This session will explore a variety of considerations that modern data scientists and data practitioners must account for when gathering and presenting data, including topics on bias, construct analysis, and machine learning. We'll discuss examples from history and headlines.
This is an important topic that lives at the crossroads of our careers, my wife's career in organizational psychology and human resources, my career in data, our work in civic non-profits, and our joint passion for history and civil rights. It's important to understand that when dealing with bias: outcomes matter, intentions don't. While many of our examples come from the historical context of the United States, not all, and we have added additional context for international audiences.
Our slidedeck and all citations and references will be made available for download here.
Register for the virtual event at 5pmCT here.
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