November 19, 2019
Preparing the Military for a Role on an Artificial Intelligence Battlefield
The Defense Innovation Board—an advisory committee of tech executives, scholars, and technologists—has unveiled its list of ethical principles for artificial intelligence (AI). If adopted by the Defense Department, then the recommendations will help shape the Pentagon’s use of AI in both combat and non-combat systems. The board’s principles are an important milestone that should be celebrated, but the real challenge of adoption and implementation is just beginning. For the principles to have an impact, the department will need strong leadership from the Joint AI Center (JAIC), buy-in from senior military leadership and outside groups, and additional technical expertise within the Defense Department.
In its white paper, the board recognizes that the AI field is constantly evolving and that the principles it proposes represent guidelines the department should aim for as it continues to design and field AI-enabled technologies. The board recommends that the Defense Department should aspire to develop and deploy AI systems that are:
- Responsible. The first principle establishes accountability, putting the onus on the human being for not only the “development, deployment, [and] use” of an AI system, but most importantly, any “outcomes” that system produces. The burden rests on the human being, not the AI.
- Equitable. The second principle calls on the DoD to take “deliberate steps” to minimize “unintended bias” in AI systems. The rise of facial recognition technology and the subsequent issues of algorithmic biases show that the board is right to prioritize mitigating potential biases, particularly as the DoD continues to develop AI systems with national security applications.
- Traceable. The third principle addresses the need for technical expertise within the Defense Department to ensure that AI engineers have an “appropriate understanding of the technology” and the insight of how a system arrives at its outcome.
- Reliable. The board’s fourth principle essentially says that an AI system should do what it has been programmed to do within the domain it has been programmed to operate in. AI engineers should then conduct tests to ensure the “safety, security, and robustness” of the system across its “entire life cycle.”
- Governable. The fifth principle tackles the need for fail-safes in situations where an AI system acts unexpectedly. The AI system should be able to “detect and avoid unintended harm,” and mechanisms should exist that allow “human or automated disengagement” for systems demonstrating “unintended escalatory” behavior.
Read the full article in The National Interest.
More from CNAS
-
U.S. Chip Controls and the Future of AI Compute
Podcast
That escalated quickly! Emily and Geoff discuss why the U.S. aim to deny China access to the computing power necessary for frontier AI capabilities has led to an ever expandin...
By Emily Kilcrease, Geoffrey Gertz & Pablo Chavez
-
Asymmetry and AI: The Battle for Power
Podcast
Paul Scharre, Vice President and Director of Studies at CNAS, joins Zero Pressure to discuss the world of asymmetric warfare, a term used to describe imbalances in conflict. F...
By Paul Scharre
-
Competition, Not Control, is Key to Winning the Global AI Race
Commentary
The United States, with much of the world’s AI-enabling infrastructure, has positioned itself as the global leader in AI innovation. That might not be the case for much longer...
By Keegan McBride & Matthew Mittelsteadt
-
Regulating AI Is Easier Than You Think
Commentary
Countries can regulate AI from the ground up by controlling access to highly specialized chips...
By Paul Scharre
