Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

· 10) Value Alignment

Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

Principle: Asilomar AI Principles, Jan 3-8, 2017

Published by Future of Life Institute (FLI), Beneficial AI 2017

Related Principles

Human centred values

Throughout their lifecycle, AI systems should respect human rights, diversity, and the autonomy of individuals. This principle aims to ensure that AI systems are aligned with human values. Machines should serve humans, and not the other way around. AI systems should enable an equitable and democratic society by respecting, protecting and promoting human rights, enabling diversity, respecting human freedom and the autonomy of individuals, and protecting the environment. Human rights risks need to be carefully considered, as AI systems can equally enable and hamper such fundamental rights. It’s permissible to interfere with certain human rights where it’s reasonable, necessary and proportionate. All people interacting with AI systems should be able to keep full and effective control over themselves. AI systems should not undermine the democratic process, and should not undertake actions that threaten individual autonomy, like deception, unfair manipulation, unjustified surveillance, and failing to maintain alignment between a disclosed purpose and true action. AI systems should be designed to augment, complement and empower human cognitive, social and cultural skills. Organisations designing, developing, deploying or operating AI systems should ideally hire staff from diverse backgrounds, cultures and disciplines to ensure a wide range of perspectives, and to minimise the risk of missing important considerations only noticeable by some stakeholders.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

Reliability and safety

Throughout their lifecycle, AI systems should reliably operate in accordance with their intended purpose. This principle aims to ensure that AI systems reliably operate in accordance with their intended purpose throughout their lifecycle. This includes ensuring AI systems are reliable, accurate and reproducible as appropriate. AI systems should not pose unreasonable safety risks, and should adopt safety measures that are proportionate to the magnitude of potential risks. AI systems should be monitored and tested to ensure they continue to meet their intended purpose, and any identified problems should be addressed with ongoing risk management as appropriate. Responsibility should be clearly and appropriately identified, for ensuring that an AI system is robust and safe.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

II. Technical robustness and safety

Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of the AI system, and to adequately cope with erroneous outcomes. AI systems need to be reliable, secure enough to be resilient against both overt attacks and more subtle attempts to manipulate data or algorithms themselves, and they must ensure a fall back plan in case of problems. Their decisions must be accurate, or at least correctly reflect their level of accuracy, and their outcomes should be reproducible. In addition, AI systems should integrate safety and security by design mechanisms to ensure that they are verifiably safe at every step, taking at heart the physical and mental safety of all concerned. This includes the minimisation and where possible the reversibility of unintended consequences or errors in the system’s operation. Processes to clarify and assess potential risks associated with the use of AI systems, across various application areas, should be put in place.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

Third principle: Understanding

AI enabled systems, and their outputs, must be appropriately understood by relevant individuals, with mechanisms to enable this understanding made an explicit part of system design. Effective and ethical decision making in Defence, from the frontline of combat to back office operations, is always underpinned by appropriate understanding of context by those making decisions. Defence personnel must have an appropriate, context specific understanding of the AI enabled systems they operate and work alongside. This level of understanding will naturally differ depending on the knowledge required to act ethically in a given role and with a given system. It may include an understanding of the general characteristics, benefits and limitations of AI systems. It may require knowledge of a system’s purposes and correct environment for use, including scenarios where a system should not be deployed or used. It may also demand an understanding of system performance and potential fail states. Our people must be suitably trained and competent to operate or understand these tools. To enable this understanding, we must be able to verify that our AI enabled systems work as intended. While the ‘black box’ nature of some machine learning systems means that they are difficult to fully explain, we must be able to audit either the systems or their outputs to a level that satisfies those who are duly and formally responsible and accountable. Mechanisms to interpret and understand our systems must be a crucial and explicit part of system design across the entire lifecycle. This requirement for context specific understanding based on technically understandable systems must also reach beyond the MOD, to commercial suppliers, allied forces and civilians. Whilst absolute transparency as to the workings of each AI enabled system is neither desirable nor practicable, public consent and collaboration depend on context specific shared understanding. What our systems do, how we intend to use them, and our processes for ensuring beneficial outcomes result from their use should be as transparent as possible, within the necessary constraints of the national security context.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022

Fifth principle: Reliability

AI enabled systems must be demonstrably reliable, robust and secure. The MOD’s AI enabled systems must be suitably reliable; they must fulfil their intended design and deployment criteria and perform as expected, within acceptable performance parameters. Those parameters must be regularly reviewed and tested for reliability to be assured on an ongoing basis, particularly as AI enabled systems learn and evolve over time, or are deployed in new contexts. Given Defence’s unique operational context and the challenges of the information environment, this principle also requires AI enabled systems to be secure, and a robust approach to cybersecurity, data protection and privacy. MOD personnel working with or alongside AI enabled systems can build trust in those systems by ensuring that they have a suitable level of understanding of the performance and parameters of those systems, as articulated in the principle of understanding.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022