Title: Fairness aware Machine Learning (ML), and Generative AI for Social Good

Guest Speaker:  Professor Latifur Khan

Fellow of IEEE, IET, BCS

Department of Computer Science, University of Texas at Dallas (UT Dallas), USA

Abstract

In this presentation, I will focus on fairness aware machine learning, generative AI and their applications.

With regard to fairness aware machine learning, we explored a problem that involves fairness constraints under changing environments. Specifically, in an online learning setting, data arrives sequentially. Here, the data distribution can change over time, but the learner is still constrained by fairness requirements. In transport, changing environments are a very common scenario. For example, autonomous vehicles drive under various changing environments such as in terms of time of day (sunrise, noon, sunset, night time), or weather patterns (such as rain, cloud cover, sunny conditions). An autonomous vehicle performs various functions which are driven by machine learning, such as pedestrian detection. These functions may be subject to various fairness constraints; for instance, pedestrian detection (whether or not the object being seen is a pedestrian) must be fair with respect to the race or gender of the individuals being detected. Furthermore, this detection task may need to be performed under the various changing environments that we just discussed, i.e., an autonomous vehicle will need to detect pedestrians under various weather conditions and time-of-day scenarios. As such, this provides a very suitable use case for the need for fair online learning under changing environments. To this end, we have proposed a novel algorithm under the assumption that data collected at each time can be disentangled with two representations, an environment-invariant semantic factor and an environment-specific variation factor. The semantic factor is further used for fair prediction under a group fairness constraint.

With regard to generative AI, we will monitor conflicts and political violence around the world by analyzing volumes of continuous or stream specialized text on a global scale. To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained large language model (LLM, part of generative AI) for conflict and political violence. We first gather a large domain-specific text corpus for large language modeling from various sources. We then build ConfliBERT using two approaches: pre-training from scratch and continual pre-training to facilitate lifelong learning. For incremental/continual learning, deep learning models should be able to learn new information while retaining previously learned skills or knowledge, but catastrophic forgetting does happen and we will address that in this talk.

*This work is funded by NSF, DOT, NIH, ONR, ARMY, and NSA. The work, ConflictBERT is in collaboration with Dr. Patrick Brandt, and Dr. Jennifer Holmes, (School of Economic, Political and Policy Sciences, UT Dallas) and the fairness aware ML work is with Dr. Feng Chen (Computer Science).

Short bio:

Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. In addition, he received his bachelor degree in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET) with first class honors (2nd position).

Dr. Khan is a fellow of IEEE, IET, BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics, IEEE Big Data Security Award, and IBM Faculty Award (research) 2016. Dr. Khan has published over 300 papers in premier journals and prestigious conferences. Currently, Dr. Khan’s research focuses on big data management and analytics, data mining and its application to cyber security, and complex data management including geospatial data and multimedia data. His research has been supported by grants from NSF, NIH, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found at www.utdallas.edu/~lkhan.