Lecturer: Thomas Chen
Fields: Artificial Intelligence/Machine Learning
Content
Artificial intelligence, including machine learning and deep learning, have been increasingly utilized for humanitarian applications, from combating climate change to assessing car accidents. AI has been utilized to combat human trafficking, break language barriers, support human rights protections, conserve wildlife, solve for poverty, track and prevent pandemics, detect terrorist attacks, and more. Techniques range anywhere from linear regression to convolutional neural networks, generative adversarial networks, and beyond. In this talk, we explore work in this field tackling humanitarian problems from all angles. This also includes mechanisms for making AI technologies more widely available to underserved communities. Interdisciplinary contributions are especially emphasized. AI for humanitarian good is a burgeoning area of work that will continue to produce novel results. Because the results we obtain are in many cases unattainable with conventional methods (computational or not), this field is one to keep an eye on in the near future.
Literature
- Chen, T. 2020. Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery. In AI for Earth Sciences Workshop at Advances in Neural Information Processing Systems (NeurIPS). URL https://ai4earthscience.github.io/neurips-2020-workshop/papers/ai4earth_neurips_2020_25.pdf
- Jobin, Anna, Marcello Ienca, and Effy Vayena. “The global landscape of AI ethics guidelines.” Nature Machine Intelligence 1.9 (2019): 389-399.
- Taddeo, Mariarosaria, and Luciano Floridi. “How AI can be a force for good.” Science 361.6404 (2018): 751-752.
Lecturer
Thomas Y. Chen is a student from the United States (affiliation: Academy for Mathematics, Science, and Engineering). His research is focused in artificial intelligence and machine learning applications for social and humanitarian good. The two primary projects he is currently working on are developing machine learning models to predict sea ice drift velocity in the Arctic, and publishing a dataset for damage assessment objects in imagery (particularly for post-disaster analysis). His other interests include AI ethics and interpretability/transparency. He has delivered talks at a number of venues related to his work, including Applied Machine Learning Days (AMLD), workshops at NeurIPS, workshops at AAAI conference, European Geosciences Union, Japanese Geosciences Union, and much more.
Affiliation: Academy for Mathematics, Science, and Engineering
Homepage: https://www.linkedin.com/in/thomas-chen-b701511b4/