Beyond Bias: Crafting Fairness in Pythonic Machine Learning Systems
Friday, May 24
15:25 - 16:10
Have you ever wondered how we can quantify and rectify biases in predictive policing algorithms using Python? In this talk, let’s discover techniques which address bias in machine learning systems and build the orchestration of algorithms for fair and harmonious systems.
Artificial intelligence systems have woven their influence into every facet of our lives, from the physical to the virtual, shaping our world with data and algorithms. But, have you ever thought about the biases in the algorithms behind predictive policing?
In this talk, we’ll explore this question. Firstly, we’ll unveil the critical significance of quantifying and rectifying biases within machine learning systems, using Python. Through a live demo, we will delve into practical techniques designed to address bias, and orchestration of algorithms that lay the foundation for the creation of fair and equitable machine learning systems. Lastly, we’ll discuss the bigger picture—how these fair algorithms can contribute to a just and inclusive society.
Rashmi Nagpal
Rashmi, a Software Engineer by profession and a passionate researcher, is dedicated to crafting beautiful AI/ML products. With nearly 4 years of experience, she has brought ideas to life at pre-seed startups and contributed to impactful redesigns and features at established industry giants. Beyond coding, Rashmi finds inspiration in capturing the wonders of the cosmos through her telescope and engaging in board games with friends. And let’s not forget her adorable Maltese breed companion, Fluffy, who adds joy to her life!
Additionally, as a Research Affiliate at MIT CSAIL, Rashmi continues to push the boundaries of technology, leveraging her expertise to bridge the gap between academia and industry. Her collaborations with esteemed researchers enable her to stay at the forefront of cutting-edge advancements in the field of AI and ML.