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Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having huge social impact. But sometimes the behavior of this software is biased and it shows discrimination based on some sensitive attributes such as sex, race etc. Prior works concentrated on finding and mitigating bias in ML models. A recent trend is using instance-based model-agnosticexplanation methods such as LIME to find out biasin the model prediction. Our work concentrates ondoi:10.6084/m9.figshare.12612449.v3 fatcat:jmxymewc4bd6lkr4cuqgybea2m