Katie He has a wealth of experience as CEO of Speechmatics, growing companies and leading financial functions.
It is no secret that machine learning has significantly impacted enterprise technology in recent years, with recent technological steps helping to increase efficiency, improve data output and even save costs across many industry sectors.
With all of these amazing improvements to our business, we’ve also learned an important lesson in how bias (and what it does) can present itself through machine learning. For example, in speech recognition, in particular, recent study Conducted by Speechmatics shows that nearly 38% of those surveyed believe that there are a lot of sounds and dialects that current technology does not understand. Furthermore, nearly a third of respondents (29%) have a voice recognition bias.
In our world, where we celebrate diversity and prioritize equality and inclusion in the workplace, addressing the presence of unconscious bias in technology should be a major focus in the short term. Doing so will not only build flexibility in our organization’s technology but will also develop it in the workplace.
Since the introduction of artificial intelligence, the fact that machines are trained with a specific type of (limited) data means that despite technical breakthroughs, the beneficiaries of these new technologies have been limited to the specific profile represented in the datasets. Simply put, we need more diversity in training data, where (like humans) the more machine learning is exposed to different thinking, speaking, facial features, etc., the more accurate machine learning becomes at detecting diverse biometrics and insights.
This can certainly be quite a challenge, of course, because data often requires tagging and classification to reduce bias, which can be a daunting and costly task. But addressing this challenge head-on can help ensure that the technology we use in our business is compatible with our world today. Besides promoting diversity, equity, and inclusiveness in our business offerings, eliminating bias in our AI technology also means we are able to achieve a more resilient future with a more resilient enterprise set of technologies.
Imagine a scenario in which our technology is adaptable to the ever-changing world we live in, giving us the flexibility and freedom to pivot while global events shape the future of our business. Investing in eliminating bias now prepares machine learning technology for long-term success without the need to constantly adjust and adjust according to the use case.
In addition to creating a more flexible corporate technology stack, curbing unconscious bias also creates a more flexible workspace for companies developing technology. Tech companies that prioritize the challenge of curbing unconscious bias in AI software may find a more creative and innovative path forward, allowing advances in the field that can benefit the industry in the long run and lead to market-shaking breakthroughs while also proving their future. technology.
For example, recent technological advances in the use of self-supervised models in artificial intelligence and speech recognition show that when data constraints are lifted, speech recognition machine learning can train on massive amounts of “unlabeled” data, which completely changes the representation of sounds. .
Tech advances like these open up greater use cases for flexibility and adaptability to an ever-changing world, and companies that challenge their engineers to eliminate bias in all AI use cases will find themselves poised for long-term success. While challenging AI-based tech companies with a seemingly impossible task like this could mean more investment in the team and technology in the short term, the results speak for themselves when the technology is adaptable in whatever use cases our future holds.
While there is still more work to be done, the challenge of the engineers developing this technology to combat bias in AI technology has led to market penetration for our customers.
As pioneers in AI technology, ensuring a robust future for this industry is important, which means ensuring flexibility in what is brought to market for customers. Our world has changed drastically in the past few years and still presents new challenges and scenarios that no one can predict. We need to provide adaptability and flexibility in our technology to our customers, and challenging our machine learning industry by curbing unconscious bias is a way to move towards a more perfect technology.
The more we can adapt to the needs of our ever-changing world and solve the problems our customers face, the better off will be the enterprise technology and the companies behind it.