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Address the Spectrum of Bias in AI Across Organizations with Brandeis Marshall



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Bias is either categorized as a fixable error or a structural inequity. This either-or language should be reframed as a both-and situation. As knowledge construction workers and insight architects, we struggle with seeing and addressing spectrum of biases. The power and ease of scale of inequities in our digital systems affects the effectiveness of achieving business goals and maintaining client loyalty. During this talk, discover the "bias wheel" as a more practical guardrail to navigating this spectrum. We will also discuss the disparate impacts of bias, including questioning the trust of and trustworthiness in our data, algorithms, systems and platforms.
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