EBA 3520 AI and Data Ethics
EBA 3520 AI and Data Ethics
In this course, students will explore the ethical complexities of information and communication technology, as well as of data science in general. Combining theoretical foundations from information ethics and real-world inquiry, students will build their ethical imaginations and skills for ethically collecting, storing, sharing and analyzing data derived from human subjects including data used in algorithms. To this end, the course will examine legal, policy, and ethical issues that arise throughout the full lifecycle of data science from collection, to storage, processing, analysis and use, including, privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Practically, using case studies, students will explore current applications of quantitative reasoning in organizations, algorithmic transparency, and unintended automation of discrimination via data that contains biases rooted in race, gender, class, and other characteristics.
During the course students shall learn:
- What are the foundational paradigms of information ethics, and how do they relate to different ways of framing and resolving ethical issues in a number of disciplinary ways?
- What are ethical implications of Data, AI and Algorithms as either tools or agents in modern work and living environments?
- Which trajectory will Data Collection and Usage, AI and Algorithmic development take? Which are the best-case and which are the worst-case scenarios?
- How can we ethically manage Data, AI and Algorithms in the context of business analytics? How may these challenges be managed in light of key legislations such as the GDRP?
- Which trajectory will Data Collection and Usage, AI and Algorithmic development take? Which are the best-case and which are the worst-case scenarios?
- How can we ethically manage Data, AI and Algorithms in the context of business analytics? How may these challenges be managed in light of key legislations such as the GDRP?
After completing the course students should have the ability to:
- Identify and assess the ethical impacts of a given course of action in data-driven organizations
- Describe techniques for protecting privacy, sharing data ethically, and minimizing both collective and individual harm associated with data-driven organizational processes.
- Practice performing an ethical audit of data-driven processes in a given organizational context.
- Critical Reflection on ethical and policy issues, and to perceive the various facets and viewpoints surrounding complex data ethics questions.
- Reflection of the ethical and social implications that the application of advanced data analytics may bring to business and society, and the impact biased or incomplete data sets may have on stakeholders.
- Be able to apply your understanding to problematize specific technologies, analyze and reflect critically on their impacts, think through various interventions and argue for how to develop technologies in more thoughtful ways.
- Overview of Ethical Issues in Data Driven Organizations
- Philosophical Foundations of Ethical Use of Data
- Philosophical Challenges of Thinking in Categories
- Elements and Functions of Regulation
- Research Ethics for Data Science
- Inequity, Inclusion and Accessibility
- Privacy and Surveillance
- Algorithmic Bias and Discrimination
- Intellectual Property and Appropriation
- Values in Design
- Data Protection Law and the Ethical Use of Analytics
- Anonymization and Informed Consent
- AI Fairness, Accountability and Transparency
- Ethnography of Data and Analytics
- Design Fiction, and Futurism
The course combines formal lectures which provide an arena to discuss the themes encountered in the readings and to help with any problems in understanding the foundational concepts, with exploration of how these will impact the business and social context. Students will prepare project deliverables for several of the key themes of the lecture (an ethical analysis, a privacy remedy strategy, a work of speculative fiction surrounding ethically ambiguous technology, and an ethical audit for a data project).
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
No particular prerequisites are required.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Individual Duration: 1 Semester(s) Comment: Students will be asked to complete a short assignment based on the reading material distributed for the course, to be completed individually and meant to be a reflection task on the material. Exam code: EBA 35202 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) Comment: In teams of 1-3, students will perform an ethical audit of a project they are working on, alternatively they may select from a submitted list of faculty research projects. Students will gather relevant case material, examine research protocols, data capture and storage, and investigate the impact of ongoing or future applications (if any). Students will provide an analysis of ethical adherence, lapses, discuss likely impacts on the organization, the subjects, the data, and key stakeholder groups throughout the community. Essay length will range from 10-15 pages. Exam code: EBA 35203 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 45 Hour(s) | |
Submission(s) | 45 Hour(s) | |
Group work / Assignments | 30 Hour(s) | |
Student's own work with learning resources | 80 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.