Explainable Artificial Intelligence for Decision Support: Unterschied zwischen den Versionen

Aktuelle Version vom 20. März 2023, 15:51 Uhr

Vortragende(r)	Yannick Ettwein
Vortragstyp	Bachelorarbeit
Betreuer(in)	Vadim Arzamasov
Termin	Fr 24. März 2023
Vortragsmodus	in Präsenz
Kurzfassung	Policy makers face the difficult task to make far-reaching decisions that impact the life of the the entire population based on uncertain parameters that they have little to no control over, such as environmental impacts. Often, they use scenarios in their decision making process. Scenarios provide a common and intuitive way to communicate and characterize different uncertain outcomes in many decision support applications, especially in broad public debates. However, they often fall short of their potential, particularly when applied for groups with diverse interests and worldviews, due to the difficulty of choosing a small number of scenarios to summarize the entire range of uncertain future outcomes. Scenario discovery addresses these problems by using statistical or data-mining algorithms to find easy-to-interpret, policy-relevant regions in the space of uncertain input parameters of computer simulation models. One of many approaches to scenario discovery is subgroup discovery, an approach from the domain of explainable Artificial Intelligence. In this thesis, we test and evaluate multiple different subgroup discovery methods for their applicabilty to scenario discovery applications.

@@ Zeile 2: / Zeile 2: @@
 |vortragender=Yannick Ettwein
 |email=yannick.ettwein@student.kit.edu
-|vortragstyp=Proposal
+|vortragstyp=Bachelorarbeit
 |betreuer=Vadim Arzamasov
-|termin=Institutsseminar/2022-12-16
+|termin=Institutsseminar/2023-03-24
 |vortragsmodus=in Präsenz
-|kurzfassung=Subgroup discovery is a well established data mining method that aims to identify and describe interesting subgroups within a dataset with respect to certain interestingness criteria.
+|kurzfassung=Policy makers face the difficult task to make far-reaching decisions that impact the life of the the entire population based on uncertain parameters that they have little to no control
-These subgroups can then be used to draw conclusions about the validity of certain scenarios found in the underlying dataset
+over, such as environmental impacts. Often, they use scenarios in their decision making process. Scenarios provide a common and intuitive way to communicate and characterize different uncertain outcomes in many decision support applications,
-In this work, we plan to compare multiple different methods of subgroup discovery on multiple datasets. We then try to generalize the results and rank the methods.
+especially in broad public debates. However, they often fall short of their potential, particularly when applied for groups with diverse
+interests and worldviews, due to the difficulty of choosing a small number of scenarios to summarize the entire range of uncertain future outcomes. Scenario discovery addresses these problems by using statistical or data-mining algorithms to find easy-to-interpret, policy-relevant regions in the space of uncertain input parameters of computer simulation models. One of many approaches to scenario discovery is subgroup discovery, an approach from the domain of explainable Artificial Intelligence.
+In this thesis, we test and evaluate multiple different subgroup discovery methods for their applicabilty to scenario discovery applications.
 }}