Doubts About Rater Objectivity: An Investigation of Possible Ways to De-Bias Implicitly Biased Rankings
(2015) FTEM01 20151Theoretical Philosophy
- Abstract (Swedish)
- The aim of this paper is to present the kinds of error that implicit bias can cause when we are to judge and rank which available candidate is the most suited for e. g. a job vacancy, and to investigate whether we can de-bias ranking lists in recruiting processes given that we suspect that implicit bias has influenced the ranking. I will argue that the available methods for this (positive discrimination and the anonymizing of applicants, for example) are not sufficient, due to the fact that they do not take errors caused by implicit bias into account. Instead, I will investigate three possible ways to de-bias given the notion of a Borda score.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/5276767
- author
- Sjödahl, Julia LU
- supervisor
- organization
- course
- FTEM01 20151
- year
- 2015
- type
- H1 - Master's Degree (One Year)
- subject
- language
- English
- id
- 5276767
- date added to LUP
- 2015-05-22 10:10:01
- date last changed
- 2015-05-22 10:10:01
@misc{5276767, abstract = {{The aim of this paper is to present the kinds of error that implicit bias can cause when we are to judge and rank which available candidate is the most suited for e. g. a job vacancy, and to investigate whether we can de-bias ranking lists in recruiting processes given that we suspect that implicit bias has influenced the ranking. I will argue that the available methods for this (positive discrimination and the anonymizing of applicants, for example) are not sufficient, due to the fact that they do not take errors caused by implicit bias into account. Instead, I will investigate three possible ways to de-bias given the notion of a Borda score.}}, author = {{Sjödahl, Julia}}, language = {{eng}}, note = {{Student Paper}}, title = {{Doubts About Rater Objectivity: An Investigation of Possible Ways to De-Bias Implicitly Biased Rankings}}, year = {{2015}}, }