DATA 471 The Trustworthy Datascientist

Lecture 1 Responsible Before getting deep into the serious stuff, we need to dispell a useless, dangerous, misunderstanding. In this course we care about responsibility. We care about how a data scientist can commit to answer to an ethical request in order to tackle an issue. We look forward, because we have problems to solve. Tony Jones. Statue of O'Connor and horse at C.Y. O'Connor beach. by Gnangarra on wikipedia

DATA471 Laboratory 1 (Monday 18 2019) How do we talk about ethics? engaging respectfully As for any discipline, talking about ethical problems and opportunities in a classroom requires agreeing on some rule. The main rule that I, as an educator, impose (it is not negotiable) is that you, the students, commit to engage respectfully. You will have to interact with me and with your peers, and you will have to do in a constructive way that foster a healthy, inclusive, fair community.

Overview DATA471 will stimulate students to think about the ethical facets of their data scientific projects and provide them with conceptual and practical tools to assess the projects. The ethics and security of data collection, storage, manipulation, analysis and communication is of paramount importance in our information based society. This course faces these topics from the point of view of data scientists—rather than consumers or data subjects—enabling the student to become trustworthy professionals.

Outline The course is organised in three blocks (ethical issues, ethical requests, ethical commitment, loosely following Simon Critchley’s Infinitely Demanding structure of disappointment, request, and commitment). Each block contains one hands-on-data laboratory, three guided discussion sessions, and four frontal lectures. The hands-on-data laboratory are based on R via Stenci.la (but students can choose to use any other programming language they and the instructor are familiar with). Each guided discussion requires the students to perform some preliminary reading.