This track will cover various topics related to building data organizations as well as the ethical implications of the work we do as data professionals (understanding bias, privacy implications, etc.). Some companies are just getting started with data, some have a more mature data team and process, and some simply have too much data and too small of a team to manage it! On top of it all, many are unaware (or perhaps unconcerned?) about the ethical implications of their work.
As our data teams continue to push the technical boundaries of what is possible, the ethical implications of massive data repositories combined with advanced data mining techniques, machine learning algos, and deep learning in general are raising important considerations in the area of data ethics. Ethical issues often raise discussions regarding organizational and cultural challenges as well.
This track will be applicable to you if you are an engineering or data manager who wants to build a stronger organizational culture around data, and if you are concerned about policy and ethical considerations surrounding the use of data and AI in your organization.
Nitya Narasimhan is a PhD, Polymath and Innovator with decades of software development and research experience across industry, startups and academia, with interests spanning distributed systems, ubiquitous computing, mobile/web development and machine learning. She is a long-time community and conference organizer in the Greater New York area, helping developers translate awareness of technology into actionable impact on software, products and careers. As a Senior Program Manager on the Cloud+AI Developer Relations team at Microsoft, she is currently focused on building programs and empowering communities to support academic, startup and professional audiences in the US. You can connect with here at #in/nityan or follow her @nitya.