Verified AI: The Way Forward to More Reliable and Trustworthy AI Systems

Why Trust Techopedia

Verified AI, utilizing formal verification techniques, guarantees the correctness and dependability of AI systems, addressing concerns of accuracy, reliability, and trustworthiness. Implementing Verified AI requires collaboration, R&D investment, standards, and public education.


The Artificial Intelligence (AI) domain has recently witnessed tremendous growth, and the extensive developments in this area have significantly impacted human life. AI has come a long way in healthcare, transportation, energy, agriculture, customer service, manufacturing, finance, entertainment, and education. Developments in these areas are also paving the way for future advances.

Nonetheless, with the widespread use of AI-based applications, several concerns have been raised about these systems’ accuracy, reliability, and trustworthiness, which may apprehend the future growth of these systems. Therefore, it is of paramount importance to build trust in the correctness and reliability of AI-based systems. The apparent reasons for the issues mentioned above are many and include data biases, algorithmic biases, inaccurate and incomplete system specifications, and lack of transparency.

It is essential to mention that the issues, such as data biases, insufficient data, and lack of transparency, can be addressed using data augmentation, transfer learning, and Explainable AI (XAI) techniques. However, issues like inaccurate and incomplete specifications and algorithmic biases are more critical as they can lead the system to generate incorrect and undesirable outcomes. Therefore, there is a need for methods that can verify whether the AI system’s behavior against a particular input is correct or otherwise. Verified Artificial Intelligence, also known as Verified AI, can address issues related to reliability and trustworthiness.

What is Verified AI?

Verified AI can be defined as “using formal verification techniques to guarantee the correctness and dependability of AI systems.” Formal verification process uses mathematical and logical methods to investigate and certify that the system operates under pre-defined specifications. Formal verification-based systems have widely been used in different fields, such as safety-critical domains, cybersecurity, compiler development, etc. Using formal verifications in AI-based systems can make these systems dependable, reliable, and free from unintended biases that may result in unfavorable or hazardous results.

Significance of ensuring reliable and trustworthy AI systems

Because of several reasons, the reliability and trustworthiness of AI-based systems are regarded as essential attributes. In the first place, unreliable or biased AI systems can have serious consequences. In domains such as autonomous vehicles and healthcare, unreliable and untrustworthy systems may lead to physical hazards for the environment and human lives.

Second, if AI systems are not designed and appropriately trained, they can amplify existing societal biases and inequalities. For instance, if an AI-driven hiring system is trained on biased data or created to support existing biases, it could inadvertently exclude certain groups based on race or gender. Thirdly, unreliable, biased, or incorrect AI technology could reduce public interest in the domain, eventually obstructing its applications in several important areas, such as healthcare.


Therefore, to promote ethical practices and responsible use of AI technology while also taking advantage of its potential benefits, it is crucial to develop reliable and trustworthy systems free from biases and inaccuracies.

Verified AI Process

Verified AI uses formal verification techniques to provide a mathematical proof of correctness to ensure that AI systems are reliable, trustworthy, and without accidental biases or errors. This process involves two steps, namely, specification and verification. 

1. Specification step

During the specification step, the problem that the AI system needs to solve is defined mathematically. The specifications must be clear, unambiguous, and representative of the system’s requirements. The specification step is critical to ensure that the AI system’s development is done according to the intended context and is aligned with the established goals. Some tools that can be used for specification include TLA+, Alloy, etc.

2. Verification step 

The verification step of Verified AI tests and validates that the AI system meets its specifications and behaves as intended in different situations. Through the verification, any possible errors or bugs can be identified and corrected, thus ensuring that it is dependable and secure. Verification involves modelling the system mathematically and analyzing its behavior through logical reasoning and mathematical proofs. Examples of the tools for formal verification in Verified AI include DeepSpec, VeriAI, SafetyChecker, Probabilistic Programming and Verification (PPV), etc.

Benefits of Verified AI

There are several benefits of using Verified AI in AI-based systems. Some of these benefits include:

A high degree of confidence in systems’ behavior

Using Verified AI enhances the trust and confidence of users in AI systems because the systems behave correctly and work as per specifications. As a result, AI systems can be used with a high degree of confidence in disciplines, such as healthcare and for development of safety-critical applications. 

Managing bias in AI systems

Through formal verification, bias in AI systems can be detected and managed. This helps improve fairness, transparency, and confidence in AI systems. 

Reduced testing time and costs

Formal verification techniques generate unambiguous and correct system specifications and verify them through mathematical models. Therefore, errors and bugs are detected and fixed early in development. As a result, the effort and time required to test the AI system decreases. This also saves the costs that would have been significantly high in case of rework to fix the bugs.

