Artificial Intelligence, In its infrastructure, is an algorithm; a sequence of information which can be pursued to deal with a precise problem. James Bridle advocates that at the roots of our mind about new technology remains an alarming problem. “We model our own minds on our understanding of computers, and believe they can solve all our problems if we supply them with enough data and make them fast enough to deliver real-time analysis.” According to Moore’s law, which calculates that computer processing power will double every two years, Bridle counteracts this point by bringing up the Gate’s law, which advocates that the benefits are neutralised by the inflation in the software of redundant coding.
Kate Crawford, a researcher at Microsoft during her talk about Social and Ethical Impacts of Artificial Intelligence talked about a situation when Julia Angwin, along with five other journalists exposed a flawed algorithmic system being used in courts. “Northpointe has used this software platform throughout courtrooms in the US. What it does is it gives a criminal defendant a number between one and ten, to indicate the risk of them being a violent offender in the future—so it’s basically like a recidivism risk. And what she found in this big investigation was that basically, black defendants were getting a false positive rate of twice that of white defendants.” (Kate Crawford, The Social and Ethical impacts of Artificial Intelligence, 2016 ) The insight that Crawford gave helps us to understand the weakness of giving control to an algorithm set by an individual to make unbiased decisions without the involvement of race in this scenario.
James Bridle also talks about algorithm bias from software in the US, made to assist with the sentencing requirements. “When someone was convicted in court, this computer programme would suggest how long they should go to jail for.” After an analysis of the software, it was established that it was giving people of colour, a longer, more vindictive punishment compared to people not of colour. The results of the analysis surprised a lot of people who believed that software or artificial intelligence was completely neutral or that technology is a levelling force that unites us, makes us all equal and allows us to make better-unprejudiced decisions about the world. Unfortunately, this was not the case, as all the software had to evaluate was what we were doing already; as we are building complex expert systems based on our own history which is profoundly prejudiced and discriminatory in many ways. Which concludes that according to Bridle, what we need in the field of AI and Algorithms are a massive democratisation of these technologies.
For artificial intelligence to not be prejudiced perhaps it needs to not reflect the world but a utopian model of a perfect world. Secondly, this requires the rejection of a flawed belief that artificial intelligence and it’s algorithms are objective. Narayanan (Princeton Prof, Al Ethics, Digital Privacy) had classified such flawed belief as an accuracy fetish, which is “the way big data has allowed everything to be broken down into numbers which seem trustworthy but conceal discrimination.
“The people in these rooms designing these systems look like each other, think like each other, and come from generally speaking very upwardly-mobile, very wealthy kind of sectors of society.” Crawford talks about how many of the people creating the set of instructions and artificial intelligence are coming from replicable backgrounds and are part of an overall similar society who design artificial intelligence to think like them which could lead to discrimination among people who cannot relate to them. So they’re mapping the world to match their interests and their way of seeing. And that might not sound like a big deal. But it is a huge deal when it comes to the fact that certain ways of life simply don’t exist in these systems. (Kate Crawford, Social Ethical Impact, 2016)
Group Work
As a group, we had to connect and create a scenario with tarot cards that were provided to us. The scenario that we constructed via the cards provided to us was of a researcher in San Diego, in 2021 doing research. Our goal was to get rich during the crisis of social immobility. Our chosen solution was to develop an AI to replace cheap labour as computers and programs will require less care and maintenance when compared to a human workforce who will need safety precautions and other legal requirements. In turn, this can also solve social immobility issues as the class of people the workers will belong to will be compelled to find alternative sources for employment. As a digital media designer, I need to think about how AI could affect me from an employment standpoint and if it could replace me in the future to do digital media designs with the right set of algorithms. I will have a unique point of view in designing which could be subjective and hard to replicate for a machine.
References
Bhatia, R. (2019). Biased AI: Princeton study indicates race and gender bias has crept into machine learning algorithms. [online] Analytics India Magazine. Available at: https://analyticsindiamag.com/biased-ai-princeton-study-indicates-race-gender-bias-crept-machine-learning-algorithms/ [Accessed 20 Nov. 2019].
Bridle, J. (2020). YouTube. [online] Youtube.com. Available at: https://www.youtube.com/watch?v=11TbXEKRXnA [Accessed 4 Dec. 2019].
Microsoft Research. (2018). Keeping an Eye on AI with Dr. Kate Crawford – Microsoft Research. [online] Available at: https://www.microsoft.com/en-us/research/blog/keeping-an-eye-on-ai-with-dr-kate-crawford/ [Accessed 4 Jan. 2020].
Self, W. (2018). New Dark Age by James Bridle review – technology and the end of the future. [online] the Guardian. Available at: https://www.theguardian.com/books/2018/jun/30/new-dark-age-by-james-bridle-review-technology-and-the-end-of-the-future [Accessed 12 Nov. 2019].
Professional Practices (Aby Jose) 