Photography and Machine Learning: when Images Become Data Images

The increasing number of digital images produced and distributed today has been an argument of investigation for numerous scholars as well as the subject of vast literature on the usage and impact of such images on our society. While the pressing issue to the majority of scholars concerns the ability of the digital image to represent reality, an increasing amount of literature questions the role of digital images in the broader, networked context of artificial intelligence and machine learning. This paper traces the evolution of traditional photography to data images to describe the shift in the essential nature of images from the way they were consumed by the public to becoming a source of raw material to feed sophisticated autonomous learning machines that are able to recognise, categorise and classify objects, humans and spaces.

The paper analyses the use of image as evidence tracing a chronology of photography in relation to technological progress. The ability of computer vision combined with the continuous stream of images and machine learning capabilities offer a wide range of beneficial possibilities, but this technology can also be misused and raises numerous ethical questions. This paper, rather than attempting to ascertain an answer to this question, seeks to display its complexity to underscore and reiterate the risks of relying entirely on machines. Unconditional trust in technology not only does not protect us from the probability of human error, but it also reduces our critical capabilities.

Read More