimising bias and discrimination as a key driver behind their selection of identity technologies. 

This means solutions deployed by industries must enable inclusive access – free from bias and discrimination – whether geographic or biometric. We need to create technology solutions that grant digital access to everyone, as well as work with governments and policymakers to make equal access a reality.

What more can be done in the industry to empower women?

I know change is possible so long as people are aware, empathetic, and committed to the cause. A recent Hackathon at Mitek proved just that – a colleague uncovered an unintended bias in one of our algorithms and flagged it, allowing us to fix it instantly. I will share four ways we can all work together to create change.

Recruit young women to study technology in university: Research has shown that in junior roles, there are four times the number of women than men. Women make up only 12% of engineering jobs at Silicon Valley and there has been only a 2% increase in female software engineer hires in the last 20 years.

Look inside: As we are seeking women to join our teams, we need to know that the job doesn’t stop there. No matter the size of our organisation, we need to scrub our business practices to root out bias and evaluate the current state of our remuneration.

Speak up for and promote women: Changing these statistics requires leaders to be intentional about practices and programs at their company and to proactively monitor the progress and performance of women even if that means “speaking out for them” and thoughtfully promoting them.

Do what’s right: #BreakTheBias isn’t just this year’s catchphrase. We can all play a role in attracting and retaining women in our industry. We all benefit from women’s contributions to eliminate AI bias.


Featured Articles

Alteryx Industry-First AI Copilot Sees New Era of Analytics

Alteryx unveils AiDIN Copilot, the first AI assistant that chats with users to build data analysis workflows

Tamr’s Anthony Deighton: Integrating AI into Enterprise Data

AI Magazine speaks with Anthony Deighton, General Manager of Data Products at Tamr, about the power of AI and how it can be harnessed to transform data

IBM and Tech Mahindra Seek to Power Up Gen AI Adoption

Both tech giants are partnering to harness the power of IBM's watsonx to help enterprises accelerate Gen AI adoption and offer new governance capabilities

NASA's First Chief AI Officer Shows AI's Value Cross Sector

AI Strategy

OpenAI: A Look at the AI Trailblazer’s Leadership Landscape

AI Strategy

Who Are Microsoft's LLM Contemporaries?

Machine Learning