Interpretable AI or How I Learned to Stop Worrying and Trust AI


With breakthroughs in areas such as image recognition, natural language understanding and board games, AI and machine learning are revolutionizing various industries such as healthcare, manufacturing and finance. As complex machine learning models are being deployed into production, the understanding of them is becoming very important. The lack of a deep understanding can result in models propagating bias and we’ve seen examples of this in criminal justice, politics, retail, facial recognition and language understanding. Explaining or interpreting AI is a hot topic in research and the industry, as modern machine learning algorithms are black boxes and nobody really understands how they work. Moreover, there is EU regulation now to explain AI under the GDPR “right to explanation”. Interpretable AI is therefore a very important topic for AI practitioners. In this talk, I will give an overview of a few state-of-the-art interpretability techniques and how you could build explainable AI systems.


Ajay Thampi is a machine learning engineer at Meta where he works on large recommender systems, responsible AI and fairness. He holds a PhD and his research was focused on signal processing and machine learning. He has published papers at leading conferences and journals on reinforcement learning, convex optimization, and classical machine learning techniques applied to 5G cellular networks.

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