Vinodkumar Prabhakaran
Vinodkumar Prabhakaran is a Research Scientist at Google, working on issues around Ethical AI and ML Fairness. Prior to this, he was a postdoctoral researcher in the Computer Science department at Stanford University, where he worked with Prof. Dan Jurafsky and others at the Stanford NLP group, in an array of projects with a focus on applying Artificial Intelligence for Social Good. He obtained his PhD in computer science from Columbia University in 2015. His research brings together natural language processing techniques, machine learning algorithms, and social science methods to build scalable ways to identify and address large-scale societal issues such as racial disparities in policing, workplace incivility, gender bias and stereotypes, and abusive behavior online.
Authored Publications
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While large, generative, multilingual models are rapidly being developed and deployed, their safety and fairness evaluations primarily hinge on resources collected in the English language and some limited translations. This has been demonstrated to be insufficient, and severely lacking in nuances of unsafe language and stereotypes prevalent in different languages and the geographical pockets they are prevalent in. Gathering these resources, at scale, in varied languages and regions also poses a challenge as it requires expansive sociolinguistic knowledge and can also be prohibitively expensive. We utilize an established methodology of coupling LLM generations with distributed annotations to overcome these gaps and create the resource SeeGULL Multilingual, spanning 20 languages across 23 regions.
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ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
Akshita Jha
Sarah Laszlo
Rida Qadri
Chandan Reddy
ACL (2024)
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Recent studies have highlighted the issue of varying degrees of stereotypical depictions for different identity group. However, these existing approaches have several key limitations, including a noticeable lack of coverage of identity groups in their evaluation, and the range of their associated stereotypes. Additionally, these studies often lack a critical distinction between inherently visual stereotypes, such as `brown' or `sombrero', and culturally influenced stereotypes like `kind' or `intelligent'. In this work, we address these limitations by grounding our evaluation of regional, geo-cultural stereotypes in the generated images from Text-to-Image models by leveraging existing textual resources. We employ existing stereotype benchmarks to evaluate stereotypes and focus exclusively on the identification of visual stereotypes within the generated images spanning 135 identity groups. We also compute the offensiveness across identity groups, and check the feasibility of identifying stereotypes automatically. Further, through a detailed case study and quantitative analysis, we reveal how the default representations of all identity groups have a more stereotypical appearance, and for historically marginalized groups, how the images across different attributes are visually more similar than other groups, even when explicitly prompted otherwise.
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With rapid development and deployment of generative language models in global settings, there is an urgent need to also scale our measurements of harm, not just in the number and types of harms covered, but also how well they account for local cultural contexts, including marginalized identities and the social biases experienced by them.
This growth in our evaluation paradigms thus, needs to be enhanced and calibrated by including people from different cultures and societies worldwide. In this work, we demonstrate this socio-culturally aware expansion in the Indian societal context for the harm of stereotyping. We devise a community engaged effort to build a resource which contains stereotypes for axes of disparity that are uniquely present in India. The resultant resource increases the number of stereotypes known for and in the Indian context by many folds and is consequently beneficial for evaluations of generative AI.
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Building Stereotype Repositories with Complementary Approaches
Akshita Jha
Jaya Goyal
Dinesh Tewari
C3NLP workshop at EACL 2023(2023)
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Measurements of fairness in NLP have been critiqued for lacking concrete definitions of biases or harms measured, and for perpetuating a singular, Western narrative of fairness globally. To combat some of these pivotal issues, methods for curating datasets and benchmarks that target specific harms are rapidly emerging. However, these methods still face the significant challenge of achieving coverage over global cultures and perspectives at scale. To address this, in this paper, we highlight the utility and importance of complementary approaches in these curation strategies, which leverage both community engagement as well as large generative models. We specifically target the harm of stereotyping and demonstrate a pathway to build a benchmark that covers stereotypes about diverse, and intersectional identities.
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Preview abstract
With rapid development and deployment of generative language models in global settings, there is an urgent need to also scale our measurements of harm, not just in the number and types of harms covered, but also how well they account for local cultural contexts, including marginalized identities and the social biases experienced by them.
This growth in our evaluation paradigms thus, needs to be enhanced and calibrated by including people from different cultures and societies worldwide. In this work, we demonstrate this socio-culturally aware expansion in the Indian societal context for the harm of stereotyping. We devise a community engaged effort to build a resource which contains stereotypes for axes of disparity that are uniquely present in India. The resultant resource increases the number of stereotypes known for and in the Indian context by many folds and is consequently beneficial for evaluations of generative AI.
