The transformative potential of artificial intelligence (AI) and machine learning has often made headlines in the news, with plenty of reports on its positive impact in diverse fields ranging from healthcare to finance.
Yet, no technology is immune to missteps. While the success stories paint a picture of machine learning’s wonderful capabilities, it is equally crucial to highlight its pitfalls to understand the full spectrum of its impact.
In this article, we explore numerous high-profile machine learning blunders so that we can draw lessons for more informed implementations in the future.
In particular, we will look at a noteworthy case from each of the following categories:
A comprehensive compilation of high-profile machine learning mishaps can be found in the following GitHub repo called Failed-ML:
Amazon AI recruitment system: Amazon’s AI-powered automated recruitment system was canceled after evidence of discrimination against female candidates.
Amazon developed an AI-powered recruitment tool to identify top candidates from a decade’s worth of resumes. However, since the tech industry is predominantly male, the system exhibited biases against female applicants.
For instance, it started downgrading resumes containing the word “women’s” or those from graduates of two women-only colleges while favoring certain terms (e.g., ‘executed’) that appeared…