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Artificial intelligence will shape so much across business and culture — but what will its effects be on the workforce? Much of the backlash against AI is not because of what it can do but rather how it might affect the livelihoods of workers and previously disadvantaged groups, widening the inequality gap even further.
This is fair enough, but ultimately AI is simply a reflection of the humans and data defining its behavior. The real task is ensuring that AI isn’t riddled with cultural, gender, and racial bias. Thus, with an appropriate dataset to direct, socialize, and guide AI, it can be a useful, unbiased automation tool for any organization.
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AI in Diversity and Inclusion
To get to the bottom of where AI plays a significant role in defining the future workplace, it’s critical to look at what companies are doing around diversity and inclusion and check if it’s being translated back into AI modeling.
Businesses strive to create an inclusive workforce thriving on diversity. According to McKinsey, companies that thrive on diversity and have an inclusive culture outperform their competitors by an average of 35%. Moreover, a firm stance on diversity and inclusion can result in improved employee performance and job satisfaction, which can lead to year-over-year revenue growth and profitability.
The last two years’ remote and work-from-home models have shown the intersection between diversity and inclusion and AI and automation. Companies need to now look at how work gets done—not just the people doing it. While AI can support a company with the automation of tasks, it does little to foster emotional connections.
When thinking about a hybrid and remote future of work, organizations need to be purposeful in their planning of AI and automation. If this planning is not calculated, they run the risk that AI will never factor in diversity. For example, in recruitment, if you are part of a sector dominated by white males, then AI will continue to scan for precisely that.
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Data Bias and Hiring
When using AI as a means of talent discovery, say scanning LinkedIn for candidates, and you haven’t built in the right thinking or acquisition model from the start, it will find the same types of people you already have. Data bias is a very real problem with AI, as it relies on the data it is fed or served.
When looking for new skills instead of experience in your sector, you need to inform or program AI to hunt for the exact skills required and not only rely on historical data to inform its decisions.
People of color and women have been fighting data bias for decades. For example, many AI models used to assess online loan applications and credit scores affect people of color and women the most due to algorithms based on systemic biases built into decade-long processes.
Therefore, you have to be purposeful in eliminating bias in your data and creating data models that understand that demographics such as skin color, age, gender, and more don’t affect a job application. These biases should be reviewed and removed continuously.
Automation and Workforce Upskilling
One of the benefits of AI is the automation of manual tasks and the freeing up of skilled professionals to perform more meaningful tasks. However, some people simply resist change and fear the erosion of job security, having to learn something new, or don’t believe they can reskill or upskill.
But business needs to evolve. We need to remind people about the benefits AI can bring, such as being able to predict certain events, making suggestions for alternative solutions to a customer situation, or managing regulatory and compliance checks. These benefits help employees better deliver on their job and significantly reduce or eliminate tasks prone to human error.
However, there is value and importance in the “human factor”; people-to-people connection is a fundamental need in everyone. Ultimately, AI and automation doesn’t replace people, it serves them, but people will only believe this if you help them understand how. To spearhead this understanding, you must include your people in your AI discussions, so they know they have some control over these decisions and help guide how you build and influence AI itself.
We Are Human, AI is Not
AI is not running itself, and it needs a skilled person to grow its intelligence, teach it about diversity, and fill its algorithm with best practices. You also need to perform regular quality and safety checks on its processes and build in warning flags for when it’s not behaving as programmed.
Humans can identify mistakes or see when an action is not fostering feelings of inclusivity. Quintessentially, when building responsible AI systems that understand diversity, you must create clear goals about what the AI and automation are expected to deliver to the business on which you can base and measure success.
Human influence, not just data, directs good AI behavior, and can quell the effects of historical data containing systemic bias. For AI to help with diversity and inclusion, it needs to help identify uncomfortable discussions and areas where critical analysis is crucial. This includes finding candidates in a hiring process that previous bias overlooked because of their color or gender but who have a specialized skill it would have taken humans months if not years to find.
AI and Business Transparency
Yes, AI will erode or negate some skills, so business leaders need to think and be transparent about the consequences using AI has on their people.
The incorporation of AI and automation can lead to work displacement, leaving many without the resources to acquire news skills. While the efficiencies of AI can elevate the economic status of a country and create opportunity for its people, engaging with teams early in the automation process helps employees transition appropriately.
Whether they decide they want to find a new niche in your organization or if they want to leave because they feel your business’s path is not suitable, it’s important to give employees the opportunity to make the decision about their career for themselves.
Balancing the Benefits
There is significant momentum in automation and AI, and it’s a market ready to explode. But the immediate challenge is leveraging AI so that it augments rather than replaces the human workforce and so it no longer perpetuates historical bias but identifies it.
Make no mistake, AI will never replace humans in their entirety; however, it is up to those who manage and control the AI to shape the changes they want to see in the workforce and automation.
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About the Author:
Louisa Gregory, Vice President Culture, Change and Diversity, Colt Technology Services