Part II: The tech industry – divided
Our first article made clear that technologists can contribute to today’s challenges. Many knowledge workers are working efficiently and able to offer help to those deep in the crisis that COVID-19 presents.
That’s not the whole picture though: a new layer of privilege is emerging, the ability to work remotely. The ramifications of the shift to more remote working are far-reaching. This article focuses on the new divides that are emerging in tech, and more broadly. It then looks at automation and data as key considerations in a post-COVID-19 world.
Contributors:
Rebecca J Parsons | Christoph Hassler
The new divides
2020 has delivered a crash course in remote working for many. The software industry, on the face of it, is well-suited to the new reality. Skilled individuals are used to connecting virtually, and businesses are in growing need of digital products and services.
Yet, the comfort and opportunity of remote working is not shared with all. The divide between those who can afford the hardware and infrastructure to make remote work possible and those who can’t will widen rapidly.
Even for those who are equipped to work from home, the ability to do this successfully depends on their private situation – space, cohabiting and family arrangements, reliable electricity and internet, the list goes on.
Who pays the highest price?
People in caregiving roles, which are mostly women, face double duties: constantly context switching between waged and domestic labour.
Children in remote schooling arrangements will find that education is not free, since it requires the tools to participate. Many children from lower income families, will struggle even more to keep up, and should they leave school as a result, may impact generations to come. NGOs are trying to fill the gaps - Thoughtworks has been partnering with REAP for several years to provide education to rural children in China. The teams worked through the early weeks of lockdown to deliver a mobile app in the knowledge that millions of children will only have access to a single smartphone per family for remote learning.
With the dawning era of location independence we are faced with a more obvious divide, this time between white collar and essential workers. Essential workers, and those in many other jobs, are required to show up in person – and these workers find themselves at significantly greater risk as a result (we explore the tech industry’s responsibility in this issue in Part V).
Automation looms
Location independence isn’t an option for manufacturing, and many other industries. Given the social distancing standards being imposed on factories, warehouses and other places where workers come together in big groups, companies will either have to rethink their logistics to accommodate new regulations, or cut out the human factor wherever possible. Again, low-skilled work, and therefore low-income groups, are being hardest hit with job losses.
Yet Artificial Intelligence (AI) still has severe limitations outside of a controlled environment. With its lack of empathy and incomplete context, AI may be able to replicate human actions, but not human instincts. While it’s possible, even preferable, to teach robots to repeat a set of movements, for example to build a car, we’re presented with a far greater challenge when it comes to acting in a complex environment filled with unexpected variables, like auto-piloting a car.
Given the new motivation to innovate in this area, we expect more movement in cutting out those parts of industry that are vulnerable to pandemics: human workers.
White collar jobs may begin to disappear in sectors that have clearly structured rules and processes, like legal work.
The question of data
If the implications of COVID-19 are represented in Maslow's hierarchy of needs, we would see that safety and protecting livelihoods would be the base of the pyramid. At the top, we would see concerns around topics such as data privacy. An individual's concerns may span the entire pyramid, regardless of income, of course. The risk is, while people are more concerned with protection of the basics, "top pyramid" concerns like data privacy, are sacrificed. This in turn could lead to a broader support for surveillance and more invasive tech.
Bars and restaurants can become hotspots for Corona outbreaks. In some regions pulling away from lockdown, patrons are required to provide their personal information. In the case of an outbreak, this information will be shared with the relevant public health department.
While good contact tracing apps go out of their way to anonymise their interactions and decentralize data, this is not the case with this practise. Having to give your name, contact address and time when you are visiting a certain place feels uncomfortable for a reason. Now it’s easy to establish where you have been, when and with whom. Certainly this information might be already available through social networks and Google, but there’s still a big hurdle for third parties to exploit that data, far more than just walking up to a restaurant and asking for it.
There are already cases where the police are pursuing this line of data collection. Taking the information given for containing Corona outbreaks and repurposing it for criminal investigations [1]. While privacy advocates say that this still falls into the realm of legality, it shows that everywhere personal data is stored, covetousness is created.
Exploring ethical tech
We explore the wider context of ethical tech in the most recent edition of Perspectives, looking in detail at the specific strategies and frameworks that can put technology-embracing enterprises on sounder ethical footing.
"Technologists have, for a long time, been operating with a utopian mindset. The assumption is technology can solve the world’s problems, and there’s no bad technology, it’s just sometimes put to bad uses."
The truth is, to produce positive ethical outcomes and minimize risks, technology has to be managed and monitored as actively as any other aspect of the business - perhaps even more so. View Perspectives.
