Artificial Intelligence (AI) systems are programmed to follow business processes according to stringent guidelines. Unfortunately, if your programmers incorrectly create those guidelines, your AI can make mistakes or fail entirely. This is especially true with regards to bias. How your algorithms interact with human beings from different cultures, genders, sexualities, races, etc., will be dependent on how your team of AI experts builds out your system.
This is where a team of AI diversity experts comes into play. This group should work arm-in-arm with your security team, business process experts and developers to set strict protocols for every decision AI makes, every word it utters and every transaction it executes. The team (which itself should be staffed with employees from diverse backgrounds, not just technology) should test and retest the processes to ensure no bias is present. They should provide honest feedback to developers, who in turn should be willing to listen and make necessary adjustments.
Your AI diversity team should be as large or small as your deployment warrants. Hire a team large enough to provide comprehensive oversight across every audience your company touches internally and externally. Want to ensure your AI system doesn’t exert gender bias in its customer service role? Hire women to train how your system speaks and interacts. Want to guard against race-based bias by your virtual recruitment agent? Bring people of color onto the pilot phase of the implementation.
In this post we’ll examine three crucial business reasons for removing human bias (as much as possible) from your AI system.
Unhappy Users Make for Wasted Deployments
Your AI investment needs to be money well-spent – and in most cases, that will only be the case if your end users actually utilize the system. Unfortunately, if your AI system (however unintentionally on your company’s part) is racially insensitive, or for example only selects white candidates for executive roles, it’s a good bet that your intended audience will avoid using the platform entirely and be left feeling offended and aggrieved. Besides dealing with aggravated users, your company’s AI deployment will never achieve ROI, all but guaranteeing that additional AI investments will never come.
By being mindful of bias during AI deployments and taking action during development, your company makes it possible for people of all backgrounds to use the system without issue. This may require hiring for new roles (such as a diversity lead for your AI project) or re-assigning current employees pre-implementation. Although you may spend more time and resources prior to the project going live, you’ll be in the best situation to avoid the impacts of unintended or overlooked AI system bias.
Angry Customers Can Lead to Lost Business
Just one instance of bias generated by AI with a customer can damage your business. Imagine this nightmare scenario: A customer screen-grabs an ethnic slur or a sexist comment and posts it to social media. A media outlet finds the post and reports on it. Thousands of people share the report with their respective social networks. You’ve accidentally created a PR nightmare – and you’ve undoubtedly lost business.
Having a team of diverse employees testing your AI system can help prevent such a scenario from materializing. AI systems do not learn bad habits without humans programming those bad habits into them. Workers can bring their own specific experiences to an AI project, and can monitor and catch anything that would possibly offend end users.
Bias Typically Leads to Errors
Bias is an unfair disposition toward a specific entity – the key word being unfair. If it’s AI’s job to provide customer service, find candidates for a potential job opening, or to respond to IT issues, AI should be able to do these jobs without any unforeseen bias. Two identical requests should be handled in the order in which they were logged, not based on the race or gender of the user who requested it but on trained, proper procedures. An AI system that is influenced by unnecessary bias inevitably makes mistakes, overlooks possible resolutions and does your company and your customers a great disservice.
Conversely, there are instances in which an AI system should absolutely have some predispositions. For example: A high-urgency IT request should be prioritized over a low-urgency request. A candidate with decades of relevant experience should in many instances be granted an interview instead of a candidate with no or little experience. A customer ready to spend six figures on an online order should be a priority compared to a customer who simply wants to know what time a retail branch closes. These scenarios should be governed by business rules, rather than being driven by specific programmers’ preference toward an ethnicity, gender or sexuality.
By building out a diverse team of AI testers, you can help to remove bias from your AI deployments. Although this might mean a longer trial period and a larger pre-implementation team, the cost of removing bias from your deployment far outweighs the risks associated with failing to do so.