We are set to enter a phase where artificial intelligence (AI) will be adopted and implemented across industry segments, academia and the public sector. AI has immense potential to help tackle some of India’s most challenging social problems. It is already being applied in multiple real-life instances 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 in Kerala).
AI is only part of a much broader set of measures that can be used to tackle societal issues, however. For now, issues such as data accessibility, lack of awareness and AI talent shortage constrain its application for social good. There are multiple areas within the social sector where AI can be applied to solve socio-economic problems in India. But there are bottlenecks and risks that must be overcome and mitigated if AI is to scale up and realize its full potential for social impact. Some of the areas are:
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, floods and thereby optimize the response of firefighters and rescue agents. Drones with AI capabilities can also be used to find missing persons in the wilderness.
With an emphasis on currently vulnerable populations, these domains involve opening access to economic resources and opportunities, including jobs, 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.
Education & Learning
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. Personalization of content through AI will enable students to gear up for adaptive learning and assessment.
Sustaining biodiversity and combating the depletion of natural resources, pollution, and climate change are challenges in this domain. AI tools are being used to study pollution control, flood forecasting and can detect illegal logging in vulnerable forest areas by analyzing audio-sensor data.
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 of the use cases could be 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 Disease Management
This area addresses health challenges, including early-stage diagnosis and optimized patient therapy treatments. Researchers at 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% accuracy by analyzing heart-rate sensor data. Ayushman Bharat’s healthcare scheme can benefit from AI in linking patient data and health records uniformly across hospitals.
Fake News Verification
This area 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 polarising 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 triggered by the dissemination of false news via messaging applications. Use cases in this domain include actively presenting opposing views to ideologically isolated pockets on social media.
Smart Cities & Infra Management
This area includes infrastructure challenges that could promote the public good in the categories of energy, water and waste management, transportation, real estate, and urban planning. Traffic-light networks can be optimized using real-time traffic camera data and Internet of Things (IoT) sensors to maximize vehicle throughput; recently Bengaluru traffic police started pilot pertaining to traffic management using AI. The technology can also be used to schedule predictive maintenance of public transportation systems, such as trains and public infrastructure to identify potentially malfunctioning components.
Financial Inclusion & Social Schemes
Initiatives related to efficiency and effective management of public — and social sector — entities, including strong institutions, transparency and financial management, are included in this area. For example, AI can be used to identify tax fraud using alternative data such as browsing data, retail data or payments history. KYC, Jan Dhan, GST initiatives can be looked into with an AI lens to mitigate possible misuse and fraud scenarios.
Crime Prevention & Judiciary Management
This area 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. India has a backlog of 33 million legal cases in lower and higher courts and most of the judiciary’s time is consumed in locating and assessing the right penal section and its ensuing legal impact. AI can be handy in streamlining this laborious process so that judicial machinery focuses on swift resolution and verdict.
AI is not a panacea for all societal problems, but it could definitely enable a rethink of the way we are managing the large administrative tasks and help simplify it with intelligent and self-learning-enabled algorithms and through easy availability of image, voice, text and video data. New AI scenarios can be unleashed to provide better insights and decision-making aspects to policymakers and ease widespread implementation.