Patterns and Antipatterns, Principles, and Pitfalls: Accountability and Transparency in Artificial Intelligence
Jeanna Matthews
Abstract
This article discusses a set of principles for accountability and transparency in AI as well as a set of antipatterns or harmful trends too often seen in deployed systems. It provides concrete suggestions for what can be done to shift the balance away from these antipatterns and toward more positive ones.
Topics & Concepts
Transparency (behavior)AccountabilitySet (abstract data type)Computer scienceManagement scienceEngineering managementData scienceProcess managementKnowledge managementArtificial intelligenceEngineeringPolitical scienceComputer securityLawProgramming languageExplainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning