Session: The (Un)Fair Machine Learning
With the growing usage of machine learning and artificial intelligence in real lives, the need for incorporating mitigation of ethical and moral issues into Machine Learning models is rising at an alarming rate. We must develop tools to evaluate 'unfairness' in these models and help data scientists deal with them.
By means of this talk, I want to spread the awareness of the biases that exist in machine learning - where they come from, how to evaluate them and various ways to mitigate them to make 'fair' and better real-life machine learning models.
I am pursuing MS in Computer Science with specialisation in data science from UMass Amherst. Previously, I graduated from IIT Indore, India and worked for 2 years at Arcesium LLC. I am a learner and an explorer of new domains, challenges and opportunities. I love to talk about ALGORITHMS, DATA SCIENCE and MACHINE LEARNING.