One way to think of AI is as “invisible work.” By design, it performs tasks that humans would otherwise complete with invisible effort. It can become easy to stop asking how it was made or with whose data. Today at AIES, we’re talking about how it takes a village to raise an AI system and learn what that village needs.

We think of bias in machine learning, like we do about people. We’re biased or unbiased; we’re corrupt, or we’re pure. The beauty and irony of machine learning lie in how difficult it is to make an orderly representation of our will when our will is anything but orderly. Bias mitigation is not a one-stop shop. It’s hard, and sometimes our efforts backfire. Today, we’re looking at the old problems behind our newest technology.

Shifting the AI governance paradigm from an oligopoly into something more democratic is how we can all reap the benefits of collective intelligence. We’re here in Montreal this week to learn from the researchers chipping away at each piece of this problem.