When AI Goes Astray: Excessive-Profile Machine Studying Mishaps within the Actual World | by Kenneth Leung | Aug, 2023

A tour of notorious machine studying blunders and failures that caught the world’s consideration

Photograph by NEOM on Unsplash

The transformative potential of synthetic intelligence (AI) and machine studying has typically made headlines within the information, with loads of experiences on its constructive influence in numerous fields starting from healthcare to finance.

But, no expertise is resistant to missteps. Whereas the success tales paint an image of machine studying’s fantastic capabilities, it’s equally essential to spotlight its pitfalls to know the total spectrum of its influence.

On this article, we discover quite a few high-profile machine studying blunders in order that we will draw classes for extra knowledgeable implementations sooner or later.

Particularly, we’ll have a look at a noteworthy case from every of the next classes:

(1) Classic Machine Learning
(2) Computer Vision
(3) Forecasting
(4) Image Generation
(5) Natural Language Processing
(6) Recommendation Systems

A complete compilation of high-profile machine studying mishaps could be discovered within the following GitHub repo known as Failed-ML:


Amazon AI recruitment system: Amazon’s AI-powered automated recruitment system was canceled after proof of discrimination in opposition to feminine candidates.


Amazon developed an AI-powered recruitment software to determine high candidates from a decade’s price of resumes. Nonetheless, for the reason that tech business is predominantly male, the system exhibited biases in opposition to feminine candidates.

As an illustration, it began downgrading resumes containing the phrase “girls’s” or these from graduates of two women-only faculties whereas favoring sure phrases (e.g., ‘executed’) that appeared…

Uninterested in your Knowledge Engineering Function? | by Madison Schott | Aug, 2023

Time collection prediction with FNN-LSTM