Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting Emergent Intersectional Biases: Contextualized Word Embeddings
  Contain a Distribution of Human-like Biases   With the starting point that implicit human biases are reflected in the
statistical regularities of language, it is possible to measure biases in
English static word embeddings. State-of-the-art neural language models
generate dynamic word embeddings dependent on the context in which the word
appears. Current methods measure pre-defined social and intersectional biases
that appear in particular contexts defined by sentence templates. Dispensing
with templates, we introduce the Contextualized Embedding Association Test
(CEAT), that can summarize the magnitude of overall bias in neural language
models by incorporating a random-effects model. Experiments on social and
intersectional biases show that CEAT finds evidence of all tested biases and
provides comprehensive information on the variance of effect magnitudes of the
same bias in different contexts. All the models trained on English corpora that
we study contain biased representations.
  Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and
Emergent Intersectional Bias Detection (EIBD), to automatically identify the
intersectional biases and emergent intersectional biases from static word
embeddings in addition to measuring them in contextualized word embeddings. We
present the first algorithmic bias detection findings on how intersectional
group members are strongly associated with unique emergent biases that do not
overlap with the biases of their constituent minority identities. IBD and EIBD
achieve high accuracy when detecting the intersectional and emergent biases of
African American females and Mexican American females. Our results indicate
that biases at the intersection of race and gender associated with members of
multiple minority groups, such as African American females and Mexican American
females, have the highest magnitude across all neural language models.

3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg