Publications

@inproceedings{Kyriakou2021,
title = {Crowdsourcing Human Oversight on Image Tagging Algorithms: An initial study of image diversity},
author = {Kyriakos Kyriakou, Pınar Barlas, Styliani Kleanthous, Evgenia Christoforou, Jahna Otterbacher},
url = {https://www.humancomputation.com/assets/wips_demos/HCOMP_2021_paper_104.pdf},
year = {2021},
date = {2021-11-01},
booktitle = {The Ninth AAAI Conference on Human Computation and Crowdsourcing},
journal = {HCOMP},
abstract = {Various stakeholders have called for human oversight of algorithmic processes, as a means to mitigate the possibility for automated discrimination and other social harms. This is even more crucial in light of the democratization of AI, where data and algorithms, such as Cognitive Services, are deployed into various applications and socio-cultural contexts. Inspired by previous work proposing human-in-the-loop governance mechanisms, we run a feasibility study involving image tagging services. Specifically, we ask whether micro-task crowdsourcing can be an effective means for collecting a diverse pool of data for evaluating fairness in a hypothetical scenario of analyzing professional profile photos in a later phase. In this work-in-progress paper, we present our proposed oversight approach and framework for analyzing the diversity of the images provided. Given the subjectivity of fairness judgements, we first aimed to recruit a diverse crowd from three distinct regions. This study lays the groundwork for expanding the approach, to offer developers a means to evaluate Cognitive Services before and/or during deployment.},
keywords = {crowdsourcing},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Ntalianis2019,
title = {Feelings’ Rating and Detection of Similar Locations, Based on Volunteered Crowdsensing and Crowdsourcing},
author = {Klimis S. Ntalianis, Andreas Kener, Jahna Otterbacher},
url = {https://ieeexplore.ieee.org/document/8755832},
doi = {10.1109/ACCESS.2019.2926812},
year = {2019},
date = {2019-07-04},
journal = {IEEE Access},
abstract = {In this paper, an innovative geographical locations' rating scheme is presented, which is based on crowdsensing and crowdsourcing. People sense their surrounding space and submit evaluations through: (a) a smartphone application, and (b) a prototype website. Both have been implemented using the state-of-the-art technologies. Evaluations are pairs of feeling/state and strength, where six different feelings/states and five strength levels are considered. In addition, the detection of similar locations is proposed by maximizing a cross-correlation criterion through a genetic algorithm approach. Technical details of the overall system are provided so that the interested readers can replicate its components. The experimental results on real-world data, which also include comparisons with Google Maps Rating and Tripadvisor, illustrate the merits and limitations of each technology. Finally, the paper is concluded by uncovering and discussing interesting issues for future research.},
keywords = {Algorithmic Bias, crowdsourcing},
pubstate = {published},
tppubtype = {article}
}