Matt Shearer

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We've just been at City Hall for London's Civic Innovation Challenge Awards
Ceremony. Sadly we didn't win on this occasion, but we were proud to be in the
finalists for the Counter Violent Extremism Social Tech Challenge.

Explainable AI Social Tech to help Londoners
Our pitch included using our SCOPA platform to provide London's analysts with
Explainable AI (XAI) decision support.

Explainable AI decision support - in this case for violent extremism specialists
Thanks to the GLA Team and Social Tech Trust!
The finalists with the GLA team and Social Tech Trust.

Explainable AI (XAI) means that the process, reasoning and decisions made by the AI are transparent and clear to the users and teams involved. Even before this became a trending technical jargon term, we were delivering Explainable AI products and services.

Read about News, Product Innovation and Services for "Explainable AI" here. We've been delivering explainable AI (XAI) for quite some time now.

Explainable AI - News, Product Innovation and Work

Explainable AI

Proud Finalists in London's 2020 Civic Innovation Challenge

Fabio Colasanti

Earlier this week, this tweet
[https://mobile.twitter.com/random_walker/status/1196870349574623232] appeared
on my feed.

> Much of what’s being sold as "AI" today is snake oil. It does not and cannot
work. In a talk at MIT yesterday, I described why this happening, how we can
recognize flawed AI claims, and push back. Here are my annotated slides: 
https://t.co/iCpyFw5urN pic.twitter.com/pmOTI3vq8p [https://t.co/pmOTI3vq8p]

— Arvind Narayanan (@random_walker) November 19, 2019
[https://twitter.com/random_walker/status/1196870349574623232?ref_src=twsrc%5Etfw]

Prof Narayanan's deck
[https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf] has
reached a relatively sizeable audience and received widespread positive
commentary. After all, anyone who has had any involvement with AI in the last
few years can 100% relate with the catchy title of his deck.
Hey - we're just about starting to see a feeble light at the end of the
blockchain tunnel, right?

But, but...
Something doesn't quite sit well with me. So I'm going to stick my neck out and
respectfully reject, if not the conclusions, the way Prof Narayanan has chosen
to make his point. Or rather, the way he has chosen to make his real point.

I find this deck deceptively coy, as the actual message is not "How to recognize
AI snake oil" but rather "Why I don't want AI anywhere near socially sensitive
topics", which would have been a perfectly valid argument, and made for a
fantastic set of slides, but required a slightly different set of arguments and,
most importantly, to be explicit about it.

At the end of the day ML/AI is about identifying patterns in data and draw
inferences, so of course it can "predict the future". Google Maps does it all
the times when it gives you an ETA for your journeys. In Prof Narayanan's own
examples, bail decisions and border control are not performed by machines, but
based on the prediction of machines. It's not about predicting the future: it's
called decision support, performed at speed that is beyond human capabilities.
Is the way banks and insurance companies make decision on mortgages and premiums
snake oil too? 
Prof Narayanan seems exceedingly preoccupied about "massive transfer of power
from [...] workers to unaccountable tech companies" and points at "enormous
commercial interests" that "we must resist" and "push back". He is of course
completely entitled to give the whole argument a political spin, but I'm not
sure this helps his arguments.

The real issue here is that some problems, and especially socially impactful
ones, carry high personal risks if an inference is wrong (problem 1). Therefore,
in order to minimise that, large amounts of potentially sensitive data may be
required (problem 2) and in most, if not all, cases there is a strong argument
for transparency, accountability and human-in-the-loop that makes such
propositions much more expensive and challenging from both a technical (problem
3) and governance (problem 4) perspective. 
Bias poses no doubt massive challenges (problem 5), not just in its
identification and removal, but also in its very definition, as what turns
common sense into bias is a function of our set of social and cultural values,
which greatly varies in time and space. Should history be changed because it
doesn't fit our Western 21st century view of the world? I don't have an answer,
but at least I'm asking the question.
Also, there is the topic of regulation which can't be ignored in these cases:
who establishes the boundaries? Who is it meant to enforce them? Do we need a
universal legislation, or would locally applicable frameworks suffice? Who makes
a call on what is an acceptable risk vs potentially beneficial outcomes -
supranational bodies, governments, individuals, etc.. ? (problem 6)

So the better discussion to have is around the risks involved in deploying AI in
socially sensitive fields, what is the minimum level of benefits it must bring
about to make it worth us facing the challenges above, and how do we go about
having that conversation.

Just branding the whole thing as snake oil is sidestepping the issue and largely
unfair, because it's bypassing the actual conversations to be had and going
straight for the outcome that Prof Narayanan - in arguably a biased opinion -
has decided is what's best for us all.

AI is a core discipline at Data Language. We have an expert team skilled in machine learning and deep learning, and importantly we also have the engineering and deployment skills to deliver AI at scale for your production systems.

AI is a core discipline at Data Language: Our expert team is skilled in machine learning and the engineering skills needed to deliver AI at scale.

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