February 24, 2020
AI Deception: When Your Artificial Intelligence Learns to Lie
In artificial intelligence circles, we hear a lot about adversarial attacks, especially ones that attempt to “deceive” an AI into believing, or to be more accurate, classifying, something incorrectly. Self-driving cars being fooled into “thinking” stop signs are speed limit signs, pandas being identified as gibbons, or even having your favorite voice assistant be fooled by inaudible acoustic commands—these are examples that populate the narrative around AI deception. One can also point to using AI to manipulate the perceptions and beliefs of a person through “deepfakes” in video, audio, and images. Major AI conferences are more frequently addressing the subject of AI deception too. And yet, much of the literature and work around this topic is about how to fool AI and how we can defend against it through detection mechanisms.
I’d like to draw our attention to a different and more unique problem: Understanding the breadth of what “AI deception” looks like, and what happens when it is not a human’s intent behind a deceptive AI, but instead the AI agent’s own learned behavior. These may seem somewhat far-off concerns, as AI is still relatively narrow in scope and can be rather stupid in some ways. To have some analogue of an “intent” to deceive would be a large step for today’s systems. However, if we are to get ahead of the curve regarding AI deception, we need to have a robust understanding of all the ways AI could deceive. We require some conceptual framework or spectrum of the kinds of deception an AI agent may learn on its own before we can start proposing technological defenses.
Read the full article from IEEE Spectrum.
Learn more about the Artificial Intelligence and International Stability Project:
Artificial Intelligence and International Stability Project
Despite calls from prominent scientists to avoid militarizing AI, nation-states are already using AI and machine-learning tools for national security purposes. AI has the pote...
Read MoreMore from CNAS
-
AI's Inhuman Advantage
Commentary
AI agents’ victories demonstrate that machines can dramatically outperform humans in command and control, a potential major advantage in war....
By Paul Scharre
-
America Can Win the AI Race
Commentary
If the United States wants to win the AI competition, it must approach Beijing carefully and construct its own initiatives thoughtfully....
By Paul Scharre
-
AI Military Competition: Tactical, Operational, and Strategic Implications
Podcast
Paul Scharre, Vice President and Director of Studies at CNAS, joins ChinaTalk to discuss AI, military, strategy, and US-China geopolitics. Listen to the full episode and more...
By Paul Scharre
-
Artificial Intelligence and Great Power Competition, With Paul Scharre
Podcast
Paul Scharre, the vice president and director of Studies at the Center for a New American Security, sits down with James M. Lindsay to discuss how artificial intelligence is r...
By Paul Scharre
