Unknown: Hello everyone and welcome to this presentation on
AI moderated decision making. In this work, we show procedures to
capture anchoring bias in sequential decision making
tasks, and how to subsequently mitigated with machine learning.
When making decisions sequentially, we often
inherently compare the current instance to be invalidated by
the decisions that we made before that. For example, this
can happen when reviewing papers in a row, grading students, or
evaluating people for jobs or college admissions. Imagine a
scenario where you evaluated and rejected three unqualified
students in a row to be admitted to a college program. Then, if
you evaluate a specific file X, you might be unconsciously
biased towards admitting that student. On the other hand, if
you've just seen three highly qualified students and admitted
them all, you might be unconsciously biased to reject
the same particular file X. This natural but flog behavior is
called the anchoring bias and has an impact on the correctness
of the decisions, as well as individual fairness, which
states that similar files should be given similar decisions
irrespectively of the order in which they were reviewed by a
human. Our work shows two ways of mitigating this kind of bias.
First, for use cases where the decisions were already made, we
find that we can flag potentially incorrect decisions
for re review, where the anchoring state of a reviewer is
captured within our probabilistic model. On the
other hand, we find that we can mitigate bias prospectively
before decisions were made. We show that we can learn when a
reviewer is biased, and sample the next instance for review and
accordingly. simply changing the order of files to be reviewed,
requires no adaptation of the test setup visible to the user.
We compare all of our methods to the conventional procedure of
evaluating instances in random order within different crowd
studies. Our work is based on two sequential decision making
datasets. First, we use decisions made for college
admissions, where reviewers evaluate student features like
GPA or recommendation letters. Over 26,000 decisions were made
by 117 unique reviewers. Second, we use the sequential evaluation
of product reviews were over 3500 decisions were made by 90
unique reviewers. For the college admissions data, the
decisions were already made. But for the product review
procedure, we had the opportunity to collect data from
different orders of review instances. First, we collected
data on reviews in random order. Second, we heuristically
alternated positive and negative reviews, depending on which
decision was made beforehand by a reviewer. Third, we use the
Learn strategy to select the next instance to be reviewed
based on a learned anchoring state of this particular
reviewer. The right hand figure shows the user interface that
was shown to evaluators doing our crowd study. When
retrospectively mitigating anchoring bias in sequential
decision making, we found that the probability of being
anchored can be modeled with an exponential distribution, where
we found that it increases up to a certain threshold. And after
this threshold, a reviewer makes bias decisions. We found that a
logical strategy can correct those potentially biased
decisions, which takes into account the biasing state of a
human reviewer, a machine learning model decision, for
example, made by a support vector machine, and the original
review that was made by the human for this retrospective
mitigation procedure, we found that the agreement to final
admission decisions made by a matter of reviewer increases for
college admissions. And we also found that sequential
dependencies to previously made decisions decrease as similar
but even stronger effect is found in product reviews, where
the agreement to the majority vote compared to the random
order evaluation increases by 5%. And sequential dependencies
to previous decisions decrease by 0.08.
When we have access to the order of the decision making process
within the life process, we show that we can mitigate anchoring
bias prospect of flee before decisions are made. We remember
that the goal is to minimize the anchoring state of an evaluator,
such that the next decision doesn't depend as much on
previous decisions. We learned this by modeling the anchoring
state with an LSTM neural network, and subsequently sample
instances in a way that reduces influence of previously made
decisions with reinforcement learning. Using this procedure,
we find that the heuristic and learn strategy, increase
agreement to majority vote and decrease the sequential
dependencies to previous decisions. We observe that learn
strategy outperforms heuristic evaluation, and increases the
agreement to the majority vote by 7%. Compared to random order
evaluation. We also see that there is a slight difference in
reinforcement learning algorithms, but the overall
agreement is the same for both reinforcement learning
procedures. In summary, our studies anchoring bias and
sequential decisions within a real world study on college
admissions, and product review decisions. We find that we can
mitigate bias post hoc after decisions were made, as well as
in situ within the decision making process. Our work strives
towards increased fairness and decision making within this
setup of sequential decision making tasks. Please check out
our paper for more details.