By Professor Marion
Oswald,
Professor Gitanjali N Gill, Professor Michael Stockdale
We in the UK already know a lot about how technology should
be regulated and controlled, and researchers have been considering the
implications of AI for decades. But the UK AI Safety Summit argues that the
risks are Are we in danger of abandoning the
knowledge of previous expeditions in mounting this latest attempt on the
summit? Half way up?
The
history of the law shows us that if you don't clearly limit, licence or stop
something happening, people will do it. We might think about factory machines
without guardrails, driving while using a mobile phone, firearms licensing
requirements, , and how the where not banned. Of course, people may choose to do things
even if unlawful or controlled, but fewer will - law and regulation sends a
clear message about acceptable behaviour. And where things are not regulated,
bad things can happen, as we saw from the
Humans
are creatures of habit, so it’s not unreasonable to think that the same will
happen with AI. People will develop and use AI where they think it will benefit
them, and damn the consequences (because initially there don't seem to be many,
or even any). Sometimes this won't matter, even if the tech is of dubious
validity. We might not mind too much if our Alexa gets a few queries wrong. (Though
we would mind if it shared our personal data illegitimately).
But
there are real risks in some spheres - existing now - that could impact
people's lives and liberty. Imagine if a large language model incorporated into
an auditing tool, but hidden from view, generates erroneous conclusions about
illegal behaviour, and people act on that - we could easily have another .
Much
of the promotional talk about ‘foundation’, 'frontier' or 'general' AI implies
that these tools have some sort of omniscience in terms of capabilities and
purposes, but this is just not true. These models can be built into a
variety of tools, that is true and this is not new, but fundamentally, they are trained to analyse how narrative, image, audio and other data are constructed. They are good at building sentences and detecting
patterns, but their output could be utter hogwash (or
insulting, libellous, dangerous or otherwise inappropriate). If we do not take control, we even risk poisoning the
veracity and reliability of digital and online information and therefore our
historical records.If we wanted to, we could do
something about the hogwash-risk by developing a GPT-like capability that’s
only trained on pre-selected trustworthy material, but maybe this isn’t
boundary-pushing enough for some.
Furthermore,
these models do not create their own role in our world! Humans ultimately control how these models are
used, who by and for what purposes. And that does not only mean by some
theoretical 'bad actor' but by our own governments, public sector bodies and
commercial companies. It's easy, and lazy, just to talk about a vague
future existential threat. But it attracts headlines on slow news days. If
we want to take control, this inevitably means considering the aims, actions
and policies of the organisations wanting to make use of AI. And that can raise
some uncomfortable and potentially contentious issues. Is it in the 'public
good' for example to use ‘predictive’ AI to single out people for further
investigation for suspected benefit fraud? What is missing from the AI system
that we would need to know in order to make the decision fair? And who should
be the arbiter of whether AI is being used for public good? Government
ministers, parliament, multi-national corporations or a wider public? Which
government, parliament, multi-national corporation or public? The UK’s, the
EU’s, China’s?
The
fundamental question for deployment of any AI should be whether it is effective
in helping the human decision-maker uphold our longstanding principles of fair
process, transparency, justice and proportionality. We can use our existing
knowledge to answer this question, but we must involve breadth of experience
across disciplines so that the identification of public good is done in a
legitimate and collective way. There must be a genuine acknowledgement that
certain actions informed by AI might well have serious detrimental
consequences, and these should be prevented. Acceptance that AI is with us to
stay and that it is likely to have huge benefits in appropriate contexts does
not mean that controls should not be put in place. Bearing this in mind, the
recent bonfire of the advisory boards , in favour
of a tech-entrepreneur centric assembly with some commercial irons in the fire,
would appear to be a retrograde step.
