This archive report was first published on 17 November 2019.
As we increasingly rely on Artificial Intelligence (AI) to make decisions in our lives, it's essential to understand how these systems work and the potential risks they pose.
Published on November 17, 2019, a recent article highlighted the growing concern that AI systems are being gamed to amplify biases relating to race, gender, and social-economic classes.
These biases are often baked into AI systems, making them difficult to detect and can cause far-reaching harm to affected individuals and communities.
For instance, recruitment AI systems may filter employees' surnames in ways that preclude people from certain regions or tribes from jobs or other services.
They can also be configured to show that people from certain communities or gender or age are more likely to commit crime when it may not be true.
Based on these results, authorities may surveil and arrest people who fit the profile depicted by the past data, even when the previously crime-prone profiles have changed.
These biases can affect one's ability to get credit facilities, quality education, or medical services.
While AI systems can be incredibly helpful extensions of how humans work, it's essential to recognize that AI architects have inherent biases and blind spots.
