October 29, 2020
AI Ethical Principles: Implementing the U.S. Military’s Framework
The US Defense Department is still in the early stages of determining how best to ensure the development and deployment of AI that is ethical, reliable, and secure. Earlier this year, the DoD formally adopted a set of AI ethical principles that are meant to guide the Department’s development, adoption, and use of AI-enabled systems.
Over the last two years, the DoD has taken a number of steps to lay the groundwork for AI adoption.
The DoD, and in particular, the Joint Artificial Intelligence Centre (JAIC), is in the midst of transforming those principles into actionable guidance for DoD personnel. For the principles to be meaningful and enduring, the JAIC will need additional authority and resources; the DoD will also need to work hand-in-hand with allies and partners who are also tackling the challenge of ensuring safe, secure, and ethical AI. What can others learn from the US experience?
Read the full article from RSIS Commentary.
More from CNAS
-
Technology & National Security
New Age of Missiles, Drones Reshapes Battlefields Around the WorldThe world is on the cusp of a third missile age, with conventional ballistic weapons proliferating at unprecedented speed in the wake of widespread, battlefield-warping deploy...
By Paul Scharre
-
Technology & National Security
Spies, Satellites & Startups: Intel in the Digital AgeAs technological innovation accelerates, how should the United States and its allies rethink collaborative threat tracking in an era of commercial tech proliferation? As AI au...
By Anthony Vinci
-
Technology & National Security
CNAS Insights | Washington Can’t Afford to Ignore AI’s Warning ShotOn July 21, OpenAI disclosed the first publicly known incident of AI models escaping their isolated testing environment, gaining unauthorized internet access and hacking into ...
By Ruby Scanlon & Janet Egan
-
Technology & National Security
Can China Keep Its AI Open?Without reliable ways to measure dangerous capabilities, some judgments may be wrong. And an open model that clears a badly drawn line proliferates globally, offering maliciou...
By Ruby Scanlon
