Eliminate Bias Through Automation and AI Video Transcription


AI systems are at danger of making biased decisions. When using historical outcomes to build models, some of the inherent biases in previous decisions may creep through into future decision making. Simply eliminating variables about race, gender, socioeconomics or geodemographics, isn’t enough to eliminate those biases.

Sources of Bias in Models


A second source of bias comes when historically there are underrepresented groups in your business and the models haven’t seen those groups in current decision making. You may also create bias inadvertently through sampling and great care must be taken when training models.

Using the Right Technology Eliminates Bias


A way to eliminate this is in the technology you use. There’s no bias inherent to the laws of physics. At Daisy, we’ve created the laws of insurance and use reinforcement learning and fuzzy logic, which creates explainability and eliminates bias in decision making. At Daisy, we see what others don’t.


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