357,327 research outputs found
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie
Logics and practices of transparency and opacity in real-world applications of public sector machine learning
Machine learning systems are increasingly used to support public sector
decision-making across a variety of sectors. Given concerns around
accountability in these domains, and amidst accusations of intentional or
unintentional bias, there have been increased calls for transparency of these
technologies. Few, however, have considered how logics and practices concerning
transparency have been understood by those involved in the machine learning
systems already being piloted and deployed in public bodies today. This short
paper distils insights about transparency on the ground from interviews with 27
such actors, largely public servants and relevant contractors, across 5 OECD
countries. Considering transparency and opacity in relation to trust and
buy-in, better decision-making, and the avoidance of gaming, it seeks to
provide useful insights for those hoping to develop socio-technical approaches
to transparency that might be useful to practitioners on-the-ground.
An extended, archival version of this paper is available as Veale M., Van
Kleek M., & Binns R. (2018). `Fairness and accountability design needs for
algorithmic support in high-stakes public sector decision-making' Proceedings
of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18),
http://doi.org/10.1145/3173574.3174014.Comment: 5 pages, 0 figures, presented as a talk at the 2017 Workshop on
Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017),
Halifax, Canada, August 14, 201
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