Key Insights

• •

  AI systems can unintentionally amplify racial biases in healthcare and hiring processes.

• •

  The healthcare system's reliance on algorithmic scoring can disproportionately harm Black and Hispanic patients.

• •

  Historically biased training data leads AI algorithms to favor certain demographic groups, perpetuating inequality.

• •

  Dark patterns in UX design can mislead users, taking advantage of their trust and leading to uninformed decision-making.

• •

  Deep fakes pose a significant threat by distorting facts, misrepresenting people, and potentially influencing democratic processes.

• •

  Human biases can still affect AI outputs despite the technology's reliance on structured data.

• •

  Research shows that job candidates often distrust AI in hiring decisions over human judgment.

• •

  There is a lack of comprehensive research into the implications of AI bias in hiring practices.

• •

  The history of unethical research, like the Tuskegee study, underscores the need for human subject protections in technology.

• •

  Ethics must be a core consideration in UX research and AI system design to prevent harm.

Notable Quotes