What are the implications of 'visually orientated algorithms' for photojournalism?

“视觉导向算法”对新闻摄影有何影响？

• 批准号: 2479786
• 金额: --
• 依托单位: London School of Economics and Political Science
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2479786
• 关键词: What; implications; visually; orientated; algorithms

In recent years, journalists have been joined in newsrooms by an array of software designed to automate aspects of their work. Many of these automated systems perform on tasks involving large quantities of structured data, for example sports and financial journalism, and they have tended to neglect areas involving more creative analysis. This situation is starting to change with advances in machine learning and natural language generation technologies. Some efforts have been made to understand the implications of these technologies for written journalism as their adoption becomes more widespread. By contrast the implications of these technologies for visual journalism, and photojournalism specifically, remain entirely neglected as a topic. Yet visually orientated algorithms have the potential to be every bit as transformative for visual journalism as their text orientated counterparts are now proving themselves to be for written journalism. This lack of inquiry into this area likely reflects the fact that the technology that underpins visually orientated algorithms still lags several steps behind those focused on analysing more quantitative data and text, but recent advances in artificial intelligence and machine learning mean that this gap is now rapidly closing. Recognising a gap in the empirical research around these topics, I wish to investigate the present and potential future implications of these visually orientated algorithms for photojournalism, a field which I characterise as encompassing both amateur and professionally produced photography and video used to illustrate or explain news and current events. Further to this I want to review whether these implications are broadly positive or negative, and to consider what measures or steps might be put in place to anticipate or mitigate some of the negative consequences. In particular, I will consider the following core questions. What do these technologies mean for the purpose of photojournalism in society? Photojournalism's role has traditionally been understood as one of reporting the news through singular visual artefacts broadly 'recognised by everyone within a public culture' I will question whether these technologies cause a shift away from this, and perhaps towards something far more fragmented and individualized. Building on the previous question, I will ask what these technologies mean for public confidence in photojournalism. The field's reputation has already been battered by the widespread use of misattributed imagery, image manipulation software, and ethical misconduct by a minority of photographers and editors. Does greater automation offer the promise of more easily catching out and exposing these abuses, or might it inadvertently exacerbate them? I will consider what the implications of these technologies are for the photojournalistic economy. I would ask whether these systems offer to make it more efficient, liberating journalists from banal and time-consuming tasks, or whether they might threaten technological unemployment. This would include considering how these technologies could aggravate trends already seen as undermining journalism's economic model. I would examine how these technologies might exacerbate biases and structural inequalities that already exist in visual journalism. For example, considering the problematic history of representation in the field, might biased algorithm training data exacerbate these tendencies further. I would also consider the danger of developers incorporating intentional biases designed to further the aims of media owners and to exploit an increasingly polarised political and media landscape.

近年来，记者们通过一系列旨在实现工作自动化的软件加入了新闻编辑室。许多这样的自动化系统执行涉及大量结构化数据的任务，例如体育和财经新闻，并且它们往往忽略涉及更具创造性分析的领域。随着机器学习和自然语言生成技术的进步，这种情况开始改变。随着这些技术的采用变得更加广泛，人们已经做出了一些努力来了解这些技术对书面新闻的影响。相比之下，这些技术对视觉新闻，特别是新闻摄影的影响，作为一个话题仍然完全被忽视。然而，面向视觉的算法有可能为视觉新闻业带来变革，就像面向文本的算法现在正在证明自己对书面新闻业的变革一样。对这一领域缺乏调查可能反映出这样一个事实：支撑视觉导向算法的技术仍然落后于那些专注于分析更多定量数据和文本的技术，但人工智能和机器学习的最新进展意味着这一差距正在迅速缩小。认识到围绕这些主题的实证研究存在差距，我希望研究这些视觉导向算法对新闻摄影的当前和潜在的未来影响，我将这个领域描述为涵盖业余和专业制作的摄影和视频，用于说明或解释新闻和时事。除此之外，我想回顾一下这些影响是积极的还是消极的，并考虑可以采取哪些措施或步骤来预测或减轻一些负面后果。我将特别考虑以下核心问题。这些技术对于社会新闻摄影意味着什么？新闻摄影的作用传统上被理解为通过“公共文化中的每个人都认可”的单一视觉制品来报道新闻，我会质疑这些技术是否会导致这种转变，甚至可能转向更加碎片化和个性化的东西。基于上一个问题，我将问这些技术对公众对新闻摄影的信心意味着什么。该领域的声誉已经因少数摄影师和编辑广泛使用错误图像、图像处理软件以及道德不当行为而受到损害。更高程度的自动化是否能够更容易地发现并揭露这些滥用行为，或者是否会无意中加剧这些滥用行为？ 我将考虑这些技术对新闻摄影经济的影响。我想问的是，这些系统是否可以提高效率，将记者从平庸且耗时的任务中解放出来，或者它们是否可能威胁到技术性失业。这将包括考虑这些技术如何加剧已经被视为破坏新闻经济模式的趋势。我将研究这些技术如何加剧视觉新闻业中已经存在的偏见和结构性不平等。例如，考虑到该领域代表性问题的历史，有偏见的算法训练数据可能会进一步加剧这些趋势。我还会考虑开发商纳入故意偏见的危险，这些偏见旨在进一步实现媒体所有者的目标并利用日益两极分化的政治和媒体格局。