I’ve been studying all things algorithms this week and found it to be a massively complex yet fascinating topic. It almost feels as if it would be impossible to fully comprehend the scale and spread of algorithms and the influence they have on our daily lives.
To that end, this week’s content on my Lifestream blog has helped me to start make sense of it.
My ‘How algorithms rule the world‘ post helped me gain some perspective about how computer based algorithms can affect the physical lives of every day users. I firstly considered this from an educational point of view however my thinking expanded somewhat after considering the policing example within the article. I now feel that algorithmic culture has a direct influence on societal culture.
I am fascinated not only with the use of algorithms to benefit large volumes of people, but also their role in predicting the future based on likelihood and probability. This theme was touched upon in my cyberpunk-related post with a reference from Red Dwarf.
My final two entries explored social factors (podcast) and big data influence (lecture) when experiencing algorithms on the internet. It was exciting to then have the opportunity to extend this knowledge into my final task.
My studying for the week concluded with a mini-experiment that I conducted in partnership with Chenée. This was a great opportunity to learn first-hand about the amalgamation of social and material factors in influencing an online experience. Our findings complimented Enyon (2013) in that our options are often influenced by the trends set by the wider, global population.
References
Enyon, R. (2013). The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology 38(3): pp. 237-240.
Thanks for this weekly summary, Stuart – it’s clear you’ve been busy again over the last seven days as we’ve explored ideas around algorithmic culture.
I’ve just enjoyed looking through the collaborative study of algorithmic culture your undertook with Chenee: what a wonderful way of demonstrating similarity and difference dependent on your online activity! Although you had deleted your previous history, I was still surprised by the high level of similarity between the results that came up in the early stages: I somehow imagined that the sophisticated workings within the algorithmic ‘black box’ would still have a way of more directly targeting and differentiating between you. It was particularly interesting then to see how the results varied at different times thereafter. I suppose the question that immediately springs to mind is how, if at all, your future use of the likes of Google, Amazon, SkyScanner and so on will be affected based upon your experiment? In fact your study could almost have included an afterword where both you and Chenee spent a few moments reflecting on how this knowledge about the the working of the different algorithms, might in turn affect your approaches in the future. All the same, thanks for a thoroughly enjoyable and thought-provoking piece of work.