by Michael Chui, Martin Harrysson, James Manyika, Roger Roberts, Rita Chung, Peter Nel, and Ashley van Heteren.
Points to Highlight
● “Artificial intelligence (AI) has the potential to help tackle some of the world’s most challenging social problems. To analyze potential applications for social good, we compiled a library of about 160 AI social-impact use cases. They suggest that existing capabilities could contribute to tackling cases across all 17 of the UN’s sustainable-development goals, potentially helping hundreds of millions of people in both advanced and emerging countries.”
● “Addressing challenges to equality, inclusion, and self-determination (such as reducing or eliminating bias based on race, sexual orientation, religion, citizenship, and disabilities) are issues in this domain. One use case, based on work by Affectiva, which was spun out of the MIT Media Lab, and Autism Glass, a Stanford research project, involves using AI to automate the recognition of emotions and to provide social cues to help individuals along the autism spectrum interact in social environments”.
● “Some of these use cases consist of tasks a human being could potentially accomplish on an individual level, but the required number of instances is so large that it exceeds human capacity (for example, finding flooded or unusable roads across a large area after a hurricane). In other cases, an AI system can be more accurate than humans, often by processing more information (for example, the early identification of plant diseases to prevent infection of the entire crop).”
● “Data accessibility remains a significant challenge. Resolving it will require a willingness, by both private- and public-sector organizations, to make data available. Much of the data essential or useful for social-good applications are in private hands or in public institutions that might not be willing to share their data. These data owners include telecommunications and satellite companies; social-media platforms; financial institutions (for details such as credit histories); hospitals, doctors, and other health providers (medical information); and governments (including tax information for private individuals).”
●“As with any technology deployment for social good, the scaling up and successful application of AI will depend on the willingness of a large group of stakeholders—including collectors and generators of data, as well as governments and NGOs—to engage. These are still the early days of AI’s deployment for social good, and considerable progress will be needed before the vast potential becomes a reality. Public- and private-sector players all have a role to play.”