Perceived Fairness From Developer’s Perspective in Artificial Intelligent Systems
Advisor Information
Dr. Deepak Khazanchi
Location
MBSC Dodge Room 302A - G
Presentation Type
Oral Presentation
Start Date
4-3-2022 9:00 AM
End Date
4-3-2022 10:15 AM
Abstract
Fairness in ML applications is becoming an important issue for both academicians and practitioners. In ML applications, unfairness is triggered due to bias in the data, curation process, and erroneous assumptions rendered within the algorithmic development process. As AI/ML applications come into broader use, mitigating bias in ML applications is critical. This research study targets the overall ML application design and development process, i.e., data collection, pre-processing, in-processing, post-processing and investigates the factors that influence the perception of fairness in AI/ML applications to form the perspective of software developers.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Scheduling Link
1
Perceived Fairness From Developer’s Perspective in Artificial Intelligent Systems
MBSC Dodge Room 302A - G
Fairness in ML applications is becoming an important issue for both academicians and practitioners. In ML applications, unfairness is triggered due to bias in the data, curation process, and erroneous assumptions rendered within the algorithmic development process. As AI/ML applications come into broader use, mitigating bias in ML applications is critical. This research study targets the overall ML application design and development process, i.e., data collection, pre-processing, in-processing, post-processing and investigates the factors that influence the perception of fairness in AI/ML applications to form the perspective of software developers.