Humans are inherently biased
Humans are hindered by their unconscious assumptions and their inability to process huge amounts of information.
Avoiding bias makes good business sense
Even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.
AI can expose bias
By exposing a bias, artificial intelligence (AI) helps us lessen the impact of that bias on our decisions and actions. It enables us to make decisions that reflect objective data instead of untested assumptions; it reveals imbalances; and it alerts people to their cognitive blind spots so they can make more accurate, unbiased decisions.
How does AI do it?
Imagine that a major company wants more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.
Even AI needs checks and balances
Humans build AI models and feed them the data, so it’s important to have checks and balances to ensure that AI decisions are bias free. For example, financial services companies have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups.
Give AI specific tasks
AI does best when it is not asked to parse generalities. For example, there’s no one-size-fits-all definition of the best software engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.
Don’t set and forget
To remain both accurate and relevant, AI has to be continually trained to account for changes in the market, your company’s needs, and the data itself. For example, if you’re building models to analyze social media posts or conversational language input, you have to make a deliberate effort to include and correct for slang and nonstandard dialects.
Use AI to evaluate AI
You may want to develop a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, and Y is redefining job responsibilities to provide greater flexibility.
Bias in AI could create a backlash
IDC predicts that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology.
Source: “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions” (IDC, 2016)
Avoid a backlash through transparency
IDC also predicts that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.
To learn more about how to use AI to help avoid bias in decision making read the feature story How AI Can End Bias.