How to Design AI for Social Good: Seven Essential Factors
By Luciano Floridi, Josh Cowls, Thomas Christopher King, Mariarosaria Taddeo. Science and Engineering Ethics, 2020
337 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Informatics, Safety Research
View Paper: https://www.semanticscholar.org/paper/7eb03a3c0605d688959eef30518f6eb268996eae
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jessica Morley
- Josh Cowls
- Luciano Floridi
- Mariarosaria Taddeo
- Andreas Tsamados
- Nikita Aggarwal
- Huw Roberts
- Arjan Durresi
In Health Informatics
In Safety Research
- Jessica Morley
- Luciano Floridi
- Josh Cowls
- Mariarosaria Taddeo
- Andreas Tsamados
- Nikita Aggarwal
- Huw Roberts
- Arjan Durresi
Citations in the corpus
- Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2023, background): “[63,244,299,300]) in urban life domains in emerging smarter eco-cities.”
- Trustworthy Artificial Intelligence: A Review (2022, background): “Over the past few years, research institutes, private organizations, and government agencies have proposed various guidelines and frameworks to make AI trustworthy [60, 68, 69, 154, 198, 224].”
- The ethics of algorithms: key problems and solutions (2020, methodology): “2019a, b), financial institutions (Lee and Floridi 2020; Aggarwal 2020) courts (Green and Chen 2019; Yu and Du 2019), local governmental bodies (Eubanks 2017; Lewis 2019), and national governments (Labati et al.”