https://www.economist.com/culture/2022/11/30/two-new-books-explore-the-upside-of-big-data-and-ai

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Culture | The algorithm's mercy

Two new books explore the upside of big data and AI

They are a refreshing counterbalance to alarmist commentary

[20221203_C]
Nov 30th 2022
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The Equality Machine. By Orly Lobel. PublicAffairs; 368 pages; $30
and PS25

Escape from Model Land. By Erica Thompson. Basic Books; 256 pages; 
$30 and PS20

TWO YEARS ago, when Elinor Lobel was 16, a "smart" insulin pump was
attached to her body. Powered by artificial intelligence (AI), it
tracks her glucose levels and administers the right dose of insulin
at the right time to keep her healthy. It is a miraculous innovation
for diabetes sufferers and just one of myriad new ways that data and 
ai can help improve lives.

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Books that decry the dark side of data abound. With menacing titles
such as "Weapons of Math Destruction" and "Algorithms of Oppression",
they suggest that there is much more to fear than fete in the
algorithmic age. The public is duly alarmed; ditto policymakers. For
instance, a proposed European Union directive may hold back some
educational applications of ai, such as its use in marking exams.

But the intellectual tide may be turning. One of the most persuasive
proponents of a more balanced view is Elinor Lobel's mother, Orly, a
law professor at the University of San Diego. In "The Equality
Machine" she acknowledges AI's capacity to produce skewed and harmful
results. But she shows how, in the right hands, it can also be used
to combat inequality and discrimination. "We need to cut through the
utopian/dystopian dualism," she writes. "The goal should be progress,
not perfection."

For example, women selling goods on eBay tend to receive less money
than men for the same item. Apprised of that bias, the website can
hide vendors' personal details until an offer is made, or alert them
to higher prices in similar transactions. Meanwhile women looking for
jobs are less likely than men to respond to postings that use
military jargon such as "mission critical" and "hero". Textio, an AI
firm, helps companies recruit female employees by scanning listings
and recommending alternative language.

"The Equality Machine" buzzes with such examples, revealing a hidden
world of coders, data scientists and activists who are working on the
technical means to achieve ethical ends, not simply griping about ai
's lapses. The book aptly describes the workings of various AI
systems, but its main contribution is to reframe problems in
constructive ways.

A tenet of privacy rules is "minimisation": collect and retain as
little information as possible, especially in areas such as race,
gender and sexual orientation. Ms Lobel flips the script, showing how
in countless cases of medical diagnosis and treatment, as well as in
hiring, pay and the legal system, knowing such characteristics can
lead to fairer outcomes. For example, in the past American regulators
did not track the performance of medical devices by the sex of
patients, though an independent study suggested women experience
twice as many deaths and injuries as men.

Ms Lobel's call to use more, not less, personal information
challenges data-privacy orthodoxy. But she insists that "tracking
differences is key to detecting disparities." She advocates a careful
loosening of intellectual-property rules to provide more transparency
over algorithmic decisions. And she floats the idea of a sort of
affirmative action in ai to support disadvantaged groups. For
instance, an algorithm that serves adverts for highly paid jobs to
men--because they mostly clinched such posts in the past--can be
programmed to show them equally to women.

As Ms Lobel says, AI need not merely reproduce or entrench old biases
. It can help expose them. And it is easier to fix an algorithm than
it is to change people's minds.

The problems with algorithmic formulae are tackled in depth in
"Escape from Model Land" by Erica Thompson of the London School of
Economics. These statistical models are the backbone of big data and 
AI: if data is the input, algorithms are the tool and models are the
product. They are everywhere, from e-commerce tips to economic and
climate-change forecasts.

Yet rather like the full-scale map of an empire imagined by the
writer Jorge Luis Borges, a perfect model of the teeming world will
always be beyond reach. The task is to ensure that the abstractions
correspond to reality as far as is humanly possible. "All models are
wrong," runs a venerable saying. "Some are useful."

Model students

Ms Thompson focuses on a challenge she calls the Hawkmoth Effect. In
the better known Butterfly Effect, a serviceable model becomes less
reliable over time because of the complexity of what it is
simulating, or because of inaccuracies in the original data. In the
case of climate change, say, this might lead to a prediction for
rising temperatures being out by a fraction of a degree. In the
Hawkmoth Effect, by contrast, the model itself is flawed; it might
fail to take full account of the interplay between humidity, wind and
temperature. This sort of mistake can be much more misleading, and
much harder to rectify.

The author calls on data geeks to improve their solutions to
real-world issues, not merely refine their formulae--in other words,
to escape from model land. "We do not need to have the best possible
answer," she writes, "only a reasonable one." Before there is a
statistical model, she notes, there is a mental version. Data
scientists need self-awareness and empathy as well as mathematical
skill.

Both these books exhibit a healthy realism about data, algorithms and
their limitations. Both recognise that making progress involves
accepting constraints, whether in law or coding. Ms Lobel calls on AI
practitioners to remedy the technology's problems; Ms Thompson asks
data scientists to be conscious of the choices and values in a
model's design. Their reflections offer the basis for a constructive
agenda. As Ms Lobel puts it: "It's always better to light a candle
than to curse the darkness." #

For more on the latest books, films, TV shows, albums and
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newsletter

This article appeared in the Culture section of the print edition
under the headline "The algorithm's mercy"

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