As the AI field is overwhelmingly white and male, another way to reduce the risk of bias and to create more inclusive experiences is to ensure the team building the AI system is diverse (for example, with regard to gender, race, education, thinking process, disability status, skill set and problem framing approach). Many current AI tools for recruiting have flaws , but they can be addressed. This requires an organizational knowledge of and reviewing how, where and when AI algorithms are deployed. This diversity helps ensure that different perspectives are brought to bear on the models being created which can help reduce some demographic bias. A beauty of AI is that we can … At first glance, this should be a win-win. Opt for transparency and explainability Opt for transparency and explainability The top right (quadrant I) represents incorporation of AI that increases accuracy and reduces bias. AI can eliminate unconscious human bias. Raise awareness of where AI is being employed, and where it can help reduce bias. “The budding field of fair machine learning looks at ways of reducing bias in algorithmic decision making based on consumer data,” said Ver Steeg. Daphne Koller. This puts the onus on businesses creating and using it to become more accountable and operate carefully and with empathy. AI can learn both good and bad things, but it depends on what it's fed. 7 ways to reduce bias in conversational AI. Applications in Business and Beyond. While we know that as humans, we are prone to expressing unconscious biases when recruiting candidates for roles, the question arises as to how we might be able to eliminate such bias leveraging AI. Ideally, the AI engineers and data scientists come from a variety of nationalities, genders, ethnicities, professional experiences and academic backgrounds. 7 ways to reduce bias in conversational AI. Build a diverse team. 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