Diversity is Key to Eradicate Implicit Bias in AI Solutions
The age of digitalization is upon us. We find stories of the impact technological advances are having–and will have on our lives, our work, and our futures–everywhere. And truly, diversity is key to eradicate implicit bias in AI solutions and technology in general.
The term implicit bias refers to the process by which our brains notice patterns and make generalizations based on observations and experiences. We often refer to this process as stereotyping and our brains do this, unconsciously, all the time. For each of us, our unconscious, or implicit, biases play a role in how we understand the larger world around us.
But, are we considering the role implicit biases will play in the development of this technology?
“This tendency for stereotype-confirming thoughts to pass spontaneously through our minds is what psychologists call implicit bias. It sets people up to overgeneralize, sometimes leading to discrimination even when people feel they are being fair,” write Keith Payne, Laura Niemi, and John M. Doris, for Scientific American.
“We all bring unconscious biases into the workplace,” writes Laura Berger. “These deeply subconscious attitudes span race, gender, appearance, age, wealth and much more. They influence everything from the car you drive to the employee you promote and the one you don’t. And because they are so reflexively triggered without our knowledge, they are virtually unconcealable.”
So as we contemplate our futures, I find myself wondering about this. Who is monitoring the unconscious biases held by those developing the technological solutions to tomorrow’s societal problems?
If we are not already discussing this, we need to start. Today.
AI in L&D: Benefits and concerns
An example of our increasing reliance on technology in L&D is the use of Artificial Intelligence (AI). My colleague, Annika von Redwitz, and I are keenly aware of the stated benefits of using AI in learning and development. As we see it, the impact of AI on L&D has the potential to disrupt the delivery of corporate learning in the future.
However, while we envision much good to come out of this, we both share concerns about the implicit biases programmers may be imparting to the technology they develop for L&D.
Why? Learning and development are critical to any company’s success today. And, to be successful L&D must prepare leaders, train managers, inspire employees, develop great communicators, promote diversity, and ensure teams are high-performing. According to PwC, by the 2030’s, 38 percent of all U.S. jobs could be replaced by AI and automation.
“Many people say AI will get ‘smarter’ over time as it is used,” writes Annika in a recent article we co-authored for “Training Industry.” “Of course, this is true, but we need to make sure the recognition software doesn’t inhibit creativity or reinforce thinking patterns that may need to change – not unlike what can happen when internal trainers do all the training in organizations for their peers.”
“Unconscious bias is our tendency to make mental shortcuts,” said Natalie Johnson, a partner at Paradigm, a firm that helps companies with diversity and inclusion. “While these shortcuts are helpful–they enable us to make decisions quickly–they can be prone to error. They can especially be prone to error when making decisions about people.”
Research published by Infosys in 2017 shows AI is perceived as a long-term strategic priority for innovation, with 76 percent of the respondents citing AI as fundamental to the success of their organization’s strategy, and 64 percent believing that their organization’s future growth is dependent on large-scale AI adoption.
“Tech companies have made big advances in terms of building artificially intelligent software that gets smarter over time and potentially makes life and work easier,” writes Michelle Cheng, for Inc. “But these examples reveal an uncomfortable reality about A.I.: even the most intelligent bots can be biased.”
Ideally, thanks to digitalization, we will all have more time to focus on people and human interaction. But, we need to remember, that human beings are developing technology like AI, each with implicit biases that impact the solutions they design.
Not only is is essential that diverse teams (of humans) work well together to develop those algorithms–it is imperative that we continue discussing how to manage the potential for problems caused by stereotypes and unconscious biases.
A version of this post was first published on Inc.
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