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Non-Model mitigations not clearly fitting above categories.
Also in Non-Model
centive mechanisms are effective treatments in motivating AI systems and encouraging the stakeholders involved in the AI system ecosystem to execute tasks in a responsible manner. An incentive registry records the rewards that correspond to the AI system’s ethical behavior and outcome of decisions [121, 125] (e.g., rewards for path planning without ethical risks). There are various ways to formulate the incentive mechanism, such as using reinforcement learning or building the incentive mechanism on a publicly accessible data infrastructure like blockchain [125]. Traditional incentive mechanisms for human participants include reputation based and payment based.
However, it is challenging to formulate the form of rewards in the context of RAI, as the ethical impact of AI systems’ decisions and behaviors might hardly be measured for some of the ethical principles (e.g., human values). Furthermore, the incentive mechanism needs to be agreed on by all stakeholders, who may have different views on the ethical impact. In addition, there may be tradeoffs between different principles, which makes the design harder. The Open Science Rewards and Incentives Registry117 incentivizes the development of an academic career structure that fosters outputs, practices, and behaviors to maximize contributions to a shared research knowledge system. FLoBC118 is a tool for federated learning over blockchain that utilizes a reward/punishment policy to incentivize legitimate training, and to punish and hinder malicious trainers
Reasoning
Cross-stakeholder incentive registry coordinates voluntary ethical AI commitments through shared reward mechanisms.
Governance Patterns
The governance for RAI systems can be defined as the structures and processes that are employed to ensure that the development and use of AI systems meet AI ethics principles. According to the structure of Shneiderman [104], governance can be built at three levels: industry level, organization level, and team level.
2.1 Oversight & AccountabilityGovernance Patterns > Industry-level governance patterns
3.1 Legal & RegulatoryGovernance Patterns > Organization-level governance patterns
2.1 Oversight & AccountabilityGovernance Patterns > Team-level governance patterns
2.1.2 Roles & AccountabilityProcess Patterns
The process patterns are reusable methods and best practices that can be used by the development team during the development process.
2.4.2 Design StandardsProcess Patterns > Requirement Engineering
2.4 Engineering & DevelopmentResponsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Lu, Qinghua; Zhu, Liming; Xu, Xiwei; Whittle, Jon; Zowghi, Didar; Jacquet, Aurelie (2024)
Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been placed at algorithm level rather than system level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI. © 2024 Copyright held by the owner/author(s).
Build and Use Model
Training, fine-tuning, and integrating the AI model
Developer
Entity that creates, trains, or modifies the AI system
Manage
Prioritising, responding to, and mitigating AI risks
