Integrating user experience data into image algorithms to mitigate online harm

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 'valence', that is capacity to arouse feelings in the viewer. That arousal can make us feel happy, sad or angry and lead to laughing, crying or shouting as a result. This has traditionally been leveraged in the visual arts, through paintings, films, television, advertising and propaganda.

Meanwhile, computers still struggle to process semantics, that is the kind of deep rich meaning embedded in social media image content feeds. Unfortunately, this means that many algorithmic tools developed to mitigate online harm simultaneously pull out innocuous content yet fail to filter out appalling material. We know in advertising research that 'repetition is persuasion', yet the cumulative effects of consumption of an image feed over time is also unaccounted for. This has lead to repeated instances of dire outcomes as algorithms unwittingly automate the escalation of harm, by failing entirely to account for human factors; in this case, the emotional valence of image content and its systematic and networked consumption. Furthermore, these test sets are often tagged manually by exploited workers in developing countries, who are thus extensively exposed to the worst of internet content too. Effectively once again, just offshoring the personal and social problem alongside the business one.

This proposal is to optimise a fully functioning prototype image filter tool that accounts for users experience towards mitigating online harms, automatically recognising harmful content with high valence. It will allow the company to start the move from service to product, a well recognised bottleneck in the digital economy in the region, and deliver a preliminary application towards a SaaS model for private investment. Aligned with the forthcoming Online Harms Bill, the tool will be able to offer creative industries content creators with quantitative data on the level of valence in their image feeds to mitigate unwanted social and behavioural outcomes in the face of escalating liabilities and costs.