The conversations around AI bias often emphasize the importance of eliminating bias, but often do not talk about how biases are introduced to AI and ML systems. In Riya Pahuja’s recent article in CIO, she emphasizes the importance of understanding how biases could impact and influence algorithms. AI is designed by humans, so they are inherently biased. Often unintentionally, designers of AI systems imprint their own biases on the algorithm.
“One of the most critical elements in being able to successfully scale AI is to ensure that it performs reliably as expected, which means addressing algorithmic and data bias as part of a holistic AI strategy.” Therefore, to properly regulate and monitor AI systems, companies must be aware of the type of biases that may impact their algorithms’ performances and have a plan to address these abnormalities. Through this process, consumers and organizations have the opportunity to build greater confidence in AI and ML systems.