AI SPECIAL .....Applying artificial intelligence for social good PART I

 Applying artificial intelligence for social good PART I

 Discussion Paper (PDF–3MB)

AI is not a silver bullet, but it could help tackle some of the world’s most challenging social problems.

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.

Real-life examples of AI are already being applied in about one-third of these use cases, albeit in relatively small tests. They range from diagnosing cancer to helping blind people navigate their surroundings, identifying victims of online sexual exploitation, and aiding disaster-relief efforts (such as the flooding that followed Hurricane Harvey in 2017). AI is only part of a much broader tool kit of measures that can be used to tackle societal issues, however. For now, issues such as data accessibility and shortages of AI talent constrain its application for social good.

This article is a condensed version of our discussion paper, Notes from the AI frontier: Applying AI for social good . It looks at domains of social good where AI could be applied, and the most pertinent types of AI capabilities, as well as the bottlenecks and risks that must be overcome and mitigated if AI is to scale up and realize its full potential for social impact.

The article is divided into five sections:

1. Mapping AI use cases to domains of social good

2. AI capabilities that can be used for social good

3. Overcoming bottlenecks, especially around data and talent

4. Risks to be managed

5. Scaling up the use of AI for social good

1.         Mapping AI use cases to domains of social good

For the purposes of this research, we defined AI as deep learning. We grouped use cases into ten social-impact domains based on taxonomies in use among social-sector organizations, such as the AI for Good Foundation and the World Bank. Each use case highlights a type of meaningful problem that can be solved by one or more AI capability. The cost of human suffering, and the value of alleviating it, are impossible to gauge and compare. Nonetheless, employing usage frequency as a proxy, we measure the potential impact of different AI capabilities.

For about one-third of the use cases in our library, we identified an actual AI deployment (Exhibit 1). Since many of these solutions are small test cases to determine feasibility, their functionality and scope of deployment often suggest that additional potential could be captured. For three-quarters of our use cases, we have seen solutions deployed that use some level of advanced analytics; most of these use cases, although not all, would further benefit from the use of AI techniques. Our library is not exhaustive and continues to evolve, along with the capabilities of AI.

Exhibit 1 SEE ORIGINAL ARTICLE

Crisis response

These are specific crisis-related challenges, such as responses to natural and human-made disasters in search and rescue missions, as well as the outbreak of disease. Examples include using AI on satellite data to map and predict the progression of wildfires and thereby optimize the response of firefighters. Drones with AI capabilities can also be used to find missing persons in wilderness areas.

Economic empowerment

With an emphasis on currently vulnerable populations, these domains involve opening access to economic resources and opportunities, including jobs, the development of skills, and market information. For example, AI can be used to detect plant damage early through low-altitude sensors, including smartphones and drones, to improve yields for small farms.

Educational challenges

These include maximizing student achievement and improving teachers’ productivity. For example, adaptive-learning technology could base recommended content to students on past success and engagement with the material.

Environmental challenges

Sustaining biodiversity and combating the depletion of natural resources, pollution, and climate change are challenges in this domain. (See Exhibit 2 for an illustration on how AI can be used to catch wildlife poachers.) The Rainforest Connection, a Bay Area nonprofit, uses AI tools such as Google’s TensorFlow in conservancy efforts across the world. Its platform can detect illegal logging in vulnerable forest areas by analyzing audio-sensor data.

Exhibit 2 SEE ORIGINAL ARTICLE

Equality and inclusion

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.

Health and hunger

This domain addresses health and hunger challenges, including early-stage diagnosis and optimized food distribution. Researchers at the University of Heidelberg and Stanford University have created a disease-detection AI system—using the visual diagnosis of natural images, such as images of skin lesions to determine if they are cancerous—that outperformed professional dermatologists. AI-enabled wearable devices can already detect people with potential early signs of diabetes with 85 percent accuracy by analyzing heart-rate sensor data. These devices, if sufficiently affordable, could help more than 400 million people around the world afflicted by the disease.

Information verification and validation

This domain concerns the challenge of facilitating the provision, validation, and recommendation of helpful, valuable, and reliable information to all. It focuses on filtering or counteracting misleading and distorted content, including false and polarizing information disseminated through the relatively new channels of the internet and social media. Such content can have severely negative consequences, including the manipulation of election results or even mob killings, in India and Mexico, triggered by the dissemination of false news via messaging applications. Use cases in this domain include actively presenting opposing views to ideologically isolated pockets in social media.

Infrastructure management

This domain includes infrastructure challenges that could promote the public good in the categories of energy, water and waste management, transportation, real estate, and urban planning. For example, traffic-light networks can be optimized using real-time traffic camera data and Internet of Things (IoT) sensors to maximize vehicle throughput. AI can also be used to schedule predictive maintenance of public transportation systems, such as trains and public infrastructure (including bridges), to identify potentially malfunctioning components.

Public and social-sector management

Initiatives related to efficiency and the effective management of public- and social-sector entities, including strong institutions, transparency, and financial management, are included in this domain. For example, AI can be used to identify tax fraud using alternative data such as browsing data, retail data, or payments history.

Security and justice

This domain involves challenges in society such as preventing crime and other physical dangers, as well as tracking criminals and mitigating bias in police forces. It focuses on security, policing, and criminal-justice issues as a unique category, rather than as part of public-sector management. An example is using AI and data from IoT devices to create solutions that help firefighters determine safe paths through burning buildings.

Our use-case domains cover all 17 of the UN’s Sustainable Development Goals

The United Nations’ Sustainable Development Goals (SDGs) are among the best-known and most frequently cited societal challenges, and our use cases map to all 17 of the goals, supporting some aspect of each one (Exhibit 3). Our use-case library does not rest on the taxonomy of the SDGs, because their goals, unlike ours, are not directly related to AI usage; about 20 cases in our library do not map to the SDGs at all. The chart should not be read as a comprehensive evaluation of AI’s potential for each SDG; if an SDG has a low number of cases, that reflects our library rather than AI’s applicability to that SDG.

Exhibit 3 SEE ORIGINAL ARTICLE

By Michael Chui, Martin Harrysson, James Manyika, Roger Roberts, Rita Chung, Pieter Nel, and Ashley van Heteren

https://www.mckinsey.com/featured-insights/artificial-intelligence/applying-artificial-intelligence-for-social-good?cid=other-eml-alt-mgi-mck-oth-1811&hlkid=485b551e30be4defadc2592045042ed6&hctky=1627601&hdpid=465daed6-1d79-427b-9ff3-417da5844a17

CONTINUES IN PART II