In the wake of recent high-profile AI bias scandals, companies have begun to realize that they need to rethink their AI strategy to include not just AI Fairness, but also Algorithmic Fairness more broadly as a fundamental tenet. At the Pragmatic Institute, we educate Fortune 500 companies about data science and AI. Through our work, we’ve discovered that many companies struggle to form a clear definition of algorithmic fairness for their organization. Without a clear definition, well-meaning fairness initiatives languish in the realm of good intentions and never arrive at meaningful impact. But defining fairness is not as easy as it may seem. Two examples highlight just how challenging this can be.
Are Your Algorithms Upholding Your Standards of Fairness?
A look at what happens when they don’t.
November 05, 2019
Summary.
The need to embrace fair AI has made it into executive suites and boardrooms alike. With recent high-profile AI bias scandals, companies have begun to realize that they need to rethink their AI strategy to include not just AI Fairness, but also Algorithmic Fairness more broadly as a fundamental tenet. But many companies struggle to form a clear definition of algorithmic fairness for their organization. Without a clear definition, well-meaning fairness initiatives languish in the realm of good intentions and never arrive at meaningful impact. But defining fairness is not as easy as it may seem. And the failure of the leaders to understand the fairness implications of AI or algorithms can have dire consequences.
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New!
HBR Learning
Ethics at Work Course
Accelerate your career with Harvard ManageMentor®. HBR Learning’s online leadership training helps you hone your skills with courses like Ethics at Work. Earn badges to share on LinkedIn and your resume. Access more than 40 courses trusted by Fortune 500 companies.
Avoid integrity traps in the workplace.