Artificial intelligence and the climate emergency: Opportunities, challenges, and recommendations

Artificial intelligence (AI) has the potential to play an important role in addressing the climate emergency, but this potential must be set against the environmental costs of developing AI systems. In this commentary, we assess the carbon footprint of AI training processes and offer 14 policy recommendations to reduce it.

A new commentary by Mariarosaria Taddeo, Andreas Tsamados, myself and Luciano Floridi has been published in Cell — One Earth.

A definition, benchmark and database of AI for social good initiatives

Initiatives relying on artificial intelligence (AI) to deliver socially beneficial outcomes—AI for social good (AI4SG)—are on the rise. However, existing attempts to understand and foster AI4SG initiatives have so far been limited by the lack of normative analyses and a shortage of empirical evidence. In this Perspective, we address these limitations by providing a definition of AI4SG and by advocating the use of the United Nations’ Sustainable Development Goals (SDGs) as a benchmark for tracing the scope and spread of AI4SG.

A new “perspective” paper by myself, Andreas Tsamados, Mariarosaria Taddeo and Luciano Floridi has recently been published in Nature: Machine Intelligence.

How to Design AI for Social Good: Seven Essential Factors

A new paper I co-authored with Luciano Floridi, Thomas C. King and Mariarosaria Taddeo has been published (open access) in Science and Engineering Ethics.

Abstract:

The idea of artificial intelligence for social good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.

Big Data and Positive Change in the Developing World: Challenges and Opportunities

Taylor, Linnet, Cowls, Josh, Schroeder, Ralph and Eric T. Meyer (2014). Big Data and Positive Change in the Developing World: Challenges and Opportunities. Policy & Internet 6 (4), pp. 418-444.

This paper is the product of a workshop that brought together practitioners, researchers, and data experts to discuss how big data is becoming a resource for positive social change in low- and middle-income countries (LMICs). We include in our definition of big data sources such as social media data, mobile phone use records, digitally mediated transactions, online news media sources, and administrative records. We argue that there are four main areas where big data has potential for promoting positive social change: advocacy; analysis and prediction; facilitating information exchange; and promoting accountability and transparency. These areas all have particular challenges and possibilities, but there are also issues shared across them, such as open data and privacy concerns. Big data is shaping up to be one of the key battlefields of our time, and the paper argues that this is therefore an opportune moment for civil society groups in particular to become a larger part of the conversation about the use of big data, since questions about the asymmetries of power involved are especially urgent in these uses in LMICs. Civil society groups are also currently underrepresented in debates about privacy and the rights of technology users, which are dominated by corporations, governments and nongovernmental organizations in the Global North. We conclude by offering some lessons drawn from a number of case studies that represent the current state-of-the-art.