Bringing Transparency Into AI

Companies are increasingly using machine learning models to make decisions, such as the allocation of jobs, loans, or university admissions, that directly or indirectly affect people's lives. Algorithms are also used to recommend a movie to watch, a person to date, or an apartment to rent. When talking to business customers – the operators of machine learning (ML) – I hear growing demand to understand how these models and algorithms work, especially when there is an expanding number of machine learning cases without humans in the loop. Imagine an ML model is recommending the top 10 candidates from 100 applicants for a job post. Before trusting the model's recommendation, the recruiter wants to check the results.