IEEE Technology and Society Magazine - September 2023 - 10

Editorial
advertising space that makes a shark feeding frenzy
look restrained, it is not completely unlikely for a
similar electronic auction to result in each generated
response of a chatbot to be preceded by something
like " I have brought this answer to you courtesy of
... " the auction winner (and note the inevitable and
misleading anthropomorphization with the first person
pronoun, rather than the indirect but typical " this
message has been brought to you by-, official partners
of-. " It could be argued that since both advertising
and generative AI are pushing hallucinations,
it is a perfect match of opportunity and cynicism).
In the second case, subscription to tiered services,
there is a distinct possibility of exacerbating
inequality, initially caused by the digital divide. For
example, there is an increasing demand for mental
health services, especially among the young. Given
the shortage of both qualified counselors and funding
for treatment, and the cynical and woefully
inaccurate snark that counseling involves a series of
canned responses ( " That's terrible. How does it make
you feel? " ), unsurprisingly it has been suggested that
chatbots should replace counselors. Notwithstanding
the dangers to patients from misdiagnosis and
mistreatment [11], there are two ways this ends:
either the chatbot is better than the human, in which
case the chatbot will be made available to those
who can afford it (with the residual hope that a medical
advance initially only accessible to the wealthy
would become cheaper and more accessible over
time); or the chatbot is worse than the human, in
which case the chatbot will be made available to
those who cannot afford the human. Either way, a
two-tier mental health service would increase inequality
and would not meet the appropriate UN Sustainable
Development Goal 3 (ensure healthy lives
and promote well-being for all ages).
It might be worth noting that OpenAI, the makers
of ChatGPT-2, -3, -3.5, and -4, already charge a
subscription service to access the latest version.
But, under the virtuous cloak of competition, it is
also disconcertingly opaque about what went into
ChatGPT-4: " this report contains no further details
about the architecture (including model size),
hardware, training compute, dataset construction,
training method, or similar " [7, p. 2]. But effectively
beta-testing the earlier versions for free and not
knowing what happened to all the user data is somehow
redolent of the adage: " if you are not paying for
the product, then you are the product. " Although
10
this seems secondary to the question of who is doing
the time-consuming and laborious job of annotation
for whom and for how much, there is more than a
hint of colonial exploitation here [12].
Although it depends on a large number of factors,
such as the size of the training data, the number
of parameters, the number of iterations learning
input-output pairs, and the actual hardware used
to run the software, training LLM demands substantial
energy consumption and significant computing
power, but also-often overlooked-a considerable
amount of freshwater (e.g., for cooling). This implies
a heavy investment in infrastructure, high operating
costs, and substantial environmental impact. The
cost of producing any single query is relatively low,
although with a user base (already) in the hundreds
of millions, this translates to sizable server farms and
warehouse-sized computers. However, with revenue
predictions in the region of billions of dollars,
OpenAI can probably afford it, or to pay Microsoft's
Cloud Computing fees.3 The question is whether the
planet can afford it (and as a follow-up, as might
equally be asked of the financial services industry,
what tangible benefits are the developers of LLM
bringing other than to themselves?).
These expenses probably do not put training and
using an LLM in quite the same cost category as a
cryptocurrency in terms of energy consumption, but
the same question could be asked of both technologies:
is it really worth it? During a cost-of-living crisis,
which may be transient, and the ongoing climate
crisis, which is anything but, and a looming global
water crisis due to overconsumption, are the benefits
to society of this technology worth the direct and
indirect social environmental costs? The question
has been asked [13], but it is hard to have a " serious "
conversation when entrenched privilege is in denial
about the climate crisis and unmoved by evidence
of its impact on others, and indifferent to the suffering
of those less financially fortunate.
The issue of generative AI and intellectual property
has been considered from the perspective of
futural appropriation [14], but the quotation from
Childres [15], the creator of the extremely popular
board game Gloomhaven, is worth repeating: " AI
art very much feels like theft to me. You're training
these AI with specific artists' influences and then just
not crediting them at all. " There are murmurings of
3See https://www.tooltester.com/en/blog/chatgpt-statistics/ for some statistics on
OpenAI's ChatGPT-X.
IEEE Technology and Society Magazine
https://www.tooltester.com/en/blog/chatgpt-statistics/
IEEE Technology and Society Magazine - September 2023

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - September 2023
