Administrative information

Title	Investigate data lineage, challenges and potential impact of the AI teams
Duration	90 min
Module	C
Lesson Type	Practical
Focus	Ethical - Compliance, Legality and Humanity
Topic	EU and International Legislation/Frameworks on data, AI, human rights and equality

Keywords

Data Lineage, Data Provenence, Differential Privacy, Data Pipeline,

Learning Goals

To understand the concept and importance of data provenence for AI teams
Impact of bias in datasets
Methods for tracing Data Lineage

Expected Preparation

Learning Events to be Completed Before

Obligatory for Students

Knowledge of GDPR and database concepts

Optional for Students

References and background for students

High level knowledge of GDPR and database concepts

Recommended for Teachers

Provide background on GDPR
Give examples of significant GDPR cases

Lesson materials

Practical Instructions

The materials of this learning event are available under CC BY-NC-SA 4.0.

Instructions for Teachers

Try to stick to the time table. If possible provide more time to the question and answer session if needed.

Outline

Duration (min)	Description	Concepts	Activity	Material
0 - 5 min	Brief of the tasks to be conducted			Practical Instructions
5 - 30 min	Task #1 - Provenance	Provenance	Guided Discussion	-
30 - 55 min	Task #2 - Compliance	Compliance	Guided Discussion	-
55 - 80 min	Task #3 - In Practice	In Practice	Guided Discussion	-
80 - 90 min	Summary of the practical		Q&A	-

More information

Click here for an overview of all lesson plans of the master human centred AI

Please visit the home page of the consortium HCAIM

Acknowledgements

The Human-Centered AI Masters programme was co-financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.

The materials of this learning event are available under CC BY-NC-SA 4.0

The HCAIM consortium consists of three excellence centres, three SMEs and four Universities