How can AI be used to identify harmful content?

Background

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.

Next steps

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

Source

This question was published as part of the set of ARIs in this document:

DSIT Areas of Research Interest 2024 GOV UK

Related UKRI funded projects


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    Funded by: Innovate UK

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    Why might this be relevant?

    The project focuses on using AI to identify harmful content online, specifically targeting children's safety.

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    Why might this be relevant?

    The project focuses on using AI to evaluate information pollution on social media, which aligns with the question of identifying harmful content online.

  • Shear: The next generation of video understanding technology to automate content moderation across the internet.

    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...

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  • Detox: Human-led AI to automate and radically improve online content moderation

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    The project focuses on creating safer online spaces by preventing, intervening, and supporting victims of online gender-based violence through advanced Machine Learning algorithms.

  • Tackling Child Exploitation in Live Streaming Applications

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  • ISIS: Protecting children in online social networks

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    Funded by: EPSRC

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    Funded by: EPSRC

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