Neuroethics

BCIs – Increasing the Wealth Gap

Kalie Uberti


Introduction

The term brain-computer interface is often associated with thoughts of a science-fiction world filled with cyborgs and aliens, but, as far off in the future as it may seem, many companies and universities today are working to make that vision a reality today. Brain-computer interfaces (BCIs) are machines that convert neural impulses to real-world outputs. These machines could make a huge difference in the lives of amputees, people born with limb differences, and people who are paralyzed [2].  Those are the intended clients for most BCIs, but it would be imprudent to assume the development of such a device would stop there. The companies envisioning these devices say they will be able to “connect minds”, allowing a type of brain-to-brain communication. They say one will be able to access the internet in their brain [3]. While each of these benefits has its own ethical implications, the most concerning one comes from the societal impacts of brain-computer interfaces as a technology allowing unprecedented mental augmentation: the exacerbation of the wealth gap between rich and poor. Image result for brain machine interface

Figure 1. Diagram showing how signals (inputs) from the brain in a BCI can be processed and translated into movements in an external device (outputs) [1}.

 

Historical Background

Beginning in the 1970s, skill bias (favoring of skilled over unskilled labor) by companies and organizations accelerated resulting in a sharp increase in within-group inequality [4]. Technology replaced many working-class jobs causing people to have to switch their field as their former field became automated [5]. Due to increased competition for highly skilled workers, the pay gap between those with college degrees and those without, has been widening [5]. Today, on average, American workers with a bachelor’s degree earn $1 million more over their lifetimes than those who only have a high school diploma [5].

This is eerily similar to what happened during the Industrial Revolution in Britain in 1800. During that time, there were many non-agricultural workers with well-paying jobs, such as handloom weavers [6]. While the industrialization of spinning first increased the demand for these workers, the arrival of powered equipment and machinery later had dire consequences on the lives of the workers. If they stayed in the same field their earnings fell over the next few decades by as much as 90% [6].

 

Modern Applications

This can be applied to the rise of brain-computer interfaces in a multitude of ways. First, the voluntary adoption of brain-computer interfaces may force the adoption on others. To take the historical example, the spinning machines from the 19th century were invented because they were profitable [4]. People would be more effective, and thus more profitable, if they are able to have more information by having a direct connection with the internet via their brain. They would be able to communicate more effectively by using the brain-to-brain communication. This would either force people who are hesitant to adopt the technology to do so or be left in the dust as more companies seek to hire people who will earn them the most profit.

While some may have the choice, others may not be so lucky. While some experts will argue that price drops as technology ages, the initial price of any new technology is exorbitant. The noninvasive brain computer interfaces of today, even with their limited capabilities, cost around $5,000-$10,000 [2]. This means that when such a device is marketed to the average consumer, the people who can afford to can adopt it quickly to stay ahead which would allow them to continue to buy the latest versions of BCI technology. Even if, one day, the technology becomes cheap enough for impoverished families to afford, the rich will have already moved on to even more expensive, updated versions of the technology. According to Jerry Kaplan, a PhD in computer science, “The benefits of automation naturally accrue to those who can invest in the new systems," [7]. What’s more, due to the exponential progress of technology, this will lead to an even greater exacerbation in the wealth gap than has ever been seen before [8].

Even with today’s technologies, inequality within advanced and emerging markets has increased. Former United States president Barack Obama called the widening income inequality the “defining challenge of our time” [9]. Some inequality is not necessarily problematic, as it allows people to compete and move ahead in life. Despite this, widening income inequality is a major issue, for more reasons than quality of life. It can have significant implications for macroeconomic stability, concentration of political power, political stability, and investment potential[9]. 


Figure 2. Graph showing increases in income inequality between the middle and upper class in established and developing countries over the past nineteen years [9].

 

Conclusion

The introduction of a technology allowing for this amount of instantaneous mental improvement is beyond anything the world has seen before. While it will open up dream-like opportunities for some, those who cannot afford it will be left off even worse than they were before. As has been shown by historical examples, this could cause the wages of workers who do not or cannot seek out such improvements to fall dramatically [6]. Already, with today’s technologies, the wage gap has widened between the rich and poor and may increase exponentially with the progress of technology [5] [8]. Though brain-computer interfaces will substantially improve the lives of their initial audience, much thought must go into the ethical implications before developing a brain-computer interface for the general consumer so as to circumvent the worsening of the wage gap.


 

References 

[1] Wu, James, and Rajesh P.N. Rao. (14/5/2019). Melding Mind and Machine: How Close Are We?. The Conversation. theconversation.com/melding-mind-and-machine-how-close-are-we-75589. Retrieved: 28/06/2019.

 

[2] Shih, Jerry J, et al. (03/2012). Brain-Computer Interfaces in Medicine. Mayo Foundation. www.ncbi.nlm.nih.gov/pmc/articles/PMC3497935/. Retrieved: 18/06/2019. 

 

[3] Adler, Jerry. (01/05/2019). Why Brain-to-Brain Communication is No Longer Unthinkable. Smithsonian. www.smithsonianmag.com/innovation/why-brain-brain-communication-no-longer-unthinkable-180954948/.  Retrieved: 19/06/2019. 

 

[4] Acemoglu, Daron. (2003). Technology and Inequality. NBER. www.nber.org/reporter/winter03/technologyandinequality.html. Retrieved: 18/06/19.

 

[5] Long, Heather. (12/10/2015). Stephen Hawking: Technology is Making Inequality Worse. CNN. money.cnn.com/2015/10/12/news/economy/stephen-hawking-technology-inequality/index.html. Retrieved: 18/06/19.

[6] Hutchinson, Martin. (06/07/2017). The Robot Revolution Resembles the First Industrial Revolution. GPI Global Policy Institute. globalpi.org/research/the-robot-revolution-resembles-the-first-industrial-revolution/. Retrieved: 19/06/2019. 

 

{7] Bowles, Nellie. (12/03/2016). Our Tech Future: the Rich Own the Robots While the Poor have ‘Job Mortgages’. The Guardian. www.theguardian.com/culture/2016/mar/12/robots-taking-jobs-future-technology-jerry-kaplan-sxsw. Retrieved: 19/06/2019. 

 

[8] Chandler, David L. (06/03/2013). How to Predict the Progress of Technology. MIT News. news.mit.edu/2013/how-to-predict-the-progress-of-technology-0306.Retrieved: 18/06/2019. 

 

[9] Dabla-Norris, Era, et al. (06/2015). Causes and Consequences of Incomes Inequality: A Global Perspective. IMF Staff Discussion Note. www.imf.org/external/pubs/ft/sdn/2015/sdn1513.pdf. Retrieved: 18/06/2019.



    Kalie Uberti

    Kalie Uberti


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