Course Summary

1. Course Summary

You've made it to the end of this course on introduction to responsible AI and AI fairness and bias. Let’s recap what you have learned. In this course, we introduced responsible AI and AI principles, which lead to developing successful AI. You learned about Google’s 3 AI principles: Bold innovation, Responsible development and deployment, and collaborative progress, together. You also learned why AI fairness and bias is important for machine learning and why it is challenging to achieve. We've identified a few tools that can help you identify fairness and bias in AI. For example, use the TensorFlow data validation to identify bias in data, and use What-if Tool and TensorFlow Model Analysis to identify bias in model. Additionally, you've learned about techniques that help mitigate bias. For example, techniques such as the refining data collection pipeline, balancing data, augmenting with other data, and relabeling data techniques all help mitigate bias in data and threshold calibration. MinDiff and Counterfactual Logit Pairing help mitigate bias in models. As artificial intelligence continues its rapid ascent, the conversation around responsible AI becomes ever more vital. New technological developments constantly present fresh challenges and opportunities in this domain. It's even more important now to ensure that when you develop for AI, you are equipped with the latest insights and best practices for responsible AI implementation.

2. Let's practice!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.