  Cognitive biases are mental shortcuts humans use in dealing with information
and the environment, and which result in biased actions and behaviors (or,
actions), unbeknownst to themselves. Biases take many forms, with cognitive
biases occupying a central role that inflicts fairness, accountability,
transparency, ethics, law, medicine, and discrimination. Detection of biases is
considered a necessary step toward their mitigation. Herein, we focus on two
cognitive biases - anchoring and recency. The recognition of cognitive bias in
computer science is largely in the domain of information retrieval, and bias is
identified at an aggregate level with the help of annotated data. Proposing a
different direction for bias detection, we offer a principled approach along
with Machine Learning to detect these two cognitive biases from Web logs of
users' actions. Our individual user level detection makes it truly
personalized, and does not rely on annotated data. Instead, we start with two
basic principles established in cognitive psychology, use modified training of
an attention network, and interpret attention weights in a novel way according
to those principles, to infer and distinguish between these two biases. The
personalized approach allows detection for specific users who are susceptible
to these biases when performing their tasks, and can help build awareness among
them so as to undertake bias mitigation.
