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Human-Centred Artificial Intelligence

HCAI

(in German: Human-Centred Artificial Intelligence )

Module-ID: FIN-INF-120488
Link: LSF
Responsibility: Ernesto William De Luca
Lecturer: Ernesto William De Luca
Classes:
  • Lecture Human-Centred Artificial Intelligence
  • Exercise class Human-Centred Artificial Intelligence
 
Applicability in curriculum: - M.Sc. INF: Informatik
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. DKE: Learning Methods and Models for Data Science
- M.Sc. DKE: Applied Data Science
- M.Sc. DE: Grundlagen Informatik
- M.Sc. DE: Human Factors
- M.Sc. VC: Computer Science

Abbreviation

HCAI

Credit Points

6

Semester

Summer

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

Intended learning outcomes:
The students ...

  • know basic principles of Human-Centred AI
  • can apply responsible AI principles
  • understand fairness and explainability
  • can evaluate ethical issues in AI
  • can apply HCAI methods on deep learning architecture and natural language processing algorithms
  • can implement User Experience and Usability principles
  • can manage and plan HCAI projects

Content:

  • Introduction to Human-Centred Artificial Intelligence: Human values in AI; The role of stakeholders; Novel HCAI Framework and Paradigms; Threats in AI; Interactive HumanCentred AI.
  • Introduction to Responsible Artificial Intelligence: Ethical theories and ethics in practice; Responsible research and innovation; The ART of AI: Accountability, Responsibility, Transparency; Ensuring Responsible AI in practice
  • AI and Society. Beyond-accuracy perspectives: Privacy;Fairness and Biases; Explainable Artificial Intelligence (XAI); Accountability; Security and Safety
  • Approaches to project management and planning: Project management; People management and Teamwork; Agile development; Risk management; Estimation techniques and project pricing; Quality standards and management

Workload:
56 contact hours + 124 hours self study

Pre-examination requirements: Type of examination: Teaching method / lecture hours per week (SWS):

Written report (Hausarbeit)

  • 2 SWS lecture
  • 2 SWS exercise class
Prerequisites according to examination regulations: Recommended prerequisites:

none

Machine Learning Information Retrieval Data Science Data Mining Fundamentals of Natural Language Processing Introduction to Deep Learning Eudaimonic Interaction Design Human-Centred Natural Language Processing

Media: Literature:

  • V. Dignum, “Responsible Artificial Intelligence – How to Develop and Use AI in a Responsible Way”, Springer, 2019.
  • B. Shneiderman, “Human-Centered AI”, Oxford University Press, 2022.
  • A. Schmidt, “Interactive Human Centered Artificial Intelligence: A Definition and Research Challenges”.
  • S. Barocas et al., “Fairness and Machine Learning”, 2019.

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