2020/2021 KAN-CCMVV4042U Datafication – foundations, transformations and challenges
English Title | |
Datafication – foundations, transformations and challenges |
Course information |
|
Language | English |
Course ECTS | 7.5 ECTS |
Type | Elective |
Level | Full Degree Master |
Duration | One Semester |
Start time of the course | Autumn |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Max. participants | 170 |
Study board |
Study Board for MSc in Economics and Business
Administration
|
Course coordinator | |
|
|
Main academic disciplines | |
|
|
Teaching methods | |
|
|
Last updated on 04-06-2020 |
Relevant links |
Learning objectives | ||||||||||||||||||||||
At the end of the course, students should be able
to:
|
||||||||||||||||||||||
Course prerequisites | ||||||||||||||||||||||
Ingen | ||||||||||||||||||||||
Examination | ||||||||||||||||||||||
|
||||||||||||||||||||||
Course content, structure and pedagogical approach | ||||||||||||||||||||||
We are living through a time of fundamental digital transformations. The same innovative technologies and sociotechnical practices that are transforming society – enabling novel modes of interaction, new opportunities for knowledge, and disruptive business paradigms – can be abused to invade people’s privacy, provide new tools of discrimination, and harm individuals and communities. This course offers students the advanced theoretical and analytical skills needed to articulate, develop and understand the social implications of these digital transformations.
To provide frameworks that can help students develop, manage and reflect on data-driven strategies in organizations, this course will identify key social, legal and ethical issues at the intersection of technology and society. The course will provide and encourage learning that is grounded in advanced scholarship, informed and evidence-based public debates and real-life examples. The course will enable students to reflect and understand key issues in current digital transformations as well as anticipate future issues that may arise as a consequence of datafication.
No single theoretical framework can capture the complexity of data-centric technologies and their social and organizational implications. Cross-disciplinary insights are therefore needed for a range of analyses and management positions across private, public and non-profit organizations. Hence, the course brings together different perspectives, research methods, and practices from sociology, law, communication and data science. It furthermore includes perspectives from key stakeholders in digital transformations, namely researchers, entrepreneurs, activists, policy creators, journalists, and public intellectuals.
The course will focus on four key societal domains: rights and ethics, labor and automation, bias and inclusion and safety and critical infrastructure. These domains involve thinking through issues such as: how do data-centric technologies impact basic rights, and legal and ethical frameworks? How can responsible strategies be developed for automation and datafication of the workplace? What are algorithmic biases and how can we account for and counter them? And which strategies exist for safe and responsible integration of datafication and AI in core societal infrastructures such as hospitals, power grids and the police? Students will be taught these key issues and understand how they are related to the collection, organization, governance and discarding of data in public and private organizations. The course will include real-world cases, which will enable the students to contextualize and situate relevant case-based examples within existing scientific debates concerning data and society.
The course is organized into three blocs. First, we begin with an introduction to the issue of data and society bridging historical knowledge, pioneer research and practitioner landscapes. This is followed by a bloc that explores key datafication domains: rights and ethics, labor and automation, bias and inclusion and safety and critical infrastructure. And finally, the course will discuss how public and private organizations can develop responsible social technologies.
|
||||||||||||||||||||||
Description of the teaching methods | ||||||||||||||||||||||
The course combines academic lectures, guest lectures by datafication practitioners, discussions, lab work, student presentations, and case studies in an engaging and participatory learning environment. | ||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||
Feedback takes a number of shapes in the course. We will discuss ideas for term papers throughout the course, students will be offered individual feedback and there will also be opportunities for students to engage in peer feedback. | ||||||||||||||||||||||
Student workload | ||||||||||||||||||||||
|
||||||||||||||||||||||
Further Information | ||||||||||||||||||||||
This course is a part of a minor in Data in Business |
||||||||||||||||||||||
Expected literature | ||||||||||||||||||||||
McIlwain, Charlton D. 2020. Black software: the Internet and racial justice, from the AfroNet to Black Lives Matter. Oxford University Press
Zuboff, Shoshana. 2019. The age of surveillance capitalism: the fight for a human future at the new frontier of power. London: Profile Books.
Jørgensen, Rikke Frank, and David Kaye. 2019. Human rights in the age of platforms. http://mitpress.mit.edu/9780262039055
Arora, Payal. 2019. The next billion users: digital life beyond the West. Harvard University Press
Mikkel Flyverbom, Ronald Deibert and Dirk Matten. 2019. The Governance of Digital Technology, Big Data, and the Internet : New Roles and Responsibilities for Business, Business & Society, Vol. 58, No. 1, 1, p. 3-19
Noble, Safiya Umoja. 2018. Algorithms of oppression :data discrimination in the age of Google. NYU Press.
danah boyd, Emily F. Keller, Bonnie Tijerina. 2016. Supporting ethical data research: an exploratory study of emerging issues in big data and technical research. Data & Society. https://datasociety.net/output/supporting-ethical-data-research-an-exploratory-study-of-emerging-issues-in-big-data-and-technical-research/.
Mayer-Schönberger, Viktor, and Kenneth Cukier. 2013. Big data: a revolution that will transform how we live, work, and think. Boston: Houghton Mifflin Harcourt.
|