Natalie Owen of Dialpad: “Bring your unique experiences to your work”

Bring your unique experiences to your work. In general, we will have different lived experiences than the average person in our industry so it is important to share your perspective when it is different from your male peers. This is especially true when it comes to how AI can be used in problematic ways that […]

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Bring your unique experiences to your work. In general, we will have different lived experiences than the average person in our industry so it is important to share your perspective when it is different from your male peers. This is especially true when it comes to how AI can be used in problematic ways that your peers may not have experienced or considered.

As part of our series about the women leading the Artificial Intelligence industry, I had the pleasure of interviewing Natlie Owen.

Natalie is the Senior Manager of the Automatic Speech Recognition and Vi Data teams at Dialpad, the industry-leading AI-powered communication and collaboration platform. Based in Kitchener-Waterloo, Natalie and her team focus on improving Dialpad’s proprietary Voice Intelligence (Vi™) engine which delivers real-time business optimization — from call coaching and automated note-taking to sentiment tracking and transcription analysis. As part of her work, Natalie is passionate about advancing the field of AI by removing barriers, improving processes, and educating others.

Thank you so much for joining us in this interview series! Can you share with us the ‘backstory” of how you decided to pursue this career path in AI?

So I actually kind of stumbled into AI. I’ve always been interested in human-computer interaction and how to make machines work for us and I feel like AI is an extension of that. So when I was working with startups to help them to mature their engineering practices and I got the opportunity to work with one in the AI space, I jumped at the chance. It was a really interesting experience to help the team bridge the gap between the scientific approach of ML work and the expectations of a more traditional engineering organization with regards to commitments, deadlines and setting expectations. I had to quickly learn what the team was doing and what were realistic expectations to set while also ensuring the team didn’t lose sight of the end product we wanted to deliver amongst the exciting scientific discoveries they were achieving.

What lessons can others learn from your story?

There isn’t only one path into the AI industry. Some are definitely easier and more straightforward than others but don’t lose hope if you started down a different path and want to make the switch.

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

As anyone who speaks a less common or non-American dialect can surely attest, transcription for different dialects tends to be far worse than for the average native-level English speaker in America. We really wanted to put in an effort to minimize that gap for our customers and try to provide as consistent an experience as possible; so my team is working on improving our transcription for different dialects and traditionally under-represented groups. We did some benchmarking and even companies with access to huge amounts of data tend to do significantly worse on speakers with dialects that are different from the majority of (US-based) users — which was really disappointing to find.

None of us are able to 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?

My tech lead at the time, now manager, Jonas Robertson. When I took over managing the ASR team at Dialpad, I knew very little about speech recognition. Jonas did a great job of helping me to quickly get up to speed with what the state-of-the-art techniques were, and enough about the internal workings that I could advise, help provide direction, and communicate realistic goals back up the chain. Thankfully at Dialpad, we have a great culture of learning and our team puts on weekly education sessions where people can share the latest thing they’ve learned with the team. It makes it easy to stay up to date with not only the latest research but also how we’ve implemented new ideas into our technology stack.

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

The sheer volume of possibilities. We’ve barely scraped the surface on what is possible with AI and as it gets more sophisticated I hope that it will have an immensely positive impact. For example, the medical applications to help improve diagnostics for individuals are still relatively new but showing great potential to improve the abilities of doctors.

It is not about replacing people either, but the ability of AI to make tasks more efficient. Ideally, this will free up time to focus on what really matters, help more people, and allow people to have more balance in their lives.

It really goes back to what got me into AI in the first place, the ability to make computers work better for us.

Another thing that excites me is that so many people in the AI industry care about the ethical implications of their work. It is extremely motivating to see so many people in the industry are not just aware of the ethical implications of their work, but really taking a stand to pressure companies to do the right things and speaking out when they see unethical practices.

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

My top concern would be all of the problematic ways in which AI can be used. AI being used to help silence dissidents or imprison those who disagree with political parties is a terrifying reality of our world today.

