Aegis-GPU5.4 Reducing bias
Where unwanted bias comes from and how to reduce it.
Key points
AI models are often trained on data limited in size, scope and diversity. NVIDIA says reducing unwanted bias is important so all people and communities can benefit.
What NVIDIA says (2)
“AI models are trained by humans, often using data that is limited by size, scope and diversity.”
“To ensure that all people and communities have the opportunity to benefit from this technology, it’s important to reduce unwanted bias in AI systems.”
NVIDIA says trustworthy AI developers look for clues and patterns that suggest an algorithm is discriminatory, and that synthetic datasets are one way to reduce unwanted bias in training data.
What NVIDIA says (2)
“trustworthy AI developers mitigate potential unwanted bias by looking for clues and patterns that suggest an algorithm is discriminatory”
“Synthetic datasets offer one solution to reduce unwanted bias in training data”
Key terms
- Unwanted bias: Systematic unfairness in a model, often from data limited in size, scope or diversity.
Sample question
Why is it important to reduce unwanted bias in AI systems, and where does bias often come from?
Show the answer
Answer: Models are often trained on data limited in size, scope and diversity; reducing bias lets all communities benefit
AI models are often trained on data limited in size, scope and diversity. NVIDIA says reducing unwanted bias is important so all people and communities can benefit.
What NVIDIA says (2)
“AI models are trained by humans, often using data that is limited by size, scope and diversity.”
“To ensure that all people and communities have the opportunity to benefit from this technology, it’s important to reduce unwanted bias in AI systems.”