- Published on February 16, 2022
- In AI Features
Council Post: Explainable AI and its impact on creating a data-driven culture
Explainable AI (XAI) aims to address how black box decisions of AI systems are made.
Image by Council Post: Explainable AI and its impact on creating a data-driven culture
- By Anirban Nandi
Over the years, the AI & ML domain has evolved by leaps and bounds. Despite the progress, the AI/ML models suffer from a few challenges, including:
Lack of explainability and trust.Security, privacy, and ethical regulations.Bias in AI systems.
These challenges can make or break AI systems.
With the rapid evolution of ML, various metrics linked to accuracy have gained importance, calling for explainability AI (XAI). As shown in the scatter plot below, accuracy and the explainability of machine learning models are suspect. For instance, deep learning techniques are higher in accuracy, whereas decision trees are poor in terms of performance and good in explainability.
Enter explainable AI (XAI)
As the model gets complex, developers often fail to understa
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Anirban Nandi
With close to 15 years of professional experience, Anirban specialises in Data Sciences, Business Analytics, and Data Engineering, spanning various verticals of online and offline Retail and building analytics teams from the ground up. Following his Masters from JNU in Economics, Anirban started his career at Target and spent more than eight years developing in-house products like Customer Personalisation, Recommendation Systems, and Search Engine Classifiers. Post Target, Anirban became one of the founding members at Data Labs (Landmark Group) and spent more than 4.5 years building the onshore and offshore team of ~100 members working on Assortment, Inventory, Pricing, Marketing, eCommerce and Customer analytics solutions.
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