Content, progress and pedagogy of the module

This module covers a broad range of urgent topics related to data and how it is used in various business applications that may connect to, for instance, law & policy, privacy & surveillance, bias & fairness. While the regulation of the processing of personal data is clearly established in the General Data Protection Regulation, this cannot be said regarding issues such as algorithmic bias, or some uses of predictive modelling techniques. To understand these issues this module will introduce to legal, ethical and policy perspectives to inform the business view.

Upon completion, students will acquire theoretical and practical knowledge, enabling them to understand and evaluate challenges and opportunities relating to legal, ethical and policy issues when working with data-driven business models. Students will gain insights into the technical mechanisms driving issues such as algorithmic bias as well as approaches to achieve alignment between social, business and technological objectives.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• central legal, ethical, social challenges associated with the use of data-driven approaches in business and related areas.
• the interplay between technical and non-technical mechanisms that can cause legal, ethical and social challenges when working with data.
• existing legal, ethical and policy frameworks for responsible data usage.

Skills

The objective is that the student after the module possesses the necessary skills in:

• applying existing frameworks and good practice to design responsible and sustainable data-driven projects.
• accessing model explainability using appropriate tooling.
• evaluating compliance with regard to differential privacy.

Competences

The objective is that the student after the module possesses the necessary competences in:

• independently identifying various legal, ethical and social challenges in data-driven projects and business practices.
• analysing potential risk scenarios, adjusting and potentially developing new options for risk challenges in complex settings.
• applying a problem based approach in the development of responsible data-driven projects and products.

Type of instruction

For information see § 17.

Exam

Prerequisite for enrollment for the exam

• A prerequisite for participating in the exam is that the student has handed in written material.

Exams