Applying AI for Social Good

By Ankita Pamnani

Interest in Artificial Intelligence (AI) has dramatically increased in recent years and AI has been successfully applied to societal challenge problems. It has a great potential to provide tremendous social good in the future.

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.

AI has a broad potential across a range of social domains.

  • Education
    • 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.
  • Public and Social Sector
  • 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.
  • Environment
    • Sustaining biodiversity and combating the depletion of natural resources, pollution, and climate change are challenges in this domain.

Some of the issues that we are currently facing with social data

  • Data needed for social-impact uses may not be easily accessible
    • 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).
    • The expert AI talent needed to develop and train AI models is in short supply
      • The complexity of problems increases significantly when use cases require several AI capabilities to work together cohesively, as well as multiple different data-type inputs. Progress in developing solutions for these cases will thus require high-level talent, for which demand far outstrips supply and competition is fierce.
    • ‘Last-mile’ implementation challenges are also a significant bottleneck for AI deployment for social good
      • Organizations may also have difficulty interpreting the results of an AI model. Even if a model achieves a desired level of accuracy on test data, new or unanticipated failure cases often appear in real-life scenarios.