When AI systems discriminate against women, the consequences can span from minor inconveniences to major life disruptions. For example, voice recognition software not accurately recognizing female voices or healthcare algorithms underserving women by not accounting for their specific symptoms. The root of this issue lies in biased training data. A critical step towards fixing this is by incorporating a wide range of gender-diverse data and perspectives from the initial design phase of AI tools.
- Log in or register to contribute
Contribute to three or more articles across any domain to qualify for the Contributor badge. Please check back tomorrow for updates on your progress.