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

· Deception

No AI system should be able to consistently cause its designers or regulators to misunderstand its likelihood or capability to cross any of the preceding red lines.

Principle: IDAIS-Beijing, May 10, 2024

Published by IDAIS (International Dialogues on AI Safety)

Related Principles

· 4. Governance of AI Autonomy (Human oversight)

The correct approach to assuring properties such as safety, accuracy, adaptability, privacy, explicability, compliance with the rule of law and ethical conformity heavily depends on specific details of the AI system, its area of application, its level of impact on individuals, communities or society and its level of autonomy. The level of autonomy results from the use case and the degree of sophistication needed for a task. All other things being equal, the greater degree of autonomy that is given to an AI system, the more extensive testing and stricter governance is required. It must be ensured that AI systems continue to behave as intended when feedback signals become sparser. Depending on the area of application and or the level of impact on individuals, communities or society of the AI system, different levels or instances of governance (incl. human oversight) will be necessary. This is relevant for a large number of AI applications, and more particularly for the use of AI to suggest or take decisions concerning individuals or communities (algorithmic decision support). Good governance of AI autonomy in this respect includes for instance more or earlier human intervention depending on the level of societal impact of the AI system. This also includes the predicament that a user of an AI system, particularly in a work or decision making environment, is allowed to deviate from a path or decision chosen or recommended by the AI system.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

· 8. Robustness

Trustworthy AI requires that algorithms are secure, reliable as well as robust enough to deal with errors or inconsistencies during the design, development, execution, deployment and use phase of the AI system, and to adequately cope with erroneous outcomes. Reliability & Reproducibility. Trustworthiness requires that the accuracy of results can be confirmed and reproduced by independent evaluation. However, the complexity, non determinism and opacity of many AI systems, together with sensitivity to training model building conditions, can make it difficult to reproduce results. Currently there is an increased awareness within the AI research community that reproducibility is a critical requirement in the field. Reproducibility is essential to guarantee that results are consistent across different situations, computational frameworks and input data. The lack of reproducibility can lead to unintended discrimination in AI decisions. Accuracy. Accuracy pertains to an AI’s confidence and ability to correctly classify information into the correct categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well formed development and evaluation process can support, mitigate and correct unintended risks. Resilience to Attack. AI systems, like all software systems, can include vulnerabilities that can allow them to be exploited by adversaries. Hacking is an important case of intentional harm, by which the system will purposefully follow a different course of action than its original purpose. If an AI system is attacked, the data as well as system behaviour can be changed, leading the system to make different decisions, or causing the system to shut down altogether. Systems and or data can also become corrupted, by malicious intention or by exposure to unexpected situations. Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm. Fall back plan. A secure AI has safeguards that enable a fall back plan in case of problems with the AI system. In some cases this can mean that the AI system switches from statistical to rule based procedure, in other cases it means that the system asks for a human operator before continuing the action.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

Responsible Deployment

Principle: The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring. Recommendations: Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off switch”). There may also be a need to incorporate human checks on new decision making strategies in AI system design, especially where the risk to human life and safety is great. Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed. Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices. Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading. If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware infected AI systems that could become the next generation of botnets. High standards of device, system and network security should be applied. Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

Principle 5 – Reliability & Safety

The reliability and safety principle ensures that the AI system adheres to the set specifications and that the AI system behaves exactly as its designers intended and anticipated. Reliability is a measure of consistency and provides confidence in how robust a system is. It is a measure of dependability with which it operationally conforms to its intended functionality and the outcomes it produces. On the other hand, safety is a measure of how the AI system does not pose a risk of harm or danger to society and individuals. As an illustration, AI systems such as autonomous vehicles can pose a risk to people’s lives if living organisms are not properly recognized, certain scenarios are not trained for or if the system malfunctions. A reliable working system should be safe by not posing a danger to society and should have built in mechanisms to prevent harm. The risk mitigation framework is closely related to this principle. Potential risks and unintended harms should be minimized in this aspect. The predictive model should be monitored and controlled in a periodic and continuous manner to check if its operations and functionality are aligned with the designed structure and frameworks in place. The AI system should be technically sound, robust, and developed to prevent malicious usage to exploit its data and outcomes to harm entities, individuals or communities. A continuous implementation continuous development approach is essential to ensure reliability.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

Fourth principle: Bias and Harm Mitigation

Those responsible for AI enabled systems must proactively mitigate the risk of unexpected or unintended biases or harms resulting from these systems, whether through their original rollout, or as they learn, change or are redeployed. AI enabled systems offer significant benefits for Defence. However, the use of AI enabled systems may also cause harms (beyond those already accepted under existing ethical and legal frameworks) to those using them or affected by their deployment. These may range from harms caused by a lack of suitable privacy for personal data, to unintended military harms due to system unpredictability. Such harms may change over time as systems learn and evolve, or as they are deployed beyond their original setting. Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets. Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion. A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms. This includes addressing bias in algorithmic decision making, carefully curating and managing datasets, setting safeguards and performance thresholds throughout the system lifecycle, managing environmental effects, and applying strict development criteria for new systems, or existing systems being applied to a new context.

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