AI’s forensic forensics instrument of AI that fails to understand what went wrong

AI systems can be damaged due to technical design defects or prejudiced training data. They can also suffer from vulnerabilities in their code, which can be operated by malicious hackers. Isolating the cause of AI damage is imperative to fix the system.

But AI systems are usually opaque, even for their creators. The challenge is how to study AI systems after they fail or fall victim to attack. There are techniques for verifying AI systems, but they require access to internal data of the AI ​​system. This access is not guaranteed, especially to criminologists called to determine the cause of their own damage in the AI ​​system, which makes the investigation impossible.

We are Computer scientists who are learning Digital forensics. Our team at the Georgia Technical Institute has built a system, Ai psychiatryOr AIP, this can recreate the script in which AI failed to determine what went wrong. The system deals with the challenges of the AI ​​forensics through the recovery and “re -re -re -AI model so that it can be tested systematically.

Uncertainty of AI

Imagine that the control car alone deviates from the road without an easily noticeable reason and then collapses. Sensor diaries and data may suggest that the defective camera caused AI to misinterpret the road sign as a deviation command. After a critical of the mission failure as Vehicle autonomous disasterInvestigators have to determine exactly what caused the mistake.

Is the crash caused by a malicious attack against AI? In this hypothetical case, the camera malfunction may be the result of a security vulnerability or an error in its software, which was operated by a hacker. If investigators find such a vulnerability, they must determine if this has caused the crash. But this determination is not a small feat.

Although there are forensic methods for restoring some evidence of drone failures, autonomous vehicles and other so-called cyber-physics systems, no one can capture the clues needed for full study of AI in this system. Advanced AIS can even Update their decision making -And therefore the clues, which makes it impossible to explore the most up-to-date models with existing methods.

Pathology for AI

AI Psychiatry applies a series of foregone algorithms to isolate data behind AI System decision making. These pieces are then reassembled in a functional model, which is identical to the original model. Investigators can “resuscitate” and in a controlled environment and test it with malicious entrances to see if it exhibits harmful or hidden behavior.

AI Psychiatry accepts for contribution memory imageA momentary photo of the bits and bytes loaded when AI worked. The image of memory during the crash in the script of autonomous vehicles contains decisive clues for the internal state and decision -making processes of AI controlling the vehicle. With AI psychiatry, investigators can now lift the right AI model from memory, distract their bits and bytes, and load the model into a secure test environment.

Our team tested AI Psychiatry with 30 AI models, 24 of which were deliberate. “backward“In order to obtain incorrect results in specific triggers. The system was successfully able to restore, respond and test any model, including models that are commonly used in real -world scenarios, such as identification of street signs in autonomous vehicles.

For now, our tests suggest that AI Psychiatry can effectively resolve the digital mystery behind failure, such as an autonomous car accident that would have left more questions before answering. And if he does not find vulnerability in the AI ​​system of the car, AI Psychiatry allows investigators to turn off AI and look for other causes, such as a defective camera.

Not just for autonomous vehicles

The main algorithm of AI Psychiatry is common: it focuses on the universal components that all AI models must have to make decisions. This makes our approach easily expanding to all AI models that use popular AI AI development frames. Anyone who works to research the possible AI refusal can use our model assessment system without a preliminary knowledge of its accurate architecture.

Whether AI is a bot that makes the product recommendations or a system that runs the autonomous drone fleets, AI Psychiatry can restore and implement AI for analysis. Ai psychiatry is coded code for any researcher to use.

AI Psychiatry can also serve as a valuable tool for conducting audits on AI systems before problems arise. With government agencies from law enforcement to childcare services for children integrating AI systems into their work streams, AI audits are becoming an increasingly common state level requirement. With an instrument like AI Psychiatry in hand, auditors can apply a consistent forensic methodology in various AI platforms and implementation.

In the long run, this will pay meaningful dividends for both the creators of AI systems and all those affected by the tasks they perform.

David OiglickDoctor of Science Student for Electric and Computer Engineering, Institute of Technology in Georgia and Brendan SolformagioAssistant Professor of Cybersecurity and Privacy, and Electrical and Computer Engineering, Institute of Technology in Georgia

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