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AI Model Inspector: AI Maintenance Inspired By Automotive Maintenance

AI Model Inspector: AI Maintenance Inspired By Automotive Maintenance

Machine Learning

3 main points
✔️ As AI advances, system and model maintenance becomes increasingly important.

We propose a method to support AI maintenance practices called ✔️ AI Model Inspector.
✔️ This method is expected to improve the robustness of AI.

AI Maintenance: A Robustness Perspective
written by Pin-Yu ChenPayel Das
(Submitted on 8 Jan 2023)
Comments: Accepted to IEEE Computer Magazine. To be published in 2023

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

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The images used in this article are from the paper, the introductory slides, or were created based on them.

Introduction

Today, AI is widely used in a variety of settings. Therefore, as AI advances, maintenance of systems and models becomes increasingly important. Regular updates and quality control are necessary to maintain performance as data and the environment change. It is also important to continue to improve the system to eliminate errors and biases and to increase its reliability and effectiveness.

Therefore, the paper also addresses the importance of AI maintenance and emphasizes the need to work to ensure the reliability and safety of AI as well as automobiles and other critical technologies. It also explores how AI technology can be used to build a sustainable future and offers suggestions for improving AI robustness.

Issue

One challenge during the development phase is data quality. In particular, inaccurate data, mislabeling, and bias or imbalance in the data can affect the quality of the model. Also, noisy data may be unintentionally included in the training data. These problems can degrade model performance.

Challenges during the implementation phase are primarily related to the robustness of the model. The model may be vulnerable to external attacks. In particular, if the model is vulnerable to hostile attacks, it may produce prediction results that differ from normal behavior. This can lead to security risks and reduced reliability of predictions. Additionally, models may not function properly in the real world if their behavior has not been adequately evaluated in an environment that differs from the training data.

Similarities between automobiles and AI

There are several similarities between automotive and AI maintenance. In automotive maintenance, requirements for reliability and safety are rigorously certified from the development stage through implementation. Similarly, developing AI models is expensive and requires large data sets and extensive training resources. With this investment, robust AI models are expected.

Automotive maintenance includes routine inspections and repairs, but there are also various procedures in AI maintenance. During the model development phase, crash tests and safety assessments are performed. And after the operational phase, model monitoring and anomaly detection are important.

AI models also need to be repaired or updated, just as parts of an automobile need to be repaired or replaced. When model performance degradation or anomalies are detected, fixes or model upgrades are made.

Additionally, there are ethical and social implications to be considered in the use of AI technology. With proper education and the adoption of sustainable practices, the use of AI is expected to have social benefits.

Given these similarities, it is suggested that the concepts of automotive maintenance can be applied to AI maintenance to help achieve robust AI systems.

AI Model Inspector

AI Model Inspector is a methodology to support AI maintenance practices. The methodology provides a conceptual pipeline for proactively detecting and mitigating robustness issues across the AI lifecycle.AI Model Inspector consists of three processes

1. Robustness test

AI model inspectors will evaluate model and data characteristics to identify potential risks regarding robustness. This includes both qualitative assessment and quantitative analysis.

2. risk mitigation

Modifications and updates to the model are made to address identified robustness risks. This may include model tweaking, retraining, additions or replacements.

3. continuous monitoring and improvement

The AI Model Inspector is applied on an ongoing basis, not a one-time basis; ongoing robustness is ensured as the AI model repeats data collection, training, and deployment states.

Specific examples include backdoor detection and mitigation and anomalous input detection and mitigation. Backdoor detection uses techniques such as Trojan net detectors to identify hidden backdoors and modify models to remove them. Anomalous input detection uses internal data representation and external knowledge checks to filter out anomalous input and update the model to enhance robustness. It also provides a roadmap for defining AI robustness levels, quantifying technological advances and risks, inspecting, auditing models, and facilitating standardization. This will improve the robustness of AI and ensure critical elements such as safety and security.

AI model inspectors will become increasingly important as AI technologies evolve. Robust AI models are essential for realizing societal values, such as protecting individual privacy and ensuring fairness. Therefore, active efforts to ensure the robustness of AI models will contribute to the reliable and sustainable development of AI technology.

Below isan AI model inspector overview diagram. If the AI for detection on the right finds a problem, it is designed to mitigate the problem.

Summary

We described the AI Model Inspector, a new maintenance framework for ensuring the robustness of AI technology; for AI systems to be reliable and function reliably, it is important to detect and mitigate the risk of lack of robustness before it occurs. This framework is automated, can be applied on a large scale, and aims to increase the reliability of AI models.

Furthermore, taking a cue from vehicle autonomy, we have also defined different levels of AI robustness: the rapid spread of AI technologies is increasing the impact of AI in our lives and in society. Thus, AI maintenance is becoming increasingly necessary.

The AI Model Inspector framework can incorporate not only robustness, but also other aspects of trustworthy AI such as fairness, accountability, and privacy. As AI technologies evolve and become more prevalent, it will be essential to develop and implement frameworks such as the AI Model Inspector to ensure trustworthiness and security.

 

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