Tackling the AI bias problem at the origin: Training data  

5 February 2021:

Ethical AI is a top priority for business leaders, but given all the hype about AI, it's easy for non-data scientists to focus on potential opportunities without giving adequate thought to its risks. As the use of AI continues to expand, organizations need to shift focus to ensure trustworthy, ethical outcomes.

Organizations are increasingly becoming responsible for harm reduction in their software -- in their own development, or through software. Algorithmic (data) bias is one of the main drivers of ethical AI discussions because the presence of bias in AI systems negatively impacts consumers, customers and brands.

As data scientists explore data bias affecting real-world AI applications, it's never been timelier to solve the AI bias problem.

Types of bias

AI bias comes in many different forms. Some are human cognitive biases and others are data-related biases. Some of the most common forms are not necessarily mutually exclusive - enterprises need to carefully examine internal and external biases in their data sets as well as their AI applications.

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Source: TechTarget

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