Although there is already material evidence on the types of serious harms individuals encounter online, there still remain a number of emerging harms, where the evidence base is still yet to mature (e.g. epilepsy trolling, online animal abuse). SOH would like to close this significant gap in understanding the impact of encountering different types of serious harms online and understanding the best approaches to measuring the impact of the Online Safety legislation.
SOH highlights the importance of Media Literacy in the digital age and asks for further studies to uncover barriers to engagement as well as the effectiveness of DSIT programmes. This issue closely relates to Counter-Disinformation interventions, which requires evidence for its effect on bystanders, topic specific disinformation and what tools can be used to combat this issue.
Research on Safety Technology would greatly develop SOH’s understanding of the relationship that DSIT online safety objectives have with the technology market today. A primary focus lands on improving Age Assurance (AA) measures. This includes ensuring transparency and assessing opportunities for the sector.
If you are keen to register your interest in working and connecting with DSIT Digital Technology and Telecoms Group and/or submitting evidence, then please complete the DSIT-ARI Evidence survey - https://dsit.qualtrics.com/jfe/form/SV_cDfmK2OukVAnirs.
Please view full details: https://www.gov.uk/government/publications/department-for-science-innovation-and-technology-areas-of-research-interest/dsit-areas-of-research-interest-2024
This question was published as part of the set of ARIs in this document:
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Funded by: Innovate UK
Lead research organisation: UNIVERSITY OF WOLVERHAMPTON
The project focuses on developing a child-centered shield against harmful online content, directly addressing the risk of generative AI creating harmful content.
The culmination of decades of academic research and commercial application, this proposal offers a step change on how algorithms account for the end user experience. Images hold within them varying degrees of emotional '...
Funded by: Innovate UK
Lead research organisation: RHIZOMETRIC DESIGN LIMITED
The project focuses on mitigating online harm through image algorithms, which directly relates to the question about generative AI evolving to avoid detection faster.
In this project, Unitary Ltd and Oxford University will develop novel algorithms to address the core challenges of video moderation. This technology will form Unitary's new product, _Shear_, to automatically detect harmf...
Funded by: Innovate UK
Lead research organisation: UNITARY LTD
The project aims to automate content moderation for harmful videos, which is directly related to the risk of generative AI creating undetectable content.