Compliance with regulatory requirements for AI systems

AI-based systems, particularly from safety-critical domains, are often required to obtain certifications from regulatory authorities. The rigorous and systematic approach used in Verified AI helps AI systems comply with regulatory requirements.

Implementing Verified AI: The way forward

Verified AI is an emerging concept that needs the attention of various stakeholders, such as governments, industry, and standardization bodies, to make it an implicit component of future AI systems. Some possible directions to work are discussed below.

Government and industry liaison

Like any innovation, strong collaboration between the government and industry is needed to realize Verified AI’s significance and foster innovation in developing trustable systems. This collaboration can have various dimensions, for example, joint R&D initiatives, sharing resources and expertise, and regulatory support. In addition, funding support by the governments to the industry for the development of Verified AI systems can accelerate their adoption by the industry. Furthermore, collaboration can be made between the government and industry to exchange datasets to develop and verify the AI models, which will be beneficial for testing the AI systems on real-world scenarios.

Investments in R&D in the Verified AI domain 

Substantial investments in research and development to support Verified AI are crucial to advancing the domain’s state-of-the-art. The investment may result in the development of novel methods and tools capable of improving the reliability and correctness of AI-based systems. Besides industrial partnerships and open-source development, funding academic research can help accelerate the development of Verified AI-based techniques and tools.   

Introducing standards and guidelines for the implementation of Verified AI 

To implement Verified AI as a discipline, it is essential to establish guidelines that emphasize formal verification tools and methods to improve the reliability and correctness of AI-based systems. Only a detailed set of guidelines covering AI systems’ development, testing, deployment, and maintenance could be practical and may evolve as a standard later on. 

Having standards may provide a common vocabulary, language, and framework for verifying AI systems and can help guarantee consistency and interoperability across different applications and industries. The inputs from different stakeholders, such as governments, academia, industry, and society in general, can help develop effective guidelines and standards for Verified AI.

Enhancing public trust in Verified AI systems through education 

The public trust in Verified AI systems can be enhanced by educating people about the capabilities and benefits of such systems. By highlighting the working of Verified AI systems in a more understandable way to the public and aligning it to their benefits, this new paradigm can gain widespread acceptance, leading to more significant benefits for society.

Challenges to Implementing Verified AI

Despite its potential to enhance the trustworthiness of AI-based systems, there are several challenges to Verified AI that need immediate attention to address them. The challenges include:

Diversified skill set requirements 

Putting the new Verified AI-based systems into action requires knowledge and expertise in both formal methods and AI. While a skilled AI resource may not be difficult to find, having an experienced workforce in the formal methods domain will be a great challenge because, due to its high reliance on mathematical techniques, many would avoid choosing it as a career. Due to this, the availability of experienced personnel in AI and formal verification is insufficient, and thus due to limited available resources the cost of development of such systems increases.

Computational challenges in formal verification of large-scale and complex AI systems

Verifying the correctness and trustworthiness of large-scale AI systems through formal verification techniques is a complex phenomenon because of the intensive exploration, specifications, and validation of all the possible behaviors of the systems. Therefore, the process becomes compute-intensive and, as a result, may require a significant amount of time and computational resources to verify and test the specifications.

Integration of Verified AI into existing development processes

The conventional development of AI systems emphasizes functionality with little attention to verification. Therefore, it can be a challenge to integrate the Verified AI with the existing development workflows. The reason is that integrating verified AI into existing processes demands a shift in focus towards safety and reliability, as well as additional testing and verification steps, which could be a challenge to cope with initially.


In conclusion, verified AI has significant potential to ensure the reliability and trustworthiness of AI systems. By using formal methods and mathematical proofs to verify the correctness of AI systems, verified AI can address concerns about AI’s safety and dependability. However, implementing verified AI comes with several challenges. Therefore, multifaceted efforts are required to realize the potential of verified AI as an effective solution for developing AI systems.



Related Reading

Related Terms

Assad Abbas
Tenured Associate Professor
Assad Abbas
Tenured Associate Professor

Dr Assad Abbas received his PhD from North Dakota State University (NDSU), USA. He is a tenured Associate Professor in the Department of Computer Science at COMSATS University Islamabad (CUI), Islamabad campus, Pakistan. Dr. Abbas has been associated with COMSATS since 2004. His research interests are mainly but not limited to smart health, big data analytics, recommender systems, patent analytics and social network analysis. His research has been published in several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Systems Journal, IEEE Journal of Biomedical and Health Informatics,…