Overcoming the Challenges of Verifying AI-Based Systems

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Overcoming the Challenges of Verifying AI-Based Systems

Introduction
About Telex
What is an AI-based System?
Challenges of Verifying AI-based Systems
Current Approaches in Verification
The Top-Down Approach
Pros of the Top-Down Approach
Cons of the Top-Down Approach
Suggested Solutions to Verification Challenges
- Statistical Methods
- Formal Verification
- Requirement-based Systems
- Careful Reasoning
- Machine Learning Techniques
  - Deep Explore
- Using Machine Learning to Verify ML
- Safety Case Approach
The Proposed Verification Process
- Enumerating Basic Dimensions
- Writing Abstract Scenarios
- Running Scenarios in a Verification System
- Collecting Coverage Data
- Continuous Improvement
Conclusion
Resources

🤖 Understanding the Challenges of Verifying AI-Based Systems

As the field of AI continues to advance, the need for robust verification techniques becomes increasingly critical. Verifying AI-based systems, such as autonomous vehicles, presents unique challenges due to their complexity and opacity. In this article, we will delve into these challenges and explore various approaches to overcome them.

1. Introduction

Welcome! In this article, we will discuss the challenges involved in verifying AI-based systems. We will explore the nuances of these systems, their opaque nature, and the difficulties they Present in the verification process. Additionally, we will examine existing approaches and propose a top-down verification approach as a potential solution.

2. About Telex

Before we dive in, let's take a moment to introduce Telex. Telex is a software company that specializes in providing Scenario-based automation and analytics tools for verifying autonomous vehicles and AI-based systems. We will refer to Telex throughout this article to provide real-world context.

3. What is an AI-Based System?

To understand the challenges of verifying AI-based systems, we must first grasp what these systems entail. In simple terms, an AI-based system consists of multiple components, such as sensing, Perception, prediction, planning, control, and more, all working together to achieve a specific goal. Imagine an autonomous vehicle, for instance, where sensors Gather data, machine learning algorithms process the information, and the vehicle responds accordingly.

However, verifying an AI-based system is not as straightforward as verifying individual components. The complexity arises due to the interdependencies between various subsystems, the involvement of machine learning algorithms, and the interactions with the dynamic real-world environment.

4. Challenges of Verifying AI-Based Systems

Verifying AI-based systems presents several challenges, including complexity, opacity, lack of clear specifications, and probabilistic checks. Let's explore each of these challenges in detail.

A. Complexity: AI-based systems, including autonomous vehicles, are inherently complex, often involving multiple layers of algorithms, mechanics, software, and hardware. The sheer Scale and interconnections make it challenging to fully understand and verify the system's behavior.

B. Opacity: Machine learning algorithms, a crucial component of many AI-based systems, introduce opacity since they lack a clear internal modular structure. With complex neural networks composed of nodes and weights, it becomes difficult to perform module-by-module verification, as typically done in traditional systems.

C. Lack of Clear Specifications: Defining clear specifications for an AI-based system can be daunting. Determining the system's boundaries and the expected output becomes challenging due to the complex interactions and probabilistic nature of these systems.

D. Probabilistic Checks: Verifying an AI-based system often involves probabilistic checks. These systems are designed to handle uncertainty and make probabilistic decisions. Checking if a bug is truly fixed or evaluating system performance under diverse conditions relies heavily on statistical methods and testing on a massive scale.

5. Current Approaches in Verification

Various approaches have been employed to verify AI-based systems. Let's explore some of the commonly used methods:

A. Statistical Methods: Statistical methods, such as Markov Chain Monte Carlo (MCMC), are widely used to assess residual risks and estimate system performance. While effective, these methods have limitations and cannot address unknown issues.

B. Formal Verification: Formal verification techniques are excellent when feasible; however, they struggle with complex, highly probabilistic systems. The lack of modularity and extensive computational requirements often make formal verification impractical for large-Scale AI-based systems.

C. Requirement-based Systems: Requirement-based systems offer a structured approach to verification by establishing specific requirements and validating system behavior against those requirements. However, a more flexible and risk-driven approach may be preferable.

D. Careful Reasoning: Some verification engineers adopt a careful reasoning approach, focusing on essential tests and avoiding non-essential, expensive tests. While this approach can be useful in certain cases, given the availability of cost-effective virtual testing, it may be more efficient to test comprehensively and reason about the probabilities of bugs.

E. Machine Learning Techniques: One approach gaining traction is the use of machine learning techniques to verify machine learning systems. Tools like DeepExploit explore load coverage rather than code coverage, improving the identification of edge cases and blind spots. However, implementation coverage and limitations of human-like reasoning in machine learning systems need to be considered.

F. Safety Case Approach: The safety case approach involves enumerating risks in human-understandable terms to make the verification process transparent and comprehensible to various stakeholders. While a Consensus is forming around this approach, challenges remain regarding verifying all risk dimensions effectively in an industrial-scale setting.

