Bias is machine learning’s original sin. It’s embedded in machine learning’s essence: the system learns from data, and thus is prone to picking up the human biases that the data represents. For example, an ML hiring system trained on existing American employment is likely to “learn” that being a woman correlates poorly with being a CEO.
How Machine Learning Pushes Us to Define Fairness
The systems we build reveal what we value.
November 06, 2019
Summary.
Along with the growth of AI have come serious questions about our ability to build unbiased, “fair” algorithms. And it’s true that without intervention, machine learning algorithms will reflect any biases in the underlying data. But precisely because ML requires us to instruct it in highly precise ways about what sort of outcomes we’ll find ethically acceptable, it’s also giving us the tools to have these discussions in clearer and more productive ways. We’re defining a whole new vocabulary and set of concepts to talk about fairness.
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Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Digital Intelligence . Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Excel in a world that's being continually transformed by technology.