I am an engineer by training three times. The third time was in Japan. On one September my father came to visit me and we had many fascinating conversations. By far, one of my favourite subjects was his theory of expansion. He doesn't call it that. He may not even recall the conversation we had but the theory of expansion states that:
"Like the Universe's overwhelming tendency to expand, so too does everything living in it seek to mimic that expansion" — Goloatshoene Moiloa.
He went on further to say that humans were special because they do not merely just expand in their physical form but in their mental capacity as well. It is out of this desire to expand that AI continues to evolve to meet the limits of the human imagination. It also, inadvertently ventures outside of it.
The Problematics
We have many examples of this inadvertence. In 2020 alone advanced technologies were behind: Britain delaying effective Covid19 spread prevention methods, a Telegram bot app that removed the clothing off pictures of women, "universal" technical tools like Twitter and Zoom perpetuating racial discrimination and language models being praised for their ability to smash certain academic benchmarks when their real life applications continuously prove to be a threat to already discriminated, marginalised, oppressed and vulnerable communities.
Solutions Under Development
These and many other stories have brought the dangers of AI into the consciousness of users and creators of AI. Experts concerned with the field have formalised academic pursuits to assist in better understanding the issues at play. Three points of intervention commonly identified are the:
- Machine Conceptualisation (MC) phase which deals with problem definitions of things we try to solve
- Machine Development (MD) phase which deals with technicality around conducting analysis
- Machine Release (MR) phase which deals with best practices of developmental release as well as policy development
For the MC stage primary concerns are around the ethics of the problems we are trying to solve and the metrics we deem fit to represent them. Concerns within the MD phase include the secretive nature of most of the algorithms we utilise to make decisions. In the MR phase, concerns are twofold — both in the release of datasets and models into the open source world, and in the development of best practice, policy and other regulatory strategies.
Is It Enough?
It is assumed that we would have dodged any major threats of pending doom by AI if we cover our bases in these three areas. Mitigation techniques are important to incorporate but they fail to address the fact that machines function within the hegemony. Any machine developed within the realm of the hegemony lies subject to its rules and practices. Machines that follow these practices serve the status quo. And most likely at the expense of someone or something else.
This hegemony is established in an AI born of a history of war, modern statistics originating from eugenics, commodifying people and their information and scientific principles founded upon rationality and functionalism. This singular view violates generally speaking, indigenous ways of knowing but more importantly indigenous ways of being — Ways of knowing and being founded upon the eternal pursuance of unification.
The Problem Is Us
The unintended impact of our machines comes as no surprise when we understand the underlying origins they embody. A technology is incapable of tending to the diversity of paradigms that the technology is meant to serve when it has embedded in it harmful points of view.
A large part of being "good", is being held accountable for the points of view we hold and pass onto our machines. Building "good" AI means expanding its founding principles to involve advanced practices. It involves developing practices that do not center AI development around people who already benefit from the status quo but instead centers those who stand to be harmed by it. It means expanding the function of AI beyond individual profit and capital gain.
We Need a Shift
The push for Machine driven decision making is motivated by a desire for insight into the world. We want to know how best to navigate it. Through AI our imagination for wanting has been expanded beyond anything we ever thought we might've guessed. But with increased opportunities for procuring newly imagined wants and the excitement surrounding the possibility of their materialisation as quickly as possible means that understanding whether these wants and their methods of realisation are good or bad is a complicated process given different contexts.
Shifting the direction of this expansion is a process that requires us to consider the careful steps to take with these technologies on our journey into executing a successful human-machine hybrid future. The universe expands in a manner that is chaotic, let us not be tempted to replicate its chaos...