scholarly journals Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks

2022 ◽  
Vol 104 ◽  
pp. 106029
Author(s):  
Mengtian Cui ◽  
Manuel Sebastian Mariani ◽  
Matúš Medo
Keyword(s):  
Temporal Effects ◽  
Evolving Networks ◽  
Algorithmic Bias

Spatiotemporal distribution of foraging in a marine predator: behavioural drivers of hotspot formation

2020 ◽  
Vol 635 ◽  
pp. 187-202
Author(s):  
T Brough ◽  
W Rayment ◽  
E Slooten ◽  
S Dawson
Keyword(s):  
Time Of Day ◽  
Spatiotemporal Distribution ◽  
Temporal Effects ◽  
Marine Predator ◽  
Marine Predators ◽  
Additive Mixed Models ◽  
Density Analysis ◽  
The Times ◽  
Passive Acoustic ◽  
Spatial Locations

Many species of marine predators display defined hotspots in their distribution, although the reasons why this happens are not well understood in some species. Understanding whether hotspots are used for certain behaviours provides insights into the importance of these areas for the predators’ ecology and population viability. In this study, we investigated the spatiotemporal distribution of foraging behaviour in Hector’s dolphin Cephalorhynchus hectori, a small, endangered species from New Zealand. Passive acoustic monitoring of foraging ‘buzzes’ was carried out at 4 hotspots and 6 lower-use, ‘reference areas’, chosen randomly based on a previous density analysis of visual sightings. The distribution of buzzes was modelled among spatial locations and on 3 temporal scales (season, time of day, tidal state) with generalised additive mixed models using 82000 h of monitoring data. Foraging rates were significantly influenced by all 3 temporal effects, with substantial variation in the importance and nature of each effect among locations. The complexity of the temporal effects on foraging is likely due to the patchy nature of prey distributions and shows how foraging is highly variable at fine scales. Foraging rates were highest at the hotspots, suggesting that feeding opportunities shape fine-scale distribution in Hector’s dolphin. Foraging can be disrupted by anthropogenic influences. Thus, information from this study can be used to manage threats to this vital behaviour in the locations and at the times where it is most prevalent.

Apparent Algorithmic Bias and Algorithmic Learning

2020 ◽  
Author(s):  
Anja Lambrecht ◽  
Catherine E. Tucker
Keyword(s):  
Algorithmic Learning ◽  
Algorithmic Bias

AI and Algorithmic Bias: Source, Detection, Mitigation and Implications

2020 ◽  
Author(s):  
Runshan Fu ◽  
Yan Huang ◽  
Param Vir Singh
Keyword(s):  
Source Detection ◽  
Algorithmic Bias

Quantifying Nonrandomness in Evolving Networks

2020 ◽  
pp. 1-13
Author(s):  
Pradumn Kumar Pandey ◽  
Mayank Singh
Keyword(s):  
Evolving Networks

Assessing and addressing algorithmic bias in practice

interactions ◽  
2018 ◽  
Vol 25 (6) ◽  
pp. 58-63 ◽  
Author(s):  
Henriette Cramer ◽  
Jean Garcia-Gathright ◽  
Aaron Springer ◽  
Sravana Reddy
Keyword(s):  
Algorithmic Bias

What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a Fantasy of Perfect Data

2021 ◽  
pp. 146144482110127
Author(s):  
Marcus Carter ◽  
Ben Egliston
Keyword(s):  
Virtual Reality ◽  
Learning Process ◽  
Learning Analytics ◽  
Soft Skills ◽  
Education Technology ◽  
Skills Training ◽  
Emerging Technology ◽  
Critical Issues ◽  
Vr Training ◽  
Algorithmic Bias

Virtual reality (VR) is an emerging technology with the potential to extract significantly more data about learners and the learning process. In this article, we present an analysis of how VR education technology companies frame, use and analyse this data. We found both an expansion and acceleration of what data are being collected about learners and how these data are being mobilised in potentially discriminatory and problematic ways. Beyond providing evidence for how VR represents an intensification of the datafication of education, we discuss three interrelated critical issues that are specific to VR: the fantasy that VR data is ‘perfect’, the datafication of soft-skills training, and the commercialisation and commodification of VR data. In the context of the issues identified, we caution the unregulated and uncritical application of learning analytics to the data that are collected from VR training.

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