May 26, 2021
Prompt 13: How can we know that current knowledge is an improvement upon past knowledge?
Object 1: CBS article headline
Published February 2021, this CBS article highlights the highly volatile valuation of Bitcoin which made thousands around the world insanely rich, justifying the Bitcoin fever. This object raises interesting questions about how we ‘know’ and how we determine whether something has improved or not.
People are obsessed with bitcoin, and automatically consider it an improvement due to the feelings associated with it: self-expression, excitement, power. This is because bitcoin offers a thrill equivalent to gambling, is extremely efficient, allows people to create an identity in a financial space and as the article demonstrates, enables regular people to become millionaires, with over 101,554 accounts having $1 million+ worth of bitcoin. The psychology behind how we determine whether current knowledge is an improvement upon past knowledge is demonstrated effectively by bitcoin. Humans tend to ignore the drawbacks of new technologies, such as the detrimental environmental impact of bitcoin, and automatically deem the technology an improvement when it serves our self-interest and helps us achieve things that previously seemed unachievable by past knowledge systems (eg: paper currency).
At the same time, bitcoin received a positive welcome as it is the first form of decentralised cryptocurrency, and there isn’t any past knowledge system available for comparison. Since it is the first revolutionary technology of its kind, it took the world by storm. As there aren’t any direct previous models to compare it to, its shortcomings (volatility, little to no regulation) and potential threats (lack of authorities which allows illegal content to be shared through its blockchain without censorship) were conveniently overlooked in the face of its ability to make people rich, with over ‘8,110 accounts with $10+ million worth of bitcoin’. This enriches the exhibition as it shows the difficulties in fairly assessing new knowledge when direct links with past knowledge or prior models don’t exist.
Object 2: Anti-robot protest in California, May 2019
This is an image of an anti-robot protest by longshore workers in Southern California (May 2019). They protested in response to the automation of the port sector which threatened their livelihoods.
Automation of port-related jobs at the Port of Los Angeles TraPac have received a mixed welcome. Although they are an impressive scientific feat, a modern-day industrial revolution, there are plenty of luddites resisting this progress. The efficiency of these robotic carriers and cranes is deemed valueless when it comes to the issue of unemployment and the threat to 9,000+ jobs. A strand of our criteria for ‘improvement’ is the knowledge in question’s relationship with humans. In this case, the improved robots aren’t aiding humans but instead potentially replacing over 1200 jobs. Hence, the workers protesting don’t consider robots an improvement (despite the technology’s potential to reshape the workforce) as their perceptions of improvement are fear-driven, loss-averse, and strongly based on human values. Our society tends to reject new technology when it replaces, rather than strengthens our humanity and the improvement factor is determined by whether the new knowledge reinforces current social conditions or undermines them.
However, depending on our role in society, our perception of ‘improvement’ can vary. For the Port Commissioners, the adoption of robotics promises increased productivity and maximised profits and is, through their lens, an improvement from past knowledge (manual labour). They also argue that the deployment of robots will create more fulfilling job experiences as automating monotonous tasks enables employees to do more creative and stimulating work, despite the workers’ obvious disfavour.
In this way, the object enhances the exhibition as it shows how the relationship between past knowledge and current knowledge in the context of ‘improvement’ can change depending on our values, concerns and potential benefits from the knowledge in question.
Object 3: picture of researcher working with Crispr-Cas9 system in a Berlin lab, 2018
This is an image of a researcher performing a CRISPR/Cas9 process at the Max-Delbrueck-Centre for Molecular Medicine in Berlin during May of 2018.
CRISPR technology is a prime example of how a presumed improvement upon past knowledge can be considered a failure depending on the lens through which the knowledge is perceived. Although the use of the CRISPR-Cas9 system for gene editing by the Transgenic Core Facility at the centre has shown the potential to revolutionise gene therapy and eradicate hereditary diseases, if misused, it can generate off- target and unintended consequences and be used for biological attacks and genetic enhancement- insidiously bringing to life an undesirable part of science fiction. The ethical dilemmas raised by CRISPR counteract its wide applications in the medical field and is indicative of the ways in which ‘we know’. The researchers themselves have opposed the unquestioning adoption of this technology and conduct in depth research to find editing techniques that don’t lead to misediting in unintended regions of the genome. This shows that humans’ criterion for improvement isn’t dogmatic, but instead constantly developing according to new findings and insights.
Moreover, attitudes towards ‘improvement’ can differ depending on several factors such as the potential benefit from technology etc. An example is economics status. Individuals from a lower economic rung have lower access to CRISPR technologies and it is virtually impossible for them, financially, to partake in gene therapy. Due to the issue of economic disparity, for them, CRISPR isn’t an ‘improvement’ from previous medicinal technologies as they are unable to access it and hence, can’t benefit from it. This enriches the exhibition by showing the role ‘fairness’ plays in our improvement criteria, as new knowledge can never be ‘improved’ if it only favours a certain demographic, while exacerbating inequality for another.