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The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. Numerous high-profile examples demonstrate the reality that AI is not a default “neutral” technology and can come to reflect or exacerbate bias encoded in human data. These risks undermine the underlying trust in AI and affect your organisation’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.
Are you ready to deliver fair, unbiased, and trustworthy AI?
Download this guide to find out:
- How to build an end-to-end process of identifying, investigating, and mitigating bias in AI
- How to choose the appropriate fairness and bias metrics to prioritise for your machine learning models
- How to successfully navigate the bias versus accuracy trade-off for final model selection and much more
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