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How to detect algorithmic bias in production
Sometimes, algorithms look biased but might not be, and other times algorithms look unbiased but may be - how can you tell?
- AI Ethics
- Experimentation
- Machine Learning
- Artificial Intelligence
Sometimes, algorithms look biased but might not be, and other times algorithms look unbiased but may be - how can you tell?
We examine how experimentation platforms used by search engine and social media companies have been designed to handle a range of experimentation issues that occur at scale.
Reinforcement learning, supervised learning, contextual bandits, active learning – how do they all fit together to make better customer outcomes?
Clear and unbiased analysis methods are critical to understanding the experiments that businesses run in-market on real users.
Uplift models are used to identify customers who are most likely to respond positively as a result of receiving some intervention.
You have set strategic goals for AI in your organisation, but how do you turn a strategy into a successful implementation plan?
Why experimentation is required for a good return on your data investment.
Here we introduce Atmosphere – Ambiata’s new continuous intelligence service – and explain how it fits into our renewed values and purpose.