Fitsilis, F., von Lucke, J. and Vrieze, F. (eds.), Westminster Foundation for Democracy (WFD), 2024.

These AI guidelines for parliaments not only promote responsible AI use but also empower parliaments to mitigate the risks of AI whilst leveraging AI’s potential to strengthen their functions and serve citizens better. They were developed by a technical working group of 22 expert parliamentary scholars and professionals from 16 countries.

Ryan S. Baker, Aaron Hawn and Seiyon Lee, in: 'OECD Digital Education Outlook 2023', OECD Publishing, 2023.

This chapter discusses the current state of the evidence on algorithmic bias in education. After defining algorithmic bias and its possible origins, it reviews the existing international evidence about algorithmic bias in education, which has focused on gender and race, but has also involved some other demographic categories. The chapter concludes with a few recommendations, notably to ensure that privacy requirements do not prevent researchers and developers from identifying bias, so that it can be addressed.

Stijn Broecke, OECD Publishing, 2023.

While still in its infancy, Artificial Intelligence (AI) is increasingly used in labour market matching, whether by private recruiters, public and private employment services, or online jobs boards and platforms. Applications range from writing job descriptions, applicant sourcing, analysing CVs, chat bots, interview schedulers, shortlisting tools, all the way to facial and voice analysis during interviews. While many tools promise to bring efficiencies and cost savings, they could also improve the quality of matching and jobseeker experience, and even identify and mitigate human bias. There are nonetheless some barriers to a greater adoption of these tools. Some barriers relate to organisation and people readiness, while others reflect concerns about the technology and how it is used, including: robustness, bias, privacy, transparency and explainability. The present paper reviews the literature and some recent policy developments in this field, while bringing new evidence from interviews held with key stakeholders.

Angelica Salvi del Peroi and Annelore Verhagen, in 'OECD Employment Outlook 2023', OECD Publishing, 2023.

This chapter provides an overview of countries’ policy action affecting the development and use of artificial intelligence (AI) in the workplace. It looks at public policies to protect workers’ fundamental rights, ensure transparency and explainability of AI systems, and clarify accountability across the AI value chain. It explores how existing non-AI-specific laws – such as those pertaining anti-discrimination and data protection – can serve as a foundation for the governance of AI used in workplace settings. While in some countries, courts have successfully applied these laws to AI-related cases in the workplace, there may be a need for AI- and workplace‑specific policies. To date, most countries primarily rely on soft law for AI-specific matters, but a number of countries are developing new AI-specific legislative proposals applicable to AI in the workplace.

Ghiath Shabsigh and El Bachir Boukherouaa, International Monetary Fund, 2023.

In recent years, technological advances and competitive pressures have fueled rapid adoption of artificial intelligence (AI) in the financial sector, and this adoption is set to accelerate with the recent emergence of generative AI (GenAI). GenAI is a significant leap forward in AI technology that enhances its utility for financial institutions that have been quick at adapting it to a broad range of applications. However, there are risks inherent in the AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. GenAI could aggravate some of these risks and bring about new types or risks as well, including for financial sector stability. This paper provides early insights into GenAI’s inherent risks and their potential impact on the financial sector.

Francesco Filippucci, et al., OECD Publishing, 2024.

This paper explores the economics of Artificial Intelligence (AI), focusing on its potential as a new General-Purpose Technology that can significantly influence economic productivity and societal wellbeing. It examines AI's unique capacity for autonomy and self-improvement, which could accelerate innovation and potentially revive sluggish productivity growth across various industries, while also acknowledging the uncertainties surrounding AI's long-term productivity impacts. The paper discusses the concentration of AI development in big tech firms, uneven adoption rates, and broader societal challenges such as inequality, discrimination, and security risks. It calls for a comprehensive policy approach to ensure AI's beneficial development and diffusion, including measures to promote competition, enhance accessibility, and address job displacement and inequality.

Philippe Lorenz, Karine Perset and Jamie Berryhill, OECD Publishing, 2023.

Generative artificial intelligence (AI) creates new content in response to prompts, offering transformative potential across multiple sectors such as education, entertainment, healthcare and scientific research. However, these technologies also pose critical societal and policy challenges that policy makers must confront: potential shifts in labour markets, copyright uncertainties, and risk associated with the perpetuation of societal biases and the potential for misuse in the creation of disinformation and manipulated content. Consequences could extend to the spreading of mis- and disinformation, perpetuation of discrimination, distortion of public discourse and markets, and the incitement of violence. Governments recognise the transformative impact of generative AI and are actively working to address these challenges. This paper aims to inform these policy considerations and support decision makers in addressing them.

Annelore Verhagen, OECD Publishing, 2024.

While means-tested benefits such as minimum income benefits (MIB) and unemployment assistance (UA) are an essential safety net for low-income people and the unemployed, incomplete take-up is the rule rather than the exception. Building on desk research, open-ended surveys and semi-structured interviews, this paper investigates the opportunities and risks of using artificial intelligence (AI) for managing these means-tested benefits. This ranges from providing information to individuals, through determining eligibility based on pre-determined statutory criteria and identifying undue payments, to notifying individuals about their eligibility status. One of the key opportunities of using AI for these purposes is that this may improve the timeliness and take-up of MIB and UA. However, it may also lead to systematically biased eligibility assessments or increase inequalities, amongst others. Finally, the paper explores potential policy directions to help countries seize AI’s opportunities while addressing its risks, when using it for MIB or UA management.