G’day.
This is a short presentation of our paper entitled
“Method for Appropriating the Brief Implicit Association Test to Elicit Biases in Users”
published at CHI’22.
My name is Tilman Dingler and I am presenting this work on behalf of my co-authors
Benjamin Tag David Eccles
Niels van Berkel and Vassilis Kostakos.
Media companies and their practices have recently come under scrutiny for the creation of echo
chambers, the fallout from micro-targeting, and practices to keep users’ attention at
all costs.
For years, the study of context-aware systems has been central to the HCI community.
But for systems to adapt to the user’s context, they also need to know about cognitive processes,
such as what we pay attention to and why, how we filter information in the vast information
space, and what makes us receptive to certain information.
This is where cognitive biases come into play: Cognitive biases are a set of heuristics learned
over time that serve as shortcuts to effectively navigate dense or complex information spaces.
These biases play an important role in how information is perceived and processed.
Systems influence that perception through their content, their Interface and, system
behaviour.
In our work, we study these biases and how computing systems exacerbate or mitigate them.
.. BUT .. we need some sort of method to collect ground truth on biases, implicit assumptions,
or hidden tendencies.
Now, we could simply ask people how they feel about conservative politics or how whether
they would support affirmative actions in University hiring processes.
But the challenge with biases is that they are often implicit, which means, people may
not be aware of them or they are aware of them but they just wouldn’t want to tell
us.
So another approach that was proposed by colleagues in psychology, namely Anthony Greenwald and
his team in 1998 is the Implicit Association Test or IAT.
The original IAT measures individual differences of the association of an attribute, for example,
a stereotypical name, or a face with a concept, for example gender or race.
This is achieved by tasking users with quickly responding to a matching task of a concept
and an attribute.
The pressure to respond as fast as possible is meant to induce heuristic processing in
users rather than in-depth contemplation.
The idea is that given certain preferences, tendencies, or plain biases, some concepts
are easier to map to certain attributes than others.
And so over the last decades, this test has been used to elicit implicit tendencies with
regard to topics, such as race, gender, and self-esteem, and the word
sets have been validated over many years.
But a lot of today’s issues are more specific and the conversation around them evolves rather
quickly in the public discourse.
So we need a method to create tests on contemporary topics as they emerge, such as pro-life / pro-choice
or gun control, for example.
But also for things like Apple vs Android or dark mode vs light mode.
So we created a method that would allow us to come up with wordsets for custom topics.
But we had to avoid our own bias in creating such word sets and at the same time make sure
that our target group had strong associations with each word in those sets.
The solution we thought of was involving the crowd in a multi-step validation process.
So eventually we ended up with a two-step process:
1) An initial sourcing of words for the set using multiple researchers and a Thesaurus,
2) using Amazon MTurk to invite people to undergo association tests using the new word
set and reporting words that have weak, no, or misleading associations, and make suggestions
for replacements.
We repeated this second step until a high agreement on all words had been reached.
To do this, we built a web-tool that administers the BRIEF version of the Implicit Association
Test also called BIAT.
This is basically a refined tool developed by Sriram and also Greenwald in 2009, which
effectively shortens the IAT.
Users of the BIAT are required to remember the objects within two categories.
In this example, we have Man-made Climate Change vs. Natural Climate Cycles.
In this example, man-made climate change is the so-called “focal” paired with the
GOOD attribute.
So whenever users see a concept that belongs to man-made climate change or is a GOOD attribute
they should press one key and for all others concepts and BAD attributes that are shown,
they should press another.
The first block is a shortened training run.
The second block pairs the concept ‘man-made climate change’ with good attributes
Then for the third block, the pairing is switched and users need to re-wire their responses
to try and associate ‘natural climate cycles’ with good attributes on the focal side and
all other on the non-focal side.
From here on, the pairing alternates until block 5.
After all 5 blocks have been administered, the software uses the reaction times to calculate
the so-called D-Score: When comparing the test block results, the
numerical range of D reaches from -2: a strong preference for concept A
to +2: a strong preference for concept B
We then also gave participants the chance to report their impression on how well this
score fits their actual tendencies, i.e., a subjective assessment.
We did this with three cohorts of MTurk workers who underwent the BIAT for four different
topics.
Each cohort we asked for feedback on weak, no, or misleading associations, and make suggestions
for word replacements.
In the first round we used a majority vote to substitute ill-fitting concepts and for
the following round we used Vatavu and Wobrock’s agreement score calculation to determine candidates
for substitution, namely those, where participants were in low to medium agreement.
We were planning on repeating this procedure until a high to very high agreement score
had been reached on all words.
In this case, however, high agreement on all words was reached in round 3.
We then conducted a final evaluation round with another 50 MTurk workers.
At the top we can see how participants reported how well the BIAT’s D-Score fit their own
tendencies.
And below we can see the correlations between the D-Score on the Y-Axis and the Self-assessment
on the X-Axis.
For political leaning we found a strong, for feminism a moderate, and for climate change
a weak correlation between BIAT and self-assessment.
Finally, we assessed how well our crowdsourced word sets compared to word sets produced by
domain experts, that is accomplished scholars in the field of politics, climate change,
and multiculturalism.
Here we can see the resulting word sets which we then discussed with our domain experts.
The expert sets were considered more specific in their meaning and associations,
while the crowdsourced sets seemed more general with more frequent overlaps, often quote:
“inspired by conversations on social media”.
The crowdsourced sets also reflected more stereotypes and seemed to resemble the geographical
and temporal dependence of the topic’s discourse.
So, in order to study the effect of user bias on system usage and vice versa, we present
a web-tool for ground truth collection on users’ implicit tendencies.
This tool is seeded with word sets that are created through our 2-step method.
We have shown that the wordsets created with this method are target-group specific in terms
of geography and contemporary discourse language.
And so we release our BIAT tool open source for the community to conduct research on cognitive
biases and their role in computing systems.
For more details, have a look at our paper and feel free to reach out.