[This is a guest post by Talia's girlfriend Annie, who is maintaining
this blog while Talia is away at Middlbury Language Academy]
In
my last post on here I mentioned that my
job involves writing programs that identify
important information from
text documents. When testing
these programs, there are two main ways of evaluating the accuracy of
their output: precision and recall. Put simply, precision
refers to what fraction of the retrieved information is relevant to
what we’re looking for, while recall
refers to what fraction of the relevant information in the original
text is retrieved by the program.
Ideally, one would want a program to have both high precision and
high recall – that is, for it to return most or all of the
information the user is looking for and little or no irrelevant
information – but this isn’t always possible. More realistically,
you’ll often face a tradeoff between precision and recall. You can
optimize for precision, and make sure that everything in your output
is the sort of thing the user is looking for, but then you run the
risk of overlooking other information that might be slightly less
obviously relevant, but still within the scope of the user’s query.
You
can optimize for recall, and make sure to return every piece of
information that could possibly be relevant, but then there’s the
chance you’ll also turn up a lot of junk data along with the useful
stuff. Or you can try to strike a balance between precision and
recall, getting each one high enough to be useful without sacrificing
the other.
Which
of these evaluation metrics is most important depends on what task
a program is intended
to accomplish, and what the end
user’s
particular needs are. For instance, suppose you’re writing a
program to automatically filter out offensive content on social
media. (Some of my classmates and I wrote a program like this for a
class project once, although it didn’t end up working very well.) If
a social media company is going to be using your program as the first
line of moderation for everything that gets posted on their platform,
you likely don’t want it to become a heavy-handed censor. In
this case you’ll probably want to err on the side of optimizing for
precision, filtering out only the things that are well and truly
beyond the pale and letting human moderators make the call on the
edge cases.
On
the other hand,
if
this
program is intended as an optional
add-on
that
users of a
social media platform can choose to enable, you’ll probably want to
err on the side of optimizing for recall. The
people who are likeliest to use such an add-on tend to have a very
strong need to filter out certain content – say, people with
anxiety or trauma who are trying to avoid seeing content that is
triggering for them – so you want to make sure all of this content
gets filtered, even if that means blocking some harmless stuff as
well. High
precision isn’t inherently better than high recall, or vice versa;
it all depends on the specific goal you’re trying to achieve.
I’m
now going to completely switch gears
for a minute and move from this relatively dry, technical subject to
one with much more emotional heft: inclusivity in the LGBTQ+
community. (I’m focusing on this community because I’m a part of
it, but I doubt it’s the only community this discussion will be
relevant for.) If you’ve spent any time discussing LGBTQ+ politics
– especially on the internet, where political discourse is a
full-contact sport – you’re no doubt aware of the frequent heated
debates about where exactly to draw the boundaries of this community.
For
instance, I
once saw a post on
Facebook
proposing the acronym SAGA
(Sexuality And Gender Acceptance) as an alternative term to LGBTQ+
that could include everyone without making the acronym cumbersomely
long, and one of the comments was arguing against this idea, pointing
out that such imprecise wording could allow kinky straight people to
elbow their way into the community. Or on a more serious note,
back in June when Pride was going on, I saw rather a lot of posts on
Tumblr arguing
about whether asexuals should be included
in Pride,
with several people arguing that asexuals
hadn’t experienced the same oppression that gay,
lesbian, bi, and trans
people had, and
so Pride wasn’t for them.
When
I come across arguments like this, all I can think is: this
is clearly
a situation in which recall matters a whole lot more than precision.
Remember
how I said that whether it’s better to optimize for recall or
precision depends on your
particular goal?
Well, what is the goal of building an LGBTQ+ community? There
are obviously many goals, but as far as I’m concerned, the primary goal
is
to provide a space for people who are marginalized by the
heteronormativity and cissexism of mainstream culture,
where
they can be safe and free to live their authentic lives, and where
they find the support and solidarity they need in order to overcome
the obstacles that the rest of society has placed in their way.
Personally, I care much more about making sure that everyone who
needs such a space has access to it than I do about keeping out
people who don’t meet some set of entry requirements. Sure,
there might be a few straight kinksters who will see inclusive
language around sexuality and assume it’s talking about them,
but there
are also
countless queer
kids who are just figuring out their sexuality or gender and aren’t
sure what label – if any – to claim. Don’t we want them to know
they have a place in our community, even if they don’t have the
precise words for what they are yet?
For every asexual
person who doesn’t face much discrimination for their orientation,
there’s another one who’s facing ostracism from their family for
rejecting their “sacred duty” of getting married and having
children. Don’t they need our love and support too? By focusing on
precision instead of recall – that is, focusing on who we need to
exclude instead of who we need to include – we run the risk of
pushing away some of the people who our community could do the most
good for.
In
other words,
when the machine
revolution
comes and our new robot overlords implement Fully Automated Luxury Gay Space Communism, I know what sort of information
retrieval algorithms
I want them to be running.
