Having been a part of the technology and IT industries for several years, I have witnessed constant change and advancement in these sectors. Accordingly, in recent years, the prominence of data, machine learning and AI-based applications has become commonplace in several workplaces worldwide, and understandably so. The level of speed, automation, and finesse AI and machine learning bring to the business operations of any organization are simply unparalleled. I have always been a strong proponent of machine learning and AI to make business functions error-free and efficient.
However, the rise of machine learning in modern corporations also accompanies another trend – algorithmic bias.
For example, facial recognition systems used for providing access to employees on company devices or offices failing to identify individuals from certain ethnicities and backgrounds. Or an AI-based hiring tool solely approving the CVs of male applicants and rejecting the female ones in a recruitment drive conducted by one of the major organizations in the world. There are countless kinds of AI bias that, in my opinion, may defeat the purpose of having the technology incorporated in the first place.
So, how can your business resolve this problem? The answers are diversity and inclusivity.
As you probably know, machine learning algorithms perfectly represent the phrase, ‘garbage in; garbage out.’ The speed and efficiency of such algorithms reflect the quality of datasets used to train them. So, while incorporating AI-based solutions into their workplace, or even otherwise, businesses can ensure that their personnel includes people from as many ethnic or gender-based backgrounds as possible. This would help them in two ways:
Firstly, several studies have shown the correlation between diversity in personnel and business performance. According to a McKinsey study, ethnically and gender-diverse companies are nearly 40% more likely to perform better than their counterparts. Similarly, other studies have also found that diverse teams in a workplace can outperform their rivals by up to 80%.
So, from a productivity angle, having a diverse workforce and an inclusivity-driven culture can drive the overall growth of your business. From an algorithmic perspective, diversity in the test subject pool translates into greater diversity in the visual and audio-based datasets used for training machine learning algorithms.
How do inclusivity and diversity in recruitment augment a company’s AI implementation? Quite simply, having a diverse pool of subjects used for AI model training enriches the machine learning algorithms. As diversity in the workplace is a precursor, companies need to have a diverse workforce to reduce bias in their AI-based tools and applications in the long run. This can only be ensured by emphasizing inclusivity and diversity while an organization hires new employees.
Now, algorithmic bias may not entirely be eliminated in one go. Therefore, businesses need to embrace an inclusivity-driven culture in the long term to rid their workplaces of machine learning-driven discrimination once and for all.
In my opinion, one thing is a certainty: your organization will feature AI and machine learning-based tools sooner rather than later.
So, are diversity and inclusivity the basis of your company’s AI implementation policies too? Or will you follow a different approach for the purpose? Whatever your opinion, kindly share them in the as comments on my post.