Below are highlights from my writings in 2020:

• Quantum Computing and AI Algorithmic Bias: Examines the correlation between application complexity, explainability and when an AI application license should be required.
• AI Liability: When “Intelligent Deviation” is Undesirable: A discussion of AI-action risk mitigation strategy to promote effective management. An update touched on AI enabled end-user tracking capabilities.
• AI and COVID-19: Securing the Intensified Reliance on AI Prime Operational Qualities: A review of safe and efficient operational characteristics and how they tie to operational certification for AI. Absent ensuring that the safe and efficient prime qualities are properly accounted for prior to deployment (in the algorithmic design and contractual obligations), the potential for harm is ineffectively and incompletely abated, rendering their erosion inevitable.
• Monitoring, Biometrics and Robotics: AI in the ‘Day-After’ COVID-19: Examines the integration of AI into monitoring, biometrics and robotics applications. Five post updates focused on the use of drones, big data, re-identification, accountability, ethics, fairness, and nondiscrimination.
• Why Writing Law Should be Like Writing Source Code: Artificial Intelligence and the Future of Privacy Laws: Begins with a revisit of my 2010 Association for the Advancement of Artificial Intelligence paper “Application of an Autonomous Intelligent Cyber Entity as a Veiled Agent and advocates for its use as a privacy-enhancing capability.
• Redefining the NIST Definition of AI Trustworthiness: Shows how NIST’s seven AI trust value variables can be reduced down to a single value: Explainability.
• A Brief XAI Recap from SLS’ 17th Annual Digital Economy Conference: Summarizes my AI panel’s discussion, which primarily focused on XAI.