User Question: Who will benefit most from XAI?
I think that everyone can benefit from explainable AI and, in the sense that, you shouldn't be thinking about who but why it is important. The reason why we believe that it is important goes back to the question of why machine learning is important. Machine learning is a very powerful tool in the sense that it allows you to make predictions given the data you have. Most people today have access to data in one way or another because they either generate the data themselves or have access to the many publicly available datasets out there for specific problems. This means that you can start asking questions of a predictive type and explore the answers within your domain.
So essentially, the question is really asking “for whom may machine learning be important to” and the answer is that it’s important to anyone who has access to some sort of data and would like to ask predictive questions about that data. If you are in that group (this group will, arguably, continue to grow as more data is being generated and more people become data-aware), then explainability is crucial even if there’s not a life or death question you’re hoping to ask and answer. You also want to understand what makes these predictions behave the way they do. That is the nature of how humans work. For example, if I tell you that we should jump off a building then the question you’d ask is “why?” You should be able to look into the reasoning and if it doesn't make sense, we shouldn't jump. So explainable AI is suitable for anyone that has a predictive need and is trying to use machine learning to get to those judgements.
Jorge Torres is the Co-founder & CTO of MindsDB. He is also a visiting scholar at UC Berkeley researching machine learning automation and explainability. Prior to founding MindsDB, he worked for a number of data-intensive start-ups, most recently working with Aneesh Chopra (the first CTO in the US government) building data systems that analyze billions of patients records and lead to highest savings for millions of patients. He started his work on scaling solutions using machine learning in early 2008 while working as first full time engineer at Couchsurfing where he helped grow the company from a few thousand users to a few million. Jorge had degrees in electrical engineering & computer science, including a masters degree in computer systems (with a focus on applied Machine Learning) from the Australian National University.