Build a diverse team. AI can learn both good and bad things, but it depends on what it's fed. At first glance, this should be a win-win. A beauty of AI is that we can … For example, you do a search for C.E.O. Opt for transparency and explainability How recruiting using AI reduces unconscious bias. AI can learn both good and bad things, but it depends on what it's fed. “A more speculative area involves research on using AI to generate content including attempts at books, music, art, games, and even recipes. Raise awareness of where AI is being employed, and where it can help reduce bias. This puts the onus on businesses creating and using it to become more accountable and operate carefully and with empathy. on Google Images, and up come … Opt for transparency and explainability 7 ways to reduce bias in conversational AI. 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. “The budding field of fair machine learning looks at ways of reducing bias in algorithmic decision making based on consumer data,” said Ver Steeg. You could mean bias in the sense of racial bias, gender bias. 1. Applications in Business and Beyond. This diversity helps ensure that different perspectives are brought to bear on the models being created which can help reduce some demographic bias. Daphne Koller. 7 ways to reduce bias in conversational AI. This puts the onus on businesses creating and using it to become more accountable and operate carefully and with empathy. AI can eliminate unconscious human bias. This requires an organizational knowledge of and reviewing how, where and when AI algorithms are deployed. Together, the findings provide strong evidence for the value of creating blind taste tests for AI systems, to reduce or remove bias and promote fairer decisions and outcomes across contexts. The top right (quadrant I) represents incorporation of AI that increases accuracy and reduces bias. 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). Ideally, the AI engineers and data scientists come from a variety of nationalities, genders, ethnicities, professional experiences and academic backgrounds. 1. Many current AI tools for recruiting have flaws , but they can be addressed. Diversity helps ensure that different perspectives are brought to bear on the models being created which help. 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