Ride the Lightning

Cybersecurity and Future of Law Practice Blog
by Sharon D. Nelson Esq., President of Sensei Enterprises, Inc.

NIST Proposes Four Principles to Make Artificial Intelligence Explainable

August 20, 2020

The National Institute of Standards and Technology (NIST) has published a 30-page August 2020 draft of "Four Principles of Explainable Artificial Intelligence." The principles are a part of NIST's research to build trust in AI systems by understanding theoretical capabilities and limitations of AI, and by improving accuracy, reliability, security, robustness, and explainability in the use of the technology.

"AI is becoming involved in high-stakes decisions, and no one wants machines to make them without an understanding of why," said NIST electronic engineer Jonathon Phillips, one of the report's authors. "But an explanation that would satisfy an engineer might not work for someone with a different background. So, we want to refine the draft with a diversity of perspective and opinions."

The four principles for explainable AI are:

  1. AI systems should deliver accompanying evidence or reasons or their outputs;
  2. AI systems should provide meaningful and understandable explanations to individual users;
  3. Explanations should correctly reflect the AI system's process for generating the output; and
  4. The AI system "only operates under conditions for which it was designed or when the system reaches a sufficient confidence in its output."

"As we make advances in explainable AI, we may find that certain parts of AI systems are better able to meet societal expectations and goals than humans are," said Phillips. "Understanding the explainability of both the AI system and the human opens the door to pursue implementations that incorporate the strengths of each."

NIST will be accepting comments until October 15, 2020.

Sharon D. Nelson, Esq., President, Sensei Enterprises, Inc.
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