Lynnaire
Sheridan, University
of Otago
Meta’s recently
launched AI agent Muse promises to be many things: it
can fire off emails, book holidays and fill out cumbersome
online forms.
But it’s attracting
particular attention for its potential as a personal
shopper that can search for bargains and make purchases
without a user’s approval.
Just think of someone
hunting for a new pair of running shoes. Within minutes, an
AI assistant could scan dozens of retailers and compare
different brands to find the cheapest suitable
pair.
And Muse is far from alone. Google and OpenAI
are
also developing AI-powered shopping tools, as tech
giants compete to make these agents part of everyday
purchasing.
The appeal of these new AI agents to
consumers is obvious: less time spent shopping, and
potentially better deals. For retailers, they could change
how products are marketed and recommended
online.
Yet, there’s another important
implication getting less attention: what happens to the
workers who make those products? And will they be left even
worse off?
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The workers we can’t
see
Major global brands negotiate prices with
suppliers, which also must manage their own production
costs. Materials might be sourced in one country, components
manufactured in another and finished products assembled
elsewhere. These layers can make workers – and the
conditions they operate in – invisible.
The
consequences of buyers’ demands for workers are well
documented. Research
by the International Labour Organisation involving 31
suppliers across five countries found that buyers’ demands
put pressure on wages, increased overtime and sometimes led
factories to cut corners on workplace
safety.
Indeed, UK researchers have
demonstrated how consumer relationships and global
supply chains influence the governance of labour standards.
More recently, Australian labour policy expert David Peetz
has
explored how interconnected technologies can concentrate
economic power.
Millions of AI shopping agents
scouring the internet for the lowest prices could intensify
these pressures, particularly if algorithms increasingly
favour cheaper products. Some businesses might respond
through innovation, but others could look for savings by
cutting labour costs.
For workers vulnerable to
exploitation, the consequences could be more serious.
Worldwide, tens
of millions of people already live in modern
slavery. Forced
labour remains a major problem in global supply chains,
particularly in industries such as manufacturing and
agriculture.
Such exploitation isn’t simply the
result of unethical employers. Pressure to cut costs, weak
regulation and workers’ limited bargaining power can all
to poor working conditions.
So could consumers
simply tell their AI shopping agents to buy ethically
instead?
The difficulty is knowing which products
meet that standard. Expensive running shoes may cost
more because of branding or profit margins, rather than
better wages or working conditions. And companies’ ethical
sourcing claims may reveal little about what happens in
subcontracted factories.
An AI assistant can
readily compare prices, delivery times and customer reviews.
But it has a much harder task establishing whether workers
are paid fairly, can join unions or have safe workplaces –
information even human consumers struggle to
obtain.
There is also a larger economic
contradiction. AI shopping promises to increase our
purchasing power by helping us find better products, avoid
inflated prices and save money.
But consumers are
also workers. If cheaper goods come at the expense of wages
and job security, some of the people enjoying those bargains
may ultimately have less money to spend. What they gain at
the checkout could be offset by what they lose at
work.
Could AI make ethical shopping
easier?
It’s also possible AI shopping agents
could help consumers make more informed choices – but only
if they have access to reliable information about how
products are made.
Some of that information is
already being collected. Open Supply
Hub, for example, maintains a public database mapping
more than 2.5 million production locations worldwide,
helping identify factories and the companies connected to
them.
Imagine an AI shopping assistant using such
information, alongside independently verified labour
standards, to find running shoes made under fair working
conditions rather than simply recommending the cheapest
pair.
But knowing where a product was made is not
the same as knowing how workers were treated. That requires
independent scrutiny, information from workers themselves
and effective protections for their rights.
The
OECD already has
guidelines for responsible sourcing in the clothing and
footwear industries, while the International Labour
Organisation’s Better
Work programme has demonstrated that efforts to improve
factory conditions can produce results.
AI could
make this information easier for consumers to find and
compare. But it can’t replace the regulation, collective
bargaining and accountability needed to improve working
conditions in the first place.
Tracing a
product’s whakapapa
– its origins and connections – means recognising the
people and employment relationships behind an everyday
purchase.
Whether AI makes those connections more
visible, or obscures them further, will depend partly on the
information available and what shopping agents are designed
to prioritise.
Lynnaire
Sheridan, Associate Professor, Department of Management,
University
of Otago
This article is
republished from The
Conversation under a Creative Commons license. Read the
original
article.