AI for good
In plain wordsUsing AI to tackle social and environmental problems, such as disease, poverty, disasters or climate change, rather than only to make a profit.
The application of AI to social, humanitarian and environmental goals, often framed by the United Nations Sustainable Development Goals: for example disease screening and drug discovery, crop and weather forecasting, disaster mapping from satellite imagery, wildlife conservation, accessibility tools and the targeting of public services. The label also names programs by companies, foundations and UN agencies that fund such projects or donate expertise and computing. Critics note that many projects never progress beyond pilots, that 'good' is often defined by funders rather than affected communities, that the framing can serve public relations (ethics washingethics washing, n.: Using talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does.), and that technical fixes can displace political or structural ones. Practitioners stress partnership with domain experts and affected communities, maintenance after launch, and rigorous evidence of impact.
The AI for good grant paid for the flood-mapping model but not for anyone to maintain it after the pilot.
Origin
'AI for social good' circulated in academic workshops from the mid-2010s. The International Telecommunication Union launched the AI for Good Global Summit with the XPRIZE Foundation in Geneva in 2017, and corporate programs followed, including Microsoft's AI for Good and Google's AI for Social Good (both 2018). Luciano Floridi and colleagues proposed design factors for AI4SG in 2020.
Related
- AI ethicsAI ethics, n.: The study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
- responsible AIresponsible AI, n.: An umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use.
- ethics washingethics washing, n.: Using talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does.
- AI colonialismAI colonialism, n.: The criticism that the AI industry repeats patterns of colonial empires, taking data, labor, land and resources from poorer places and peoples while profits and control stay with a few rich companies and countries.
- digital dividedigital divide, n.: The gap between people, places or countries that have good access to digital technology, and the skills to use it, and those that do not; AI may widen it.
- human-centered AIhuman-centered AI, n.: An approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them.
Sources
- Floridi, L., Cowls, J., King, T. C. & Taddeo, M. (2020). How to Design AI for Social Good: Seven Essential Factors. Science and Engineering Ethics 26, 1771–1796.
Mentioned in
AI colonialismAI colonialism, n.: The criticism that the AI industry repeats patterns of colonial empires, taking data, labor, land and resources from poorer places and peoples while profits and control stay with a few rich companies and countries., digital dividedigital divide, n.: The gap between people, places or countries that have good access to digital technology, and the skills to use it, and those that do not; AI may widen it., ethics washingethics washing, n.: Using talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does., techno-solutionismtechno-solutionism, n.: The belief that technology, such as a clever app or algorithm, can fix complicated social problems on its own, overlooking their political, economic and human causes., small AIsmall AI, n.: Cheap, compact AI tools built for one practical job — like advising farmers or screening patients — that poorer countries can afford to run, instead of giant all-purpose models.