Integrating user experience data into image algorithms to mitigate online harm

作为数十年学术研究和商业应用的成果，该提案为算法如何解释最终用户体验提供了一个步骤。图像中包含不同程度的情感“效价”，即唤起观众感受的能力。这种唤醒可以使我们感到快乐，悲伤或愤怒，并导致大笑，哭泣或大喊大叫。传统上，这在视觉艺术中得到了利用，通过绘画、电影、电视、广告和宣传。与此同时，计算机仍然难以处理语义，即嵌入社交媒体图像内容提要中的那种深刻而丰富的含义。不幸的是，这意味着许多为减轻在线伤害而开发的算法工具同时会删除无害的内容，但却无法过滤掉令人震惊的内容。我们在广告研究中知道，“重复就是说服力”，但随着时间的推移，图像提要消费的累积效应也无法解释。这导致了可怕后果的反复发生，因为算法无意中自动化了伤害的升级，完全没有考虑到人为因素;在这种情况下，图像内容的情感价值及其系统和网络消费。此外，这些测试集通常由发展中国家受剥削的工人手动标记，因此他们也广泛接触到最糟糕的互联网内容。有效地再一次，只是离岸的个人和社会问题旁边的业务之一.这个建议是优化一个功能齐全的原型图像过滤器工具，占用户体验对减轻在线危害，自动识别有害内容与高效价.它将使该公司能够开始从服务到产品的转变，这是该地区数字经济中公认的瓶颈，并为私人投资提供SaaS模式的初步应用。与即将出台的《在线伤害法案》相一致，该工具将能够为创意产业内容创作者提供有关其图像馈送中效价水平的定量数据，以减轻在面临不断升级的责任和成本时不必要的社会和行为后果。

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.