Reducing Bias in AI with Open Source – Abubakar Siddiq Ango, GitLab
Bias in AI has become a hot topic and increasingly, we are seeing how dangerous it is. Dangerous because AI is gain more influence in decisions that impact lives, decisions about who gets employment, healthcare or economic development and who gets profiled for crime, extra search at the airport, and so on. Even tools designed to identify AI-generated content have shown bias against non-native speakers based on their choice of verbiage. To reduce bias, experts often refer to having diverse training data and cognitive diversity, but where else is this achievable than in Open-source communities? This session is designed to open up discussions around reducing bias in AI with Open source. Abubakar will start by sharing examples of biases, sharing strategies to leverage open-source communities, and opening the floor for attendees to share their opinions and views, with the goal of creating a resource that will be valuable to the community.
