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Computers and Society

New submissions

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New submissions for Wed, 16 Jan 19

[1]  arXiv:1901.04542 [pdf, other]
Title: BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala
Comments: 7 pages, 4 figures
Subjects: Computers and Society (cs.CY); Social and Information Networks (cs.SI)

We present a method to reconstruct networks of socialbots given minimal input. Then we use Kernel Density Estimates of Botometer scores from 47,000 social networking accounts to find clusters of automated accounts, discovering over 5,000 socialbots. This statistical and data driven approach allows for inference of thresholds for socialbot detection, as illustrated in a case study we present from Guatemala.

[2]  arXiv:1901.04972 [pdf, other]
Title: Topological Analysis of Bitcoin's Lightning Network
Subjects: Computers and Society (cs.CY); Social and Information Networks (cs.SI)

Bitcoin's Lightning Network (LN) is a scalability solution for Bitcoin allowing transactions to be issued with negligible fees and settled instantly at scale. In order to use LN, funds need to be locked in payment channels on the Bitcoin blockchain (Layer-1) for subsequent use in LN (Layer-2). LN is comprised of many payment channels forming a payment channel network. LN's promise is that relatively few payment channels already enable anyone to efficiently, securely and privately route payments across the whole network. In this paper, we quantify the structural properties of LN and argue that LN's current topological properties can be ameliorated in order to improve the security of LN, enabling it to reach its true potential.

Cross-lists for Wed, 16 Jan 19

[3]  arXiv:1901.04562 (cross-list from cs.LG) [pdf, other]
Title: Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (stat.ML)

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road.
In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product

[4]  arXiv:1901.04824 (cross-list from stat.OT) [pdf, ps, other]
Title: Approaching Ethical Guidelines for Data Scientists
Comments: 18 pages, submitted Nov 12th 2018
Subjects: Other Statistics (stat.OT); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (stat.ML)

The goal of this article is to inspire data scientists to participate in the debate on the impact that their professional work has on society, and to become active in public debates on the digital world as data science professionals. How do ethical principles (e.g., fairness, justice, beneficence, and non-maleficence) relate to our professional lives? What lies in our responsibility as professionals by our expertise in the field? More specifically this article makes an appeal to statisticians to join that debate, and to be part of the community that establishes data science as a proper profession in the sense of Airaksinen, a philosopher working on professional ethics. As we will argue, data science has one of its roots in statistics and extends beyond it. To shape the future of statistics, and to take responsibility for the statistical contributions to data science, statisticians should actively engage in the discussions. First the term data science is defined, and the technical changes that have led to a strong influence of data science on society are outlined. Next the systematic approach from CNIL is introduced. Prominent examples are given for ethical issues arising from the work of data scientists. Further we provide reasons why data scientists should engage in shaping morality around and to formulate codes of conduct and codes of practice for data science. Next we present established ethical guidelines for the related fields of statistics and computing machinery. Thereafter necessary steps in the community to develop professional ethics for data science are described. Finally we give our starting statement for the debate: Data science is in the focal point of current societal development. Without becoming a profession with professional ethics, data science will fail in building trust in its interaction with and its much needed contributions to society!

Replacements for Wed, 16 Jan 19

[5]  arXiv:1811.03435 (replaced) [pdf]
Title: Data Science as Political Action: Grounding Data Science in a Politics of Justice
Authors: Ben Green
Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG)
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