In How Many Ways Can an Algorithm be Fair?

Date: 5 April 2017

Time: 11:00 – 12:00

To register your interest, please email Turing Events.


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Recent research in machine learning has thrown up some interesting measures of algorithmic fairness – the different ways that a predictive algorithm can be fair in its outcome.

In  this talk, Suchana Seth will explore what these measures of fairness imply for technology policy and regulation, and where challenges in implementing them lie. The goal is to use these definitions of fairness to hold predictive algorithms accountable.

 

Suchana Seth

Suchana is a physicist-turned-data scientist from India, and the Mozilla Open Web Fellow at Data & Society Research Institute. She has built scalable data science solutions for startups and industry research labs, and holds patents in text mining and natural language processing. Suchana believes in the power of data to drive positive change; she volunteers with DataKind, mentors data-for-good projects, and advises research on IoT ethics. She is also passionate about closing the gender gap in data science, and leads data science workshops with organizations like Women Who Code. At Data & Society, Suchana is studying ways to operationalize ethical machine learning and AI in the industry. Her interests include fairness, accountability and transparency in machine learning, monetizing AI ethically, security vulnerabilities specific to machine learning and AI systems, and the regulatory landscape for predictive algorithms.