Practical Principles of Ethical AI

Theory is useful, but how do we apply ethics in day-to-day AI development? Many organizations use the F.A.T.E. framework to ensure their tools are safe and just.

F.A.T.E. Framework

• Fairness: Algorithms must not create or reinforce bias. If an AI is used for hiring, it must not favor one gender or ethnicity over another. Developers must actively audit their data for representation.
• Accountability: Who is responsible when AI fails? There must always be a "human in the loop." Automated decisions should have an appeal process overseen by a person.
• Transparency: Users should be informed when they are interacting with an AI. Furthermore, the criteria used by the AI to make a decision should be open to inspection.
• Ethics: The system should align with broader human values, avoiding harm (non-maleficence) and actively doing good (beneficence).