Lise Getoor
ABSTRACT
Data science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data-driven technologies. How can machine learning and AI systems reason effectively about complex dependencies and uncertainty? Furthermore, how do we understand the ethical and societal issues involved in data-driven decision-making? There is a pressing need to integrate algorithmic and statistical principles, social science theories, and basic humanist concepts so that we can think critically and constructively about the socio-technical systems we are building. In this talk, I will overview this emerging area.
Index Terms
- Responsible Data Science
Recommendations
Responsible Data Science
SIGMOD '19: Proceedings of the 2019 International Conference on Management of DataData science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data-driven technologies. How can machine learning and database systems ...
Responsible Big Data Analytics for E-Business Services
ICBDR '21: Proceedings of the 5th International Conference on Big Data ResearchThis paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business ...
How to be a responsible slave: managing the use of expert information systems
Computer ethicists have for some years been troubled by the issue of how to assign moral responsibility for disastrous events involving erroneous information generated by expert information systems. Recently, Jeroen van den Hoven has argued that agents ...
Comments