Responsible AIThe critical role of impact sourcing on AI model expansion
Addressing bias in AI models is a key principle of responsible AI. Discover how diversifying your team of AI contributors through impact sourcing can help.
Responsible AIThe shift to responsible AI and best practices for implementation
Given its immense influence in shaping our experiences, it’s increasingly important that AI is developed ethically and responsibly. Discover leading responsible AI principles and how they can be implemented.
Responsible AIClosing the gender data gap in AI
What is the gender data gap in artificial intelligence (AI) and what can companies do to ensure more inclusive AI data practices? Discover key insights and learnings.
Responsible AIBuilding technology for an inclusive future: A conversation with the founder of WAYE
With the development of next-generation technology comes the need to build for inclusivity. Learn about the future of work, technology and more from our conversation with Sinead Bovell, founder of WAYE.
Responsible AIHow to mitigate bias in artificial intelligence models
If you train your model with biased data, it will learn and even amplify those biases. Here’s how to address bias in artificial intelligence models.
Responsible AISeven types of data bias in machine learning
Discover the seven most common types of data bias in machine learning to help you analyze and understand where it happens in order to avoid it.
Responsible AIThe importance of controlling for bias in AI
As AI plays an increasingly larger role in customer service, it has the potential to bake in underlying human biases. Discover key strategies for controlling machine bias in your AI-enabled tools.
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