Linking Artificial Intelligence Principles (LAIP)

· Accountability

People and corporations who design and deploy AI systems must be accountable for how their systems are designed and operated. The development of AI must be responsible, safe and useful. AI must maintain the legal status of tools, and legal persons need to retain control over, and responsibility for, these tools at all times. Workers, job applicants and ex workers must also have the “right of explanation” when AI systems are used in human resource procedures, such as recruitment, promotion or dismissal. They should also be able to appeal decisions by AI and have them reviewed by a human.

Principle: Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

Published by Centre for International Governance Innovation (CIGI), Canada

Related Principles

Accountability

Those responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled. This principle aims to acknowledge the relevant organisations' and individuals’ responsibility for the outcomes of the AI systems that they design, develop, deploy and operate. The application of legal principles regarding accountability for AI systems is still developing. Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. This includes both before and after their design, development, deployment and operation. The organisation and individual accountable for the decision should be identifiable as necessary. They must consider the appropriate level of human control or oversight for the particular AI system or use case. AI systems that have a significant impact on an individual's rights should be accountable to external review, this includes providing timely, accurate, and complete information for the purposes of independent oversight bodies.

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

Ensure “Interpretability” of AI systems

Principle: Decisions made by an AI agent should be possible to understand, especially if those decisions have implications for public safety, or result in discriminatory practices. Recommendations: Ensure Human Interpretability of Algorithmic Decisions: AI systems must be designed with the minimum requirement that the designer can account for an AI agent’s behaviors. Some systems with potentially severe implications for public safety should also have the functionality to provide information in the event of an accident. Empower Users: Providers of services that utilize AI need to incorporate the ability for the user to request and receive basic explanations as to why a decision was made.

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

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

Ensuring Accountability

Principle: Legal accountability has to be ensured when human agency is replaced by decisions of AI agents. Recommendations: Ensure legal certainty: Governments should ensure legal certainty on how existing laws and policies apply to algorithmic decision making and the use of autonomous systems to ensure a predictable legal environment. This includes working with experts from all disciplines to identify potential gaps and run legal scenarios. Similarly, those designing and using AI should be in compliance with existing legal frameworks. Put users first: Policymakers need to ensure that any laws applicable to AI systems and their use put users’ interests at the center. This must include the ability for users to challenge autonomous decisions that adversely affect their interests. Assign liability up front: Governments working with all stakeholders need to make some difficult decisions now about who will be liable in the event that something goes wrong with an AI system, and how any harm suffered will be remedied.

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

4. Adopt a Human In Command Approach

An absolute precondition is that the development of AI must be responsible, safe and useful, where machines maintain the legal status of tools, and legal persons retain control over, and responsibility for, these machines at all times. This entails that AI systems should be designed and operated to comply with existing law, including privacy. Workers should have the right to access, manage and control the data AI systems generate, given said systems’ power to analyse and utilize that data (See principle 1 in “Top 10 principles for workers’ data privacy and protection”). Workers must also have the ‘right of explanation’ when AI systems are used in human resource procedures, such as recruitment, promotion or dismissal.

Published by UNI Global Union in Top 10 Principles For Ethical Artificial Intelligence, Dec 11, 2017