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==Important Aspects== | ==Important Aspects== | ||
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A few important aspects that the attendees took away from the workshop were: | |||
*everyone has some form of bias you have to try to get rid of that bias by realizing that there is one | |||
* the Cognitive Bias Codex: | |||
*Simliarity Effect: if recruit is similar to last member/ other members, it is more likely that they will get picked | |||
*Questions to ask yourself: If the person was older/ male/ of a different ethnicity, would you be more likely to choose them? Then do so. | |||
*Ways to prevent making biased decisions: | |||
bias observing/ flagging | |||
more than one person to make the decision | |||
no decisions after a long day | |||
Revision as of 13:21, 17 December 2023
Gender Bias in der Personalauswahl (Jul 7 2023)
On July 7th, Dr. Lisa Howarth visited the Faculty of Mathematics to do a workshop on gender biases in the scientific field are dealt with today and how the performance of an individual can be distorted by gender, and on how to use bias-management methods to create a more equal way of choosing the staff, based on competence, not gender.
The Organizer
Dr. Lisa Howarth is a psychologist, university and organization advisor. She has been researching on topics of equality internationally for 10 years and is also a coach for University executives and colleagues, etc. as well as having concepted University-Career-Programs and Gender and Diversity Competence Programs. She is also part of GenderWerkstätte, GMEI (Gender Mainstreaming Experts International) and director of FELIN (female leaders initiative)
Important Aspects
A few important aspects that the attendees took away from the workshop were:
- everyone has some form of bias you have to try to get rid of that bias by realizing that there is one
- the Cognitive Bias Codex:
- Simliarity Effect: if recruit is similar to last member/ other members, it is more likely that they will get picked
- Questions to ask yourself: If the person was older/ male/ of a different ethnicity, would you be more likely to choose them? Then do so.
- Ways to prevent making biased decisions:
bias observing/ flagging more than one person to make the decision no decisions after a long day