Humans are innately biased, and the AI we develop can reflect our biases. These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Those learned biases might be perpetuated during the deployment of AI, resulting in skewed outcomes.
AI bias can have unintended consequences with potentially harmful outcomes. Examples include applicant tracking systems discriminating against gender, healthcare diagnostics systems returning lower accuracy results for historically underserved populations, and predictive policing tools disproportionately targeting systemically marginalized communities, among others.
