Is AI squeezing the life out of data-capturing human workers in the south? Or is it a new, evolved form of life itself? Noema debates itself
“Cute wes anderson robot, flowers and leaves woven in and around its body, deep in thought, hyperrealistic, Kodak photographic light”. AG prompt to MidJourney
It’s a sign of how accelerated the discussion about new forms of AI are: the same publication (the consistently inspiring Noema) runs two pieces in the same time-frame: one of which accuses AI of cheapening human life, the second of which asserts that AI is a new stage in the evolution of life itself.
The first piece (under Noema’s Technology and the Human strand) is by Adrienne Williams, Milagros Miceli and Timnit Gebru, at the Distributed AI Research (DAIR) Institute. Gebru is the institute’s founder and executive director, previously co-lead of the Ethical AI research team at Google.
Their piece’s emphasis is on how much expoilted, developing-world labour is intimately involved with the operation of AI systems. Behind the wonders of face recognition or self-driving cars is the exploitative same old, same old:
Data labeling jobs are often performed far from the Silicon Valley headquarters of “AI first” multinational corporations — from Venezuela, where workers label data for the image recognition systems in self-driving vehicles, to Bulgaria, where Syrian refugees fuel facial recognition systems with selfies labeled according to race, gender, and age categories. T
hese tasks are often outsourced to precarious workers in countries like India, Kenya, the Philippines or Mexico. Workers often do not speak English but are provided instructions in English, and face termination or banning from crowdwork platforms if they do not fully understand the rules.
These corporations know that increased worker power would slow down their march toward proliferating “AI” systems requiring vast amounts of data, deployed without adequately studying and mitigating their harms. Talk of sentient machines only distracts us from holding them accountable for the exploitative labor practices that power the “AI” industry.
While researchers in ethical AI, AI for social good, or human-centered AI have mostly focused on “debiasing” data and fostering transparency and model fairness, here we argue that stopping the exploitation of labor in the AI industry should be at the heart of such initiatives.
If corporations are not allowed to exploit labor from Kenya to the U.S., for example, they will not be able to proliferate harmful technologies as quickly — their market calculations would simply dissuade them from doing so.
Thus, we advocate for funding of research and public initiatives that aim to uncover issues at the intersection of labor and AI systems. AI ethics researchers should analyze harmful AI systems as both causes and consequences of unjust labor conditions in the industry. Researchers and practitioners in AI should reflect on their use of crowdworkers to advance their own careers, while the crowdworkers remain in precarious conditions.
Instead, the AI ethics community should work on initiatives that shift power into the hands of workers. Examples include co-creating research agendas with workers based on their needs, supporting cross-geographical labor organizing efforts and ensuring that research findings are easily accessed by workers rather than confined to academic publications.
The Turkopticon platform created by Lilly Irani and M. Six Silberman, “an activist system that allows workers to publicize and evaluate their relationships with employers,” is a great example of this.
The second piece, in an entirely different register, is from the astrobiologist and theoretical physicist Sara Walker. Her essential challenge is whether we can manage to see AIs as another stage in the evolution of forms of life on this planet, rather than something anti-life (or certainly anti human). She sets out some arguments below:
..Biological modes of evolution do not apply to biospheres. We do not yet fully understand what evolution is doing at the planetary scale. We do know that the history of individual organisms within the biosphere has gone from the simple to the more complex (though not the case along every lineage).
Prokaryotes are “simple” — mostly single-celled life with no internal organelles within them. “Complex” life evolved as cells became more structured, with components inside and out, allowing multicellular life and tissues with specific functions.
Individual multicellular systems then formed societies. In human societies, we went on to evolve language. As the evolutionary biologists Eörs Szathmáry and John Maynard Smith have pointed out, each of these major evolutionary transitions has been associated with new modes of information transfer and storage. The multisocietal aggregates that have only very recently emerged on this planet are made possible through the interaction of linguistic societies.
A natural extension of this evolutionary history is to recognize how “thinking” technologies may represent the next major transition in the planetary evolution of life on Earth. It is what we might expect as societies scale up and become more complex, just as life simpler than us has done in the past.
The functional capabilities of a society have their deepest roots in ancient life, a lineage of information that propagates through physical materials. A cell might evolve along a specific lineage into a multicellular structure (something that’s not inevitable but has happened independently on Earth at least 25 times).
But in the same way, the emergence of artificial intelligences and planetary-scale data and computation can be seen as an evolutionary progression — a biosphere becoming a technosphere.
One example of planetary-scale computation is global monitoring of planetary health, if the data can be used to adaptively respond. Another example is large language models because they require the global integration of massive amounts of language data for their training.
The Gaia hypothesis was intended to conceptualize how life has established feedback loops with the planet that allow it to maintain itself over time. It did not address the hierarchy of complexity that life evolves over time — that is, the major transitions of life recurring across scales, from molecular to cellular, to multicellular to societal, to multisocietal to planetary.
If life is truly a planetary phenomenon, we should expect to see the same features recurring across time at new levels of organization as they gradually scale up to the planetary. What is emerging now on Earth is planetary-scale, multisocietal life with a new brain-like functionality capable of integrating many of the technologies we have been constructing as a species over millennia.
It is hard for us to see this because it is ahead of us in evolutionary time, not behind us, and therefore is a structure much larger in time than we are. Furthermore, it is hard to see because we are accustomed to viewing life on the scale of a human lifespan, not in terms of the trajectory of a planet.
Life on this planet is very deeply embedded in time, and we as individuals are temporary instances of bundles of informational lineages. We are deeply human (going back 3.8 billion years to get here), and this is a critically important moment in the history of our planet but it is not the pinnacle of evolution.
What our planet can generate may just be getting started. In all likelihood, we are already a few rungs down in the hierarchy of informational systems that might be considered “alive” on this planet right now.
We are 3.8-billion-year-old lineages of information structuring matter on our planet. We need to recognize our world teems with life and also that life is what we are evolving into.
It is only when we understand ourselves in this context that we have any hope of recognizing whatever life, currently unimagined and evolving along radically different lineages, might exist, or we might generate to co-evolve with us.
More here. See our growing archive of posts on artificial intelligence.