Artificial intelligence (AI) has become so enmeshed in daily life that its presence is often invisible. AI technology drafts legal contracts, assesses job applicants, approves loan applications, detects financial fraud, and screens healthcare patients — the list of applications is endless and allows AI to be unbiased. Sam Babic, chief innovation officer at Hyland, discusses how open-source data science could help.
Like humans who performed certain tasks before technology was introduced, AI may execute them with a biased perspective.
Here’s why: AI algorithms “learn” by analyzing training datasets for predictable patterns and rules. But human biases are frequently baked into those datasets, even when engineers don’t intend to discriminate. And these biases — like a dataset that consists of a single or unrepresentative demographic — are usually not overt, making them challenging to correct. The result may be mortgage approval systems that deny one race over another, facial recognition tools that fail to identify people of color, and image generators that only display images of white men when asked to depict a CEO.
As companies increasingly rely on AI tools to automate, streamline, and speed up routine business functions, minimizing bias in AI has never been more important. One solution is open-source data science, built on the work of a global community of contributors, enabling solution providers to introduce less biased AI tools with speed, governance, and transparency.