kennethleungty/Failed-ML

Compilation of high-profile real-world examples of failed machine learning projects

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This compilation provides concrete examples of machine learning projects that failed in real-world scenarios. It takes descriptions of projects in various domains, such as recruitment, healthcare, and finance, and details the reasons for their failure, often due to biases or inaccuracies. Data scientists, product managers, and ethical AI researchers can use this resource to understand common pitfalls and develop more robust, equitable AI solutions.

751 stars. No commits in the last 6 months.

Use this if you are developing or deploying AI systems and want to proactively identify potential failure points and biases in your models.

Not ideal if you are looking for technical deep dives into model architectures or code-level debugging of machine learning failures.

AI ethics machine learning product management data science best practices AI risk management bias detection
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 15 / 25

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751

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50

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License

MIT

Last pushed

Jun 14, 2024

Commits (30d)

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