“With great power comes great responsibility.” It was true for Peter Parker in the Spider-Man of the early 2000’s, and it’s true for the AI/ML industry today.
Advances in AI are changing how we deliver healthcare services, how companies recruit and hire, how we shop online, how we police and administer justice, and just about everything in between. But the more we use AI to power and automate crucial parts of our daily lives, the more we need to be able to trust that these models are accurate, equitable, and high-performing.
That’s why we’ve created a deep dive piece that explores the four major categories of bias that can affect AI/ML models - Responsible AI: How to Mitigate Bias in Your Training Data
In this piece we:
- distinguish between the four types of bias,
- explain the difference between bias in your training data versus bias in your algorithm,
- sketch out the basic ways that experienced ML/AI teams work to mitigate these potential sources of bias.
Stories of bias unleashed at scale and models gone wrong dominate much of the media’s coverage of our industry. We have a collective responsibility to tackle issues of bias at the outset of any project, thinking through the nuances and consequences of decisions throughout the process of model development
At Alegion, we are deeply committed to responsible AI, and we’ve helped hundreds of customers tackle issues of bias in their training data developmentReach out to us today and request a demo now to learn how we can help you avoid bias in your training data