Ethics of Data Driven Innovation

Natasa Milic-Frayling | Wednesday, July 17
Professor Natasa Milic-Frayling, Chair of Data Science, University of Nottingham, UK

Digital technologies now permeate all aspects of our lives, supporting new ways of data processing, knowledge acquisition, and information exchange. However, with increased dependence on digital technologies, there are concerns about broad shifts and deep rifts in the society caused by differences in the level of digital literacy and access and control of information services, social engagement platforms, and computational systems. Furthermore, as industry, businesses, and the public sector embrace digital technologies, the practitioners face a wide range of issues, including important concerns of professional ethics in computing and data driven innovation.

The aim of this course is to enable computing professionals to apply critical thinking and logical reasoning to specific ethical issues encountered in computing, leveraging appropriate ethical frameworks, professional guidelines, and computing design principles.

We will cover the ACM Code of Ethics and discuss accountability and responsibility in computing profession. Besides engineering excellence, privacy, security, and safety, we will review Open Data initiatives and practices that increase impact of digital obsolescence and risks to our digital assets.

Through examples of specific empirical studies in data science, we will contrast the computational insights gained from the common practice of mining user logs with a broader awareness of socio-economic and technical design factors that, in fact, affect the structure and properties of the collected data. Such awareness can help with improving computational approaches.

The course is taught through lectures and group assignment taken during the sessions. Lectures cover six topics, reflecting on the techniques and methods commonly used in Data Science:

  • Critical Thinking
  • Ethics Theories
  • Professionalism & ACM Code of Conduct
  • Ethical Design & Privacy Protecting Principles
  • Ethics for Safety & Reliability, Accountability & Responsibility
  • Ecosystem View of Data Science Scenarios.