This course provides a comprehensive exploration of ethical considerations within the dynamic field of data science including ethical approaches, principles, privacy, and informed consent. The course also presents the responsible data science framework, model interpretability and fairness, and code of ethics in data science. The course covers various ethical dilemmas and contributes positively to society through responsible data practices. Upon the successful completion of this course, students will be able to: • Understand ethical principles and approaches, and their relevance to data science. • Recognize the importance of data privacy and informed consent in tackling privacy concerns. • Identify and implement the steps of the Responsible Data Science (RDS) framework. • Comprehend the necessity of model interpretability and AI fairness in ethical data science. • Follow the code of ethics and professional conduct in the field of data science. Students will use these competencies during their academic journey and professional lives as data scientists in all tasks related to data processing.

Temps présentiel : 12.5 heures

Charge de travail étudiant : 50 heures

Méthode(s) d'évaluation : Examen écrit

Référence :
• Fleming, G., & Bruce, P. C. (2021). Responsible data science transparency and fairness in algorithms. Wiley. • Goltz, N. (Sean), & Dowdeswell, T. (2023). Real world AI ethics for Data Scientists Practical Case Studies. Taylor & Francis Ltd. • Hasselbalch, G., & Tranberg, P. (2016). Data ethics: The New Competitive Advantage. Publishare. • Velasquez, M. G. (2018). Business ethics: Concepts and cases. Pearson Education South Asia Pte Ltd. • Franks, B. (2020). 97 things about ethics everyone in data science should know: Collective wisdom from the experts. O’Reilly.