Generative AI for Social Good

Abstract: 

This talk will focus on current generative AI methods, including image and text generation, with a focus on social good applications, including medical imaging applications, diversity training applications, public health initiatives, and underrepresented language applications. We'll start with an overview of common generative AI algorithms for image and text generation before launching into a series of case studies with more specific algorithm overviews and their successes on social good projects.

We'll explore an algorithm called TopoGAN that is being used to augment medical image samples. We'll look at GPT-4 and open-source large language models (LLMs) that can generate cases of bias and fairness. We'll consider how language translation and image generators such as stable diffusion can quickly produce public health campaign material. Finally, we'll explore language generation with low-resource languages like Hausa and Swahili, highlighting the potential for language applications in the developing world to aid businesses, governments, and non-profits communicating with local populations. We'll end the talk with a discussion of ethical generative AI and potential for misuse.

Learning outcomes include familiarity with common generative AI algorithms and sources, their uses in a variety of settings, and ethical considerations when developing generative AI algorithms. This will equip programming-oriented data scientists with a background to implement algorithms themselves and business-focused analytics professionals with a background to consider strategic initiatives that might benefit from generative AI.

Background Knowledge:

1) Familiarity with image and text generation models/tools
2) Understanding of the role topology plays in generative AI
3) Understanding of common social good use cases and the benefits of using generative AI in these contexts
4) Understanding of potential uses in the developing world to meet analytics and social good needs
5) Consideration of ethical use of generative AI and some of the fairness issues/legal issues that are currently debated with this technology
6) Understanding of where to look for common open-source or low-cost generative AI resources (Python stable diffusion packages, NightCafe free accounts, HuggingFace models, Python TopoGAN package...)

Bio: 

Colleen M. Farrelly is a mathematician and data scientist focused on network science, topological data analysis, generative AI, and psychometrics. She focuses on machine learning for social good, including many initiatives in Africa. Her current affiliations are Post Urban Ventures (UK) and Candlesticks (Kenya), as well as academic partners in the US and Africa, with whom she publishes. She is the author of The Shape of Data and a forthcoming network science book.

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