6. The Top-Down Approach

To address the challenges of verifying AI-based systems, we propose a top-down verification approach. This approach involves breaking down the system's verification into basic dimensions and writing abstract scenarios to cover those dimensions. Here's how it works:

Enumerate Basic Dimensions: Identify and enumerate various risk dimensions related to the system, including driving scenarios, human behaviors, ML limitations, and potential failures.
Write Abstract Scenarios: Use a scenario description language, such as MSDL, to create abstract scenarios that cover the identified dimensions. These abstract scenarios define the conditions and interactions the system should be tested against.
Run Scenarios in a Verification System: Utilize a suitable verification platform to run the abstract scenarios. This process involves executing scenarios multiple times with different seeds to account for variations.
Collect Coverage Data: Collect coverage information from the verification system to assess how well the system performs in terms of coverage for each risk dimension. This data provides insights into the verification progress and highlights areas that require further attention.
Continuous Improvement: Iterate the process, continually updating the abstract scenarios, running them in the verification system, and collecting coverage data. This iterative approach ensures comprehensive verification and continuous improvement over time.

7. Pros of the Top-Down Approach

The top-down approach to verification offers several advantages:

Modular and Clear Verification Process: By breaking down verification into basic dimensions and abstract scenarios, the verification process becomes modular and transparent, enabling easier understanding and management of the verification effort.
Comprehensive Coverage: The proposed approach aims to cover a wide range of risk dimensions, ensuring a holistic verification of the entire system rather than focusing solely on individual components.
Industrial-Scale Feasibility: The iterative nature of the approach allows for scalability and industrial-scale verification. By leveraging verification platforms, it becomes feasible to verify complex, opaque systems efficiently.

8. Cons of the Top-Down Approach

While the top-down approach brings significant benefits, a few considerations should be noted:

Implementation Challenges: Implementing the top-down approach may require adapting existing verification systems and tools to accommodate the modular verification process. This adaptation may incur additional development effort.
Verification Performance: The effectiveness of the top-down approach depends on the chosen verification platform and the quality of the abstract scenarios. It is crucial to select suitable platforms and constantly improve the abstract scenarios to ensure accurate and comprehensive verification.

9. Suggested Solutions to Verification Challenges

To overcome the challenges of verifying AI-based systems, a combination of approaches and techniques can be employed. Let's briefly explore some of these solutions:

Statistical Methods: Statistical methods provide insights into residual risks and system performance but should be complemented by other techniques due to their limitations in addressing unknown issues.
Formal Verification: Formal verification can be a powerful tool when applicable, but it may struggle with highly complex and probabilistic systems.
Requirement-based Systems: A structured requirement-based approach can be helpful, but considering risk dimensions rather than strict requirements may offer more flexibility and effectiveness.
Careful Reasoning: While careful reasoning can help prioritize tests, leveraging cost-effective virtual testing and comprehensive verification may provide better results.
Machine Learning Techniques: Utilizing machine learning techniques, such as deep exploration, can improve verification by considering load coverage rather than focusing solely on code coverage. However, implementation coverage and the limitations of machine learning reasoning should be taken into account.
Using Machine Learning to Verify ML: Training machine learning models to verify other machine learning systems shows promise and has achieved notable success in various competitions. However, the opacity of machine learning systems poses challenges in ensuring transparency and interpretability.
Safety Case Approach: Creating safety cases that enumerate risk dimensions in understandable terms is gaining recognition; however, practical methods for effectively verifying all risk dimensions in an opaque system at an industrial scale need further exploration.

10. The Proposed Verification Process

To put the top-down approach into practice, the verification process can be summarized as follows:

Enumerate all basic dimensions: Identify various risk dimensions related to the system's behavior, functionality, and external interactions.
Write abstract scenarios: Use a scenario description language, like MSDL, to create abstract scenarios that cover the identified dimensions.
Run scenarios in a verification system: Utilize a suitable verification platform to execute the abstract scenarios multiple times, accounting for variations.
Collect coverage data: Continuously collect coverage data from the verification system to assess the extent of coverage achieved for each risk dimension.
Continuous improvement: Iterate the process, updating abstract scenarios, running them in the verification system, and analyzing coverage data to identify gaps and refine the verification approach.

By adopting this process, the verification efforts can be focused on deliberately covering various risk dimensions, ensuring a comprehensive and methodical approach to verifying AI-based systems.

11. Conclusion

Verifying AI-based systems presents unique challenges due to their complexity and opacity. However, by utilizing a top-down verification approach and leveraging the strengths of different techniques, we can enhance the reliability and safety of AI systems. The proposed process of enumerating risk dimensions, writing abstract scenarios, and collecting coverage data provides a systematic framework for verification. As AI continues to advance and play an increasingly significant role in various domains, robust verification processes are essential for building trust and ensuring safe and reliable AI-based systems.