Across industries, businesses are expanding their use of artificial intelligence (AI) systems. AI isn't just for the tech giants like Meta and Google anymore; logistics firms leverage AI to streamline operations, advertisers use AI to target specific markets and even your online bank uses AI to power its automated customer service experience. For these companies, dealing with ethical risks and operational challenges related to AI is inevitable – but how should they prepare to face them? Poorly executed AI products can violate individual privacy and in the extreme, even weaken our social and political systems. In the U.S., an algorithm used to predict likelihood of future crime was revealed to be biased against Black Americans, reinforcing racial discriminatory practices in the criminal justice system.

With the growing technological advancements, it is now possible to create complex applications without spending huge amounts of money, or waiting for months and years, and employ multiple developers. The introduction of low-code and no-code platforms has made it possible to build applications integrated with advanced technologies. With the advent of these platforms, more and more businesses are looking to leverage their power to build AI products. This brings the visual drag-and-drop tools to the picture to help data scientists fill the void and make AI less intimidating for less technical people. In this article, we talk about the top low-code or no-code platforms to build AI products.

Artificial Intelligence (AI) and Machine Learning (ML) products are unique. They hold enormous power and are by definition constantly changing. Due to the level of sophistication involved, the development process for AI products is distinct from traditional products. In this presentation, Ria Sankar, Director of Program Management at Microsoft, introduces the best practices for developing AI products with insight, integrity, and consistency. Ria Sankar is a founding member of the AI for Good Research Lab at Microsoft.