  With the emergence of the Software 3.0 era, there is a growing trend of
compressing and integrating large models into software systems, with
significant societal implications. Regrettably, in numerous instances, model
compression techniques impact the fairness performance of these models and thus
the ethical behavior of DNN-powered software. One of the most notable example
is the Lottery Ticket Hypothesis (LTH), a prevailing model pruning approach.
This paper demonstrates that fairness issue of LTHbased pruning arises from
both its subnetwork selection and training procedures, highlighting the
inadequacy of existing remedies. To address this, we propose a novel pruning
framework, Ballot, which employs a novel conflict-detection-based subnetwork
selection to find accurate and fair subnetworks, coupled with a refined
training process to attain a high-performance model, thereby improving the
fairness of DNN-powered software. By means of this procedure, Ballot improves
the fairness of pruning by 38.00%, 33.91%, 17.96%, and 35.82% compared to
state-of-the-art baselines, namely Magnitude Pruning, Standard LTH,
SafeCompress, and FairScratch respectively, based on our evaluation of five
popular datasets and three widely used models. Our code is available at
https://anonymous.4open.science/r/Ballot-506E.
