Whether we realize it or not, artificial intelligence (AI) has infiltrated our everyday lives—it powers tasks and applications from internet searches to dating sites to credit card fraud detection. But AI systems are trained using data, and these data can contain inherent biases as a result of past decisions by humans. Dr. Lai-Tze Fan, Canada Research Chair in Technology and Social Change, aims to create more equitable, diverse and inclusive AI technologies by exploring how social inequalities and biases in AI can be initiated—or interrupted and improved.
By producing data-driven research on AI-biased design and production, Fan and her research team are identifying and breaking down the unseen social implications of using this technology. They are creating innovative alternative methods, resources and toolkits for AI with outcomes enhanced by electronic data interchange (EDI). They are also developing interactive and experiential methods and tests, and applying EDI-enhanced design principles and practices for AI.