BIG DATA CONFERENCE
Technology Startup Founder
A technology startup founder whose purpose is to ensure algorithmic systems are Fair, Accountable and Transparent. Having over 17 years of experience in evaluating large scale software systems as a PhD researcher, management consultant and finally as a practice leader for an international firm in Greece. An active member of the startup community, serving the Orange Grove incubator as Chairman of the Board of Directors.
Trust and Quality in the Era of Software 2.0
There is a widespread excitement about the potential of Machine Learning (ML) models, but with market pressures, haste to ship and deliver them; those models fail to deliver what they promise or even worse they can have a negative impact for businesses and individuals. It is our thesis that just as we define quality properties for governing a typical software system from the way it is implemented (e.g. maintainability) to the way it behaves (e.g. functional suitability), we need to do a similar thing for ML models. That is why apart from Accuracy, the so-called F.A.T. properties, (Fairness, Accountability, Transparency) should be essential quality properties for a ML model (or AI system) contributing towards its adoption and trusted governance. In his talk Yiannis Kanellopoulos will present an approach on how an ML model can be evaluated in terms of its Fairness, Accountability and Transparency. Using examples of case studies (from industrial and publicly available datasets) Yiannis will share insights and the benefits one can get by making a ML model accountable, transparent and trying to mitigate its biases.