AI for social good: unlocking the opportunity for positive impact

 Abstract

Advances in machine learning (ML) and artificial perspicacity (AI) present an opportunity to build better implements and solutions to avail address some of the world’s most pressing challenges, and distribute positive gregarious impact in accordance with the priorities outlined in the Cumulated Nations’ 17 Sustainable Development Goals (SDGs). The AI for Gregarious Good (AI4SG) kineticism aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing prosperous long-term collaborations between AI researchers and application-domain experts, relate them to subsisting AI4SG projects and identify key opportunities for future AI applications targeted towards gregarious good.

Exordium

The challenges facing our world today have grown in intricacy and increasingly require immensely colossal, coordinated efforts: between countries; and across a broad spectrum of governmental and non-governmental organisations (NGOs) and the communities they accommodate. These coordinated efforts work towards fortifying the Sustainable Development Goals (SDGs)1, and there perpetuates to be a paramount role for technology to fortify the developmental organisations and efforts active in this field to distribute the highest impact.

Artificial perspicacity (AI) and machine learning (ML) have magnetized widespread interest in recent years due to a series of high-profile successes. AI has shown prosperity in games and simulations2,3, and is being increasingly applied to a wide range of practical quandaries, including verbalization recognition4 and self-driving cars5. These commercial applications often have indirect positive convivial impact by incrementing the availability of information through better search and language-translation implements, providing amended communication accommodations, enabling more efficient conveyance, or fortifying more personalised healthcare6. With this interest come a plethora of questions regarding convivial impact, malevolent uses, jeopardies, and governance of these innovations, which are of foremost importance7,8.

Targeted applications of AI to the domain of convivial good have recently come into focus. This field has magnetized many actors, including charities like DataKind (established in 2012)9, academic programmes such as the Data Science for Convivial Good (DSSG) programme at the University of Chicago (established in 2013)10, the UN Ecumenical Pulse Labs11, AI for Gregarious Good workshops in conferences such as the 2018 and 2019 NeurIPS conference12,13, the 2019 ICML conference14 and the 2019 ICLR conference15, along with corporate funding programmes such as Google AI for Good Grants16, Microsoft AI for Humanity17, Mastercard Center for Inclusive Magnification and the Rockefeller Foundation’s Data Science for Convivial Impact18, amongst several others.

Results from several recent studies hint at the potential benefits of utilizing AI for gregarious good. Amnesty International and ElementAI demonstrated how AI can be habituated to avail trained human mitigators with identifying and quantifying online abuse against women on Twitter19. The Makerere University AI research group fortified by the UN Pulse Lab Kampala developed automated monitoring of viral cassava disease20, and this same group collaborated with Microsoft Research and other academic institutions to establish an electronic agricultural rialto in Uganda21. Satellite imagery was acclimated to avail soothsay poverty22 and identify burned-down villages in conflict zones in Darfur23, and collaborative efforts between climate and machine learning scientists initiated the field of climate informatics24,25 that perpetuates to advance predictive and interpretive implements for climate action. Future ameliorations in both data infrastructure and AI technology can be expected to lead to an even more diverse set of potential AI4SG applications.

This affluence of projects, sometimes isolated, has led to several meta-initiatives. For example, the Oxford Initiative on AIxSDGs26, launched in September 2019, is a curated database of AI projects addressing SDGs that indexes proximate to 100 projects. Once publicly accessible, it should fortify a formal study of such projects’ characteristics, prosperity factors, geographical repartition, gaps, and collaborations. Endeavors at homogeneous repositories include the ITU AI Repository27. Another growing initiative, fixated on networking AI4SG and making their blueprints facilely accessible and reproducible by anyone, is the AI Commons cognizance hub28 backed by 21 fortifying organisations and 71 members. These meta-initiatives can avail aggregate the experience and transfer cognizance between AI4SG projects, as well as establish connections between teams and organisations with complementary aims.

Despite the optimism, technical and organisational challenges remain that make prosperous applications of AI/ML hard to distribute within the field and that make it arduous to achieve lasting impact. Some of the issues are deeply ingrained in the tech culture that involves moving expeditious and breaking things while iterating towards solutions, and a lack of familiarity with the non-technical aspects of the problems29. There is withal a long history of tech for good, including 30 years of Information and Communication Technology for Development (ICT4D). Not all applications of technology aimed at distributing positive convivial impact manage to achieve their goals30, leaving us with consequential experiences from which we must learn. Importantly, technology should not be imagined as a solution on its own31, outside of the context of its application: it merely aligns with human intent and magnifies human capacity32. It is ergo critical to put it in accommodation of application-domain experts early, through deep partnerships with technical experts.

To achieve positive impact, AI solutions need to adhere to ethical principles and both the European Commission33 as well as OECD34 have put together guidelines for developing innovative and trustworthy AI. Cognate principles are encoded in the Montreal Declaration for Responsible AI35 and the Toronto Declaration36. The European Commission states that AI needs to be lawful, ethical and robust, to evade causing unintended harm. OECD Principles on AI state that AI should be driving inclusive magnification and sustainable development; designed so as to revere the rule of law, human rights, democratic values and diversity; transparent, so that people can understand AI outcomes; robust, safe and secure; deployed with accountability, so that organisations can be held responsible for AI systems they develop and use. Opportune ethical design and governance of AI systems is a broad research topic of fundamental paramountcy, and has been the focus of institutions and initiatives like the AI Now Institute37 and the ACM Conference on Fairness, Accountability and Transparency38.

Withal, it is consequential to recognise the interconnectedness of the Sustainable Development Goals (SDGs) and of efforts to achieve them. The UN stresses that each goal needs to be achieved so that no one is left behind. Yet, an intervention with a positive impact on one SDG could be detrimental to another SDG and its targets. Cognizance of this interconnectedness should additionally be a driving principle for fair and inclusive AI for gregarious good: AI applications should aim to maximise a net positive effect on as many SDGs as possible, without causing avoidable harm to other SDGs. Consequently, while being punctilious to evade the pitfalls of analysis paralysis39, both application-domain experts and AI researchers should aspire to quantify the effects, both positive and negative, of their AI for gregarious good applications across the five areas of people, planet, prosperity, tranquility and partnerships, which are the targets of the sustainable development agenda.

A recent UN report40 details how over 30 of its agencies and bodies are working towards integrating AI within their initiatives. According to the report, AI4SG projects need to be approached as a collaborative effort, bringing communities together to conscientiously assess the involutions of designing AI systems for SDGs. These initiatives should aim to involve NGOs, local ascendant entities, businesses, the academic community, as well as the communities which these efforts support. The report highlights the astronomical potential of the technology across a wide spectrum of applications, while recognising the desideratum for ameliorating data literacy and a responsible approach to AI research and deployment. Our own efforts to put these considerations into practice have led us to put forward in the next section a set of guidelines with which to approach AI4SG, which we then exemplify with a set of case studies afore concluding with a call to action for technical communities and their consequential role in fortifying the prosperity of our gregarious and ecumenical goals.

Guidelines for AI4SG collaborations

To address the challenges involved with establishing prosperous collaborations between AI researchers and application-domain experts working on SDGs, we facilitated a series of structured multidisciplinary discussions at a dedicated seminar41 assembling experts from both communities to identify key aspects of prosperous partnerships, and potential obstacles. This process involved establishing focused working groups around key topics and perpetually converging to disseminate the results, obtain feedback and discuss within the wider group. We present the conclusions in the form of guidelines to apprise future AI4SG initiatives and ground our recommendations in practical examples of prosperous AI4SG collaborations.

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