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Democratisation of Usable Machine Learning in Computer Vision [article]

Raymond Bond, Ansgar Koene, Alan Dix, Jennifer Boger, Maurice D. Mulvenna, Mykola Galushka, Bethany Waterhouse Bradley, Fiona Browne, Hui Wang, Alexander Wong
2019 arXiv   pre-print
In this paper, we undertake a SWOT analysis to study the strengths, weaknesses, opportunities, and threats of building usable ML tools for mass adoption for important areas leveraging ML such as computer  ...  The paper proposes a set of data science literacy criteria for educating and supporting lay-users in the responsible development and deployment of ML applications.  ...  The concepts presented in this paper complement perspectives to other ethical positions, such as [9] and the IEEE P7003TM working standard, and provide a starting point for engaging in the responsible  ... 
arXiv:1902.06804v1 fatcat:jalnwcgyuvg6pek2u4fmf2tape

ARTIFICIAL INTELLIGENCE'S ALGORITHMIC BIAS: ETHICAL AND LEGAL ISSUES
ПРЕДВЗЯТОСТЬ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА: ВОПРОСЫ ЭТИКИ И ПРАВА

Yu. S. Kharitonova, Lomonosov Moscow State University, V. S. Savina, F. Pagnini, Plekhanov Russian University of Economics, LOYTEC Electronics GmbH
2021 Вестник Пермского университета Юридические науки  
The legal community sees the opportunity to solve the problem of algorithmic bias through various kinds of declarations, policies, and standards to be followed in the development, testing, and operation  ...  At the same time, self-learning algorithms create or reproduce inequalities between participants in circulation, lead to discrimination of all kinds due to algorithmic bias.  ...  ., P7003™ Standard for Algorithmic Bias Considera- Bessi A., Scala A., Caldarelli G. et al. Anatomy of tions: Work in Progress Paper.  ... 
doi:10.17072/1995-4190-2021-53-488-515 fatcat:u3vq3nktafailmc6knaiq67qcu

FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions [article]

Sebastian Schelter, Yuxuan He, Jatin Khilnani, Julia Stoyanovich
2019 arXiv   pre-print
Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational  ...  As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions.  ...  for algorithmic bias considerations: work in progress paper. In Proceedings of the [41] Indre Zliobaite. 2017. Measuring discrimination in algorithmic decision making.  ... 
arXiv:1911.12587v1 fatcat:5jfebuy47jgdfi32erakds5fny