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

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.