Ethical and legal considerations are essential to many applications of data science.|
This colloquium series is intended to make the students aware of such considerations and to give them the vocabulary to discuss those matters with experts of legal and ethical aspects.
Furthermore, the colloquium will enable students to develop an understanding of professional integrity and provides them with applicable practices for conducting responsible data science.
Some of the objectives of the ethics colloquium include:
After this colloquium, students will be able to identify ethical issues in data projects.
- Students develop an understanding for the ethical concerns of data science. They recognize how data and data practices, machine learning and their implementation into business models or public administration is related to societal values. Students identify various ethical challenges raised through current data practices and their application in society.
- Students will identify different layers of responsibility for designing data science applications, applying them in societal contexts and regulating them. They are familiar with case examples of best and worse practices, and texts addressing ethical, legal, political and social concerns of data science.
They can recognize responsibilities and voice their own concerns and how these affect design decisions.
They are familiar with guidelines for responsible data ethics and the code of conducts of their profession.
The objective of this colloquium is to help students to develop a professional integrity as responsible data scientists.
Attendance is mandatory.
Moreover, you have to write a brief report (of 500) words explaining which talks were interesting and why, and which talks you thought were missing.
The Data Science ethics colloquium is a series of meetings, organized throughout blocks 1 to 3.|
The meetings alternate bi-weekly with the INFOMDSPC Data science ethics colloquium series.
a) lectures on ethical and legal aspects of data science projects
b) assignments for self-study or group work.
The precise schedule will be communicated in time.
Occasionally papers may be assigned to be read before the lecture.