Time: 2:00 pm – 3:00 pm
Location: Arora 16/17 Masterclass
This session is only bookable for those that have a confirmed place at Women in Data
This session has a limit of 130 spaces, please log in to your Women in Data account to register for a place.
Session title: Causality and Fairness in Machine Learning Projects
Speakers: Nisara Sriwattanaworachai and Dr Ines Marusic, Data Scientists, from QuantumBlack, Al by McKinsey
During this session Nisara will be presenting and discussing with the audience ‘Causality in machine Learning projects’. As we know, more and more organisations are using machine learning to help make business decisions. In this type of problem performance of models is no longer the only concern. In many machine learning projects, it is crucial to distinguish between events that cause outcomes and those that merely correlate. Nisara will discuss the concept of causality and how we use this to generate deeper insights for our clients.
Next, Viktoriia will be presenting on ‘Fairness in machine learning projects’. With an increasing number of machine learning projects in areas such as banking, finance, and pharmaceuticals, models are being applied to make decisions that can severely effect people’s lives. Unfortunately, the data in these domains often contains bias that exists in our society. The goal of algorithmic fairness is to ensure machine learning models make fair predictions devoid of discrimination. This presentation will show the full fairness pipeline from bias detection to evaluation of the model’s fairness to post-processing, to make the model’s predictions fairer using the example of a pharmaceutical case study.
During this workshop there will be opportunities for smaller problem solving discussions, on these topics, within the audience.
Name:
Nisara Sriwattanaworachai, Data Scientist – QuantumBlack, Al by McKinsey
Bio:
Nisara is a Data Scientist at the London office of QuantumBlack. Nisara has developed an expertise in a wide range of explanatory and predictive models, as well as linear and non-linear optimisation methods. At QuantumBlack, she also contributes to R&D efforts in causal inference. Prior to joining QuantumBlack, Nisara worked as a Data Scientist for a tech start-up in Berlin focusing on analytics projects applied to the real estate market.
Nisara holds a PhD in Mathematics from Freie Universität Berlin, where she undertook research into spectral clustering of stochastic processes to identify optimal patterns for drug design. Nisara also holds a Masters in Operations Research from Cornell University.
Name:
Ines Marusic, Senior Data Science Consultant – QuantumBlack
Bio:
Dr Ines Marusic is a Senior Data Scientist at QuantumBlack, McKinsey & Company. Ines has developed data science products and solutions for some of the world’s largest organisations, helping them adopt machine learning at scale to transform their businesses and enhance their performance. She also leads the Fairness and Ethics initiative for QuantumBlack’s Data Science practice globally.
Ines holds a PhD in Computer Science and an MSc in Mathematics and Foundations of Computer Science from the University of Oxford, where her research was in computational foundations of machine learning. She is a co-founder of the Oxford Women in Computer Science Society and the Oxbridge Women in Computer Science Conference.
