Presentation Title

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

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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COinS
 
Mar 4th, 9:00 AM Mar 4th, 10:15 AM

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.