20 research outputs found
Detection of Offensive and Threatening Online Content in a Low Resource Language
Hausa is a major Chadic language, spoken by over 100 million people in
Africa. However, from a computational linguistic perspective, it is considered
a low-resource language, with limited resources to support Natural Language
Processing (NLP) tasks. Online platforms often facilitate social interactions
that can lead to the use of offensive and threatening language, which can go
undetected due to the lack of detection systems designed for Hausa. This study
aimed to address this issue by (1) conducting two user studies (n=308) to
investigate cyberbullying-related issues, (2) collecting and annotating the
first set of offensive and threatening datasets to support relevant downstream
tasks in Hausa, (3) developing a detection system to flag offensive and
threatening content, and (4) evaluating the detection system and the efficacy
of the Google-based translation engine in detecting offensive and threatening
terms in Hausa. We found that offensive and threatening content is quite
common, particularly when discussing religion and politics. Our detection
system was able to detect more than 70% of offensive and threatening content,
although many of these were mistranslated by Google's translation engine. We
attribute this to the subtle relationship between offensive and threatening
content and idiomatic expressions in the Hausa language. We recommend that
diverse stakeholders participate in understanding local conventions and
demographics in order to develop a more effective detection system. These
insights are essential for implementing targeted moderation strategies to
create a safe and inclusive online environment.Comment: 25 pages, 5 figures, 8 table
Algorithmic loafing and mitigation strategies in Human-AI teams
This research work was initiated under the Scottish Informatics & Computer Alliance (SICSA) Remote Collaboration Activities when the first author was working at the University of St Andrews, UK. We would like to thank the SICSA for the partial funding of the research work.Exercising social loafing – exerting minimal effort by an individual in a group setting – in human-machine teams could critically degrade performance, especially in high-stakes domains where human judgement is essential. Akin to social loafing in human interaction, algorithmic loafing may occur when humans mindlessly adhere to machine recommendations due to reluctance to engage analytically with AI recommendations and explanations. We consider how algorithmic loafing could emerge and how to mitigate it. Specifically, we posit that algorithmic loafing can be induced through repeated encounters with correct decisions from the AI and transparency may combat it. As a form of transparency, explanation is offered for reasons that include justification, control, and discovery. However, algorithmic loafing is further reinforced by the perceived competence that an explanation provides. In this work, we explored these ideas via human subject experiments (n = 239). We also study how improving decision transparency through validation by an external human approver affects performance. Using eight experimental conditions in a high-stakes criminal justice context, we find that decision accuracy is typically unaffected by multiple forms of transparency but there is a significant difference in performance when the machine errs. Participants who saw explanations alone are better at overriding incorrect decisions; however, those under induced algorithmic loafing exhibit poor performance with variation in decision time. We conclude with recommendations on curtailing algorithmic loafing and achieving social facilitation, where task visibility motivates individuals to perform better.Publisher PDFPeer reviewe