Unfortunately, AI often just reflects back existing prejudices and biases and there is an amazing lack of awareness even within the industry sometimes. We joke on our team that every few years another person invents their own version of phrenology (think systems that predict criminality based on their facial features) due largely to not realizing the data they started with is not objective but a result of the society we live in.

Far too often that data is collected in unethical ways and/or used in a manner that people do not expect or understand the consequences of. This leads to people unwittingly helping to build systems that they would not agree with if they fully understood. And it is a complex topic that may be difficult to understand especially once it is written in legalese and hidden deep in your terms and conditions.

Lastly, as with all of STEM I’m concerned with the lack of diversity in the AI industry and what that means for moving the needle on these concerns around bias and discrimination. If the people developing these systems have no experience of the effects of these biases it is a lot harder for most of them to care about introducing them into the systems they develop.

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?

I do think that advanced AI has the potential to pose a danger, but not in the “robots enslaving humanity” way that most people think of. I think there is a much greater risk (in that it is already happening) of corporations, governments, and those with the means, using AI to harm the general population.

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?

I think people need to demand to know when and how these systems are being used, what data is being collected and the biases innate within the systems. Regulation, like in the EU, is I think our best bet at combating this as any business in our capitalist society left to its own devices will exploit every advantage it can to make money. I think that an aware public is necessary to hold these groups accountable.

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

Within my current sphere of influence, I’m working with our legal team and a few others to put together ethics guidelines for how we will use AI within Dialpad and what things we won’t do. When working in the industry, it can be far too easy to focus on building what people are willing to buy and lose track of the potential implications or misuses of a feature. We’re really passionate about ensuring our customers are well informed about how their data is being used, have full control of their data at all times, and that our AI models are as fair and objective as possible.

I think one of the key steps in ensuring we’re doing the right thing has been in building an inclusive and diverse team at Dialpad. It was really important to me that while building our team that we ensured we hired people who know firsthand about some of the challenges of implementing fair and objective AI and giving them the space to share their thoughts and experiences to make suggestions on how we can improve both our internal processes and metrics as well as our customer-facing models.

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?

  1. If you think you’re being treated differently because you’re a woman in a male-dominated team, it is probably not in your head; find a team that respects you. Life is too short to work somewhere that doesn’t value you.
  2. Always be learning. The AI space is changing and growing constantly so you have to be open to learning new ways of doing things, new technologies, and how you can build things that will be most useful for your specific industry.
  3. Bring your unique experiences to your work. In general, we will have different lived experiences than the average person in our industry so it is important to share your perspective when it is different from your male peers. This is especially true when it comes to how AI can be used in problematic ways that your peers may not have experienced or considered.

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

I think this is a bigger societal issue that has no easy fix. We need to encourage women to pursue STEM fields if they show interest and as a society, we need to stop gendering things like math or computing ability. We also need to make these learning spaces more welcoming to women. It can feel very alienating being the only woman in a lecture hall, being told you only got accepted into a program due to your gender, or having to engage in a hyper-competitive environment.

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

It is not a specific quote but I’ve always believed that trust and respect are not earned but rather the default that you have the power to revoke. I want to assume the best in others and that people have good motivations. But to not be taken advantage of, I ensure that if someone shows me they cannot be trusted, I don’t keep trusting blindly.

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 wouldn’t really be starting it but I’d throw my weight behind the movement for universal basic income. So many people around the world are exploited because it is the only way for them to survive. I think if everyone started from a place of safety we’d have a lot more people working to do good in the world instead of making the rich richer.

How can our readers follow you on social media?

I’m not really active on social media but you can follow the Dialpad social media team to keep up with what my team is up to; they’re frequently posting new blogs on interesting topics in applied AI over at Dialpad on Linkedin and @DialpadHQ on Twitter.

This was very inspiring. Thank you so much for joining us!

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