In their paper titled "Assurance 2.0: A Manifesto," authors Robin Bloomfield and John Rushby address the challenges faced by system assurance in the modern era. They highlight two main challenges: the emergence of autonomous systems driven by machine learning and AI, and the need for ultra-rapid system development. These challenges are accompanied by persistent issues such as the requirement for efficient, effective, and timely assurance. The authors argue that traditional assurance methods are often perceived as hindrances to innovation due to their perceived high cost and time-consuming nature. To address this problem, they propose a modernized framework called Assurance 2.0. Contrary to expectations, this framework actually enables innovation while ensuring continuous incremental assurance. Assurance 2.0 achieves this by introducing a more rigorous approach to assurance. It places increased emphasis on reasoning and evidence employed during the assurance process, aiming for explicit identification of defeaters (factors that can undermine confidence) and counterevidence (evidence contradicting expected outcomes). By adopting these practices, Assurance 2.0 aims to support innovation while maintaining a robust assurance process. Overall, Bloomfield and Rushby's manifesto presents Assurance 2.0 as an enabler rather than a hindrance to innovation in system assurance. It offers a modernized framework that addresses the challenges posed by autonomous systems and rapid development, making assurance more rigorous through enhanced focus on reasoning, evidence, defeaters, and counterevidence.
- Emergence of autonomous systems driven by machine learning and AI
- Need for ultra-rapid system development
- Requirement for efficient, effective, and timely assurance
- Traditional assurance methods are often perceived as hindrances to innovation due to their high cost and time-consuming nature
- Enables innovation while ensuring continuous incremental assurance
- Introduces a more rigorous approach to assurance
- Emphasizes reasoning and evidence during the assurance process
- Aims for explicit identification of defeaters and counterevidence
- Assurance 2.0 supports innovation while maintaining a robust assurance process
- System assurance faces challenges in the modern era due to the rise of autonomous systems driven by machine learning and AI, as well as the need for fast system development.
- There are persistent issues in system assurance, including the requirement for efficient and timely assurance.
- Traditional assurance methods are often seen as obstacles to innovation because they are expensive and time-consuming.
- A new framework called Assurance 2.0 has been proposed to address these challenges and issues.
- Assurance 2.0 allows for innovation while still ensuring a strong assurance process.
Definitions
- System assurance: The process of making sure that a system works correctly and meets certain standards or requirements.
- Autonomous systems: Systems that can operate on their own without human control or intervention.
- Machine learning: A type of artificial intelligence where computers can learn from data and improve their performance without being explicitly programmed.
- AI (Artificial Intelligence): The simulation of human intelligence processes by machines, especially computer systems.
- Ultra-rapid: Very fast or quick.
- Efficient: Doing something in a way that saves time, effort, or resources.
- Effective: Producing the desired result or having an intended impact.
- Timely: Happening at the right moment or within an appropriate time frame.
- Hindrances: Things that cause problems or difficulties; obstacles.
- Cost-consuming: Requiring a lot of money to implement or maintain.
- Modernized framework: An updated or improved way of doing something, in this
Assurance 2.0: A Manifesto
In their paper titled "Assurance 2.0: A Manifesto," authors Robin Bloomfield and John Rushby address the challenges faced by system assurance in the modern era. As technology continues to advance, these challenges become more complex and require new solutions. In this article, we will discuss the two main challenges highlighted by Bloomfield and Rushby, as well as their proposed solution of Assurance 2.0 – a modernized framework for system assurance that aims to enable innovation while ensuring continuous incremental assurance.
The Challenges Faced by System Assurance
Bloomfield and Rushby highlight two main challenges posed by today's rapidly changing technological landscape: the emergence of autonomous systems driven by machine learning and AI, and the need for ultra-rapid system development. These challenges are accompanied by persistent issues such as the requirement for efficient, effective, and timely assurance – something traditional methods often fail to provide due to their perceived high cost and time-consuming nature.
Introducing Assurance 2.0
To address these problems, Bloomfield and Rushby propose a modernized framework called Assurance 2.0 which enables innovation while ensuring continuous incremental assurance through rigorous practices such as explicit identification of defeaters (factors that can undermine confidence) and counterevidence (evidence contradicting expected outcomes). This approach is designed to support innovation while maintaining a robust assurance process without sacrificing efficiency or effectiveness.
Conclusion
Overall, Bloomfield and Rushby's manifesto presents Assurance 2.0 as an enabler rather than a hindrance to innovation in system assurance. It offers a modernized framework that addresses the challenges posed by autonomous systems and rapid development, making assurance more rigorous through enhanced focus on reasoning, evidence, defeaters, and counterevidence – ultimately enabling greater efficiency with improved results compared to traditional methods of system assurance
