AI For Social Good

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Summary

Course Description

Description

This course explores the use of artificial intelligence (AI) to address critical challenges in societal domains such as healthcare, education, environment, and public safety. It aims to provide students with a deep understanding of how AI technologies can be applied ethically and effectively for social good, drawing from cutting-edge research. The course will focus on both the technical and societal implications of AI-driven solutions, highlighting the unique challenges and opportunities in this field. We will study the latest advancements in AI techniques presented at top-tier conferences like IJCAI, AAAI, KDD, and NeurIPS, examining how these can be harnessed to create impactful societal solutions.

Contents

Topics will span a wide array of AI domains such as but limited to:

  • Healthcare: AI for diagnosis, personalised medicine, public health monitoring, and remote care
  • Environment and Sustainability: AI in climate modelling, pollution monitoring, energy efficiency, and conservation
  • Disaster Resilience: AI for disaster prediction, response, and resource management
  • Education: Personalized learning systems, intelligent tutoring, analysis of educational outcomes
  • Public Safety and Security: Social engineering, AI for detecting misinformation, predictive policing

The AI technologies would include:

  • Supervised Learning: Applications in classification and prediction tasks (e.g., disease diagnosis, fraud detection, poverty prediction)
  • Unsupervised Learning: Clustering and anomaly detection for environmental monitoring, resource allocation
  • Semi-supervised Learning: Addressing challenges with limited labelled data in social good applications
  • Reinforcement Learning: Applications in resource optimization, healthcare interventions, and adaptive learning systems
  • Natural Language Processing (NLP): Sentiment analysis, misinformation detection, speech recognition for social applications
  • Computer Vision: Object detection, segmentation and classification for environmental monitoring, healthcare imaging
  • Deep Learning: Use of neural networks for complex social problems (e.g., medical imaging, disaster prediction)

Texts/References:

AI for Social Impact Book by Milind Tambe, Fei Fang, and Bryan Wilder

We will be following research publications appearing at AAAI, IJCAI, KDD, Neurips, ICLR, and similar top conferences or symposiums.

There are some related courses at top universities that would serve as a good reference - AI for Social Impact course at Harvard - AI for Social Impact Seminar series 2020 - Social good at Google - AI for Social Good course at Stanford - AI for social good coursera specialisation

Tentative guest lectures (in no order)

  1. Dr. Alok Talekar, Google India
  2. Dr. Harsh Parikh, John Hopkins University
  3. Dr. Aparna Taneja, Google India
  4. Dr. Anupam Sobti, Plaksha University