Aurelie Jacquet | Searcharxiv

arXiv2023

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

Responsible AI is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of 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. Also, 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 responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). 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 Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement responsible AI.

cs.AI↗

arXiv2022

A Trust Framework for Government Use of Artificial Intelligence and Automated Decision Making

This paper identifies the current challenges of the mechanisation, digitisation and automation of public sector systems and processes, and proposes a modern and practical framework to ensure and assure ethical and high veracity Artificial Intelligence (AI) or Automated Decision Making (ADM) systems in public institutions. This framework is designed for the specific context of the public sector, in the jurisdictional and constitutional context of Australia, but is extendable to other jurisdictions and private sectors. The goals of the framework are to: 1) earn public trust and grow public confidence in government systems; 2) to ensure the unique responsibilities and accountabilities (including to the public) of public institutions under Administrative Law are met effectively; and 3) to assure a positive human, societal and ethical impact from the adoption of such systems. The framework could be extended to assure positive environmental or other impacts, but this paper focuses on human/societal outcomes and public trust. This paper is meant to complement principles-based frameworks like Australia's Artificial Intelligence Ethics Framework and the EU Assessment List for Trustworthy AI. In many countries, COVID created a bubble of improved trust, a bubble which has arguably already popped, and in an era of unprecedented mistrust of public institutions (but even in times of high trust) it is not enough that a service is faster, or more cost-effective. This paper proposes recommendations for government systems (technology platforms, operations, culture, governance, engagement, etc.) that would help to improve public confidence and trust in public institutions, policies and services, whilst meeting the special obligations and responsibilities of the public sector.

cs.CY↗