41 research outputs found
Dialog Structure Through the Lens of Gender, Gender Environment, and Power
Understanding how the social context of an interaction affects our dialog behavior is of great interest to social scientists who study human behavior, as well as to computer scientists who build automatic methods to infer those social contexts. In this paper, we study the interaction of power, gender, and dialog behavior in organizational interactions. In order to perform this study, we first construct the Gender Identified Enron Corpus of emails, in which we semi-automatically assign the gender of around 23,000 individuals who authored around 97,000 email messages in the Enron corpus. This corpus, which is made freely available, is orders of magnitude larger than previously existing gender identified corpora in the email domain. Next, we use this corpus to perform a largescale data-oriented study of the interplay of gender and manifestations of power. We argue that, in addition to one’s own gender, the “gender environment” of an interaction, i.e., the gender makeup of one’s interlocutors, also affects the way power is manifested in dialog. We focus especially on manifestations of power in the dialog structure — both, in a shallow sense that disregards the textual content of messages (e.g., how often do the participants contribute, how often do they get replies etc.), as well as the structure that is expressed within the textual content (e.g., who issues requests and how are they made, whose requests get responses etc.). We find that both gender and gender environment affect the ways power is manifested in dialog, resulting in patterns that reveal the underlying factors. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions
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Social Power in Interactions: Computational Analysis and Detection of Power Relations
In this thesis, I investigate whether social power relations are manifested in the language and structure of social interactions, and if so, in what ways, and whether we can use the insights gained from this study to build computational systems that can automatically identify these power relations by analyzing social interactions. To further understand these manifestations, I extend this study in two ways. First, I investigate whether a person’s gender and the gender makeup of an interaction (e.g., are most participants female?) affect the manifestations of his/her power (or lack of it) and whether it can help improve the predictive performance of an automatic power prediction system. Second, I investigate whether different types of power manifest differently in interactions, and whether they exhibit different but predictable patterns. I perform this study on interactions from two different genres: organizational emails, which contain task oriented written interactions, and political debates, which contain discursive spoken interactions
Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates
Perception of offensiveness is inherently subjective, shaped by the lived
experiences and socio-cultural values of the perceivers. Recent years have seen
substantial efforts to build AI-based tools that can detect offensive language
at scale, as a means to moderate social media platforms, and to ensure safety
of conversational AI technologies such as ChatGPT and Bard. However, existing
approaches treat this task as a technical endeavor, built on top of data
annotated for offensiveness by a global crowd workforce without any attention
to the crowd workers' provenance or the values their perceptions reflect. We
argue that cultural and psychological factors play a vital role in the
cognitive processing of offensiveness, which is critical to consider in this
context. We re-frame the task of determining offensiveness as essentially a
matter of moral judgment -- deciding the boundaries of ethically wrong vs.
right language within an implied set of socio-cultural norms. Through a
large-scale cross-cultural study based on 4309 participants from 21 countries
across 8 cultural regions, we demonstrate substantial cross-cultural
differences in perceptions of offensiveness. More importantly, we find that
individual moral values play a crucial role in shaping these variations: moral
concerns about Care and Purity are significant mediating factors driving
cross-cultural differences. These insights are of crucial importance as we
build AI models for the pluralistic world, where the values they espouse should
aim to respect and account for moral values in diverse geo-cultural contexts
Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation
Human annotations play a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and
decisions that go into building ML datasets has not received nearly enough
attention. In this paper, we survey an array of literature that provides
insights into ethical considerations around crowdsourced dataset annotation. We
synthesize these insights, and lay out the challenges in this space along two
layers: (1) who the annotator is, and how the annotators' lived experiences can
impact their annotations, and (2) the relationship between the annotators and
the crowdsourcing platforms and what that relationship affords them. Finally,
we put forth a concrete set of recommendations and considerations for dataset
developers at various stages of the ML data pipeline: task formulation,
selection of annotators, platform and infrastructure choices, dataset analysis
and evaluation, and dataset documentation and release
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
While generative multilingual models are rapidly being deployed, their safety
and fairness evaluations are largely limited to resources collected in English.
This is especially problematic for evaluations targeting inherently
socio-cultural phenomena such as stereotyping, where it is important to build
multi-lingual resources that reflect the stereotypes prevalent in respective
language communities. However, gathering these resources, at scale, in varied
languages and regions pose a significant challenge as it requires broad
socio-cultural knowledge and can also be prohibitively expensive. To overcome
this critical gap, we employ a recently introduced approach that couples LLM
generations for scale with culturally situated validations for reliability, and
build SeeGULL Multilingual, a global-scale multilingual dataset of social
stereotypes, containing over 25K stereotypes, spanning 20 languages, with human
annotations across 23 regions, and demonstrate its utility in identifying gaps
in model evaluations. Content warning: Stereotypes shared in this paper can be
offensive