Bias detection and estimation under technical portfolio reviews
A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.
1. A computer-implemented bias detection method for generating a contextual measurement of class and methodological bias in a review processes, the method comprising:
creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer;
predicting a probability of overlapping data points between the applicant and the reviewer;
building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer; and
calculating a bias score via the deep learning model run over the enriched embeddings,
wherein the bias score includes a combination of:
a class bias;
a methodological bias;
an ecological fallacy;
a reviewer bias;
a reviewer variation; and
an implicit bias.
2. The method of claim 1 , further comprising outputting the bias score to a profile of the reviewer if the bias score is greater than a preset value.
3. The method of claim 1 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the bias score.
4. The method of claim 2 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the output bias score.
5. The method of claim 1 , wherein the bias score includes a combination of:
a class bias; and
a position related parameter.
6. The method of claim 1 , embodied in a cloud-computing environment.
7. A computer program product for bias detection, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith for generating a contextual measurement of class and methodological bias in a review processes, the program instructions executable by a computer to cause the computer to perform:
creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer;
predicting a probability of overlapping data points between the applicant and the reviewer;
building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer; and
calculating a bias score via the deep learning model run over the enriched embeddings,
wherein the bias score includes a combination of:
a class bias;
a methodological bias;
an ecological fallacy;
a reviewer bias;
a reviewer variation; and
an implicit bias.
8. The computer program product of claim 7 , further comprising outputting the bias score to a profile of the reviewer if the bias score is greater than a preset value.
9. The computer program product of claim 7 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the bias score.
10. The computer program product of claim 8 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the output bias score.
11. The computer program product of claim 7 , wherein the bias score includes a combination of:
a class bias; and
a position related parameter.
12. A bias detection system for generating a contextual measurement of class and methodological bias in a review processes, the system comprising:
a processor; and
a memory, the memory storing instructions to cause the processor to perform:
creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer;
predicting a probability of overlapping data points between the applicant and the reviewer;
building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer; and
calculating a bias score via the deep learning model run over the enriched embeddings,
wherein the bias score includes a combination of:
a class bias;
a methodological bias;
an ecological fallacy;
a reviewer bias;
a reviewer variation; and
an implicit bias.
13. The system of claim 12 , further comprising outputting the bias score to a profile of the reviewer if the bias score is greater than a preset value.
14. The system of claim 12 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the bias score.
15. The system of claim 13 , further comprising generating a report that highlights instances of bias in the review by the reviewer based on the output bias score.
16. The system of claim 12 , wherein the bias score includes a combination of:
a class bias; and
a position related parameter.
17. The system of claim 12 , embodied in a cloud-computing environment.