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All that Agrees Is Not Gold: Evaluating Ground Truth Labels and Dialogue Content for Safety
Chris Homan
Greg Serapio-García
Alex Taylor
(2023)
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Dialogue safety as a task is complex, in part because ‘safety’ entails a broad range of topics and concerns, such as toxicity, harm, legal concerns, health advice, etc. Who we ask to judge safety and who we ask to define safety may lead to differing conclusions. This is because definitions and understandings of safety can vary according to one’s identity, public opinion, and the interpretation of existing laws and regulations. In this study, we compare annotations from a diverse set of over 100 crowd raters to gold labels derived from trust and safety (T&S) experts in a dialogue safety task consisting of 350 human-chatbot conversations. We find patterns of disagreements rooted in dialogue structure, dialogue content, and rating rationale. In contrast to typical approaches which treat gold labels as ground truth, we propose alternative ways of interpreting gold data and incorporating crowd disagreement rather than mitigating it. We discuss the complexity of safety annotation as a task, what crowd and T&S labels each uniquely capture, and how to make determinations about when and how to rely on crowd or T&S labels.
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Along with the recent advances in large language modeling, there is growing concern that language technologies may reflect, propagate, and amplify various social stereotypes about groups of people. Publicly available stereotype benchmarks play a crucial role in detecting and mitigating this issue in language technologies to prevent both representational and allocational harms in downstream applications. However, existing stereotype benchmarks are limited in their size and coverage, largely restricted to stereotypes prevalent in the Western society. This is especially problematic as language technologies are gaining hold across the globe. To address this gap, we present SeeGULL, a broad-coverage stereotype dataset, expanding the coverage by utilizing the generative capabilities of large language models such as PaLM and GPT-3, and leveraging a globally diverse rater pool to validate prevalence of those stereotypes in society. SeeGULL is an order of magnitude larger in terms of size, and contains stereotypes for 179 identity groups spanning 6 continents, 8 different regions, 178 countries, 50 US states, and 31 Indian states and union territories. We also get fine-grained offensiveness scores for different stereotypes and demonstrate how stereotype perceptions for the same identity group differs across in-region vs out-region annotators.
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Intersecting Demographics: Bayesian Multilevel Models Reveal Age, Gender, and Racial Differences in Safety Perception of Chatbot Conversations
Chris Homan
Greg Serapio-García
Alex Taylor
(2023)
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Chatbots based on large language models (LLM) exhibit a level of human-like behavior that promises to have profound impacts on how people access information, create content, and seek social support. Yet these models have also shown a propensity toward biases and hallucinations, i.e., make up entirely false information and convey it as truthful. Consequently, understanding and moderating safety risks in these models is a critical technical and social challenge. We use Bayesian multilevel models to explore the connection between rater demographics and their perception of safety in chatbot dialogues. We study a sample of 252 human raters stratified by gender, age, race/ethnicity, and location. Raters were asked to annotate the safety risks of 1,340 chatbot conversations. We show that raters from certain demographic groups are more likely to report safety risks than raters from other groups. We discuss the implications of these differences in safety perception and suggest measures to ameliorate these differences.
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DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Alex Taylor
Chris Homan
Greg Serapio-García
NeurIPS2023(2023)
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Machine learning approaches often require training and evaluation datasets with a
clear separation between positive and negative examples. This risks simplifying
and even obscuring the inherent subjectivity present in many tasks. Preserving such
variance in content and diversity in datasets is often expensive and laborious. This
is especially troubling when building safety datasets for conversational AI systems,
as safety is both socially and culturally situated. To demonstrate this crucial
aspect of conversational AI safety, and to facilitate in-depth model performance
analyses, we introduce the DICES (Diversity In Conversational AI Evaluation for
Safety) dataset that contains fine-grained demographic information about raters,
high replication of ratings per item to ensure statistical power for analyses, and
encodes rater votes as distributions across different demographics to allow for indepth explorations of different aggregation strategies. In short, the DICES dataset
enables the observation and measurement of variance, ambiguity, and diversity in
the context of conversational AI safety. We also illustrate how the dataset offers
a basis for establishing metrics to show how raters’ ratings can intersects with
demographic categories such as racial/ethnic groups, age groups, and genders. The
goal of DICES is to be used as a shared resource and benchmark that respects
diverse perspectives during safety evaluation of conversational AI systems.
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MD3: The Multi-Dialect Dataset of Dialogues
Clara Rivera
Dora Demszky
Devyani Sharma
InterSpeech(2023) (to appear)
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We introduce a new dataset of conversational speech representing English from India, Nigeria, and the United States. Unlike prior datasets, the Multi-Dialect Dataset of Dialogues (MD3) strikes a balance between open-ended conversational speech and task-oriented dialogue by prompting participants to perform a series of short information-sharing tasks.
This facilitates quantitative cross-dialectal comparison, while avoiding the imposition of a restrictive task structure that might inhibit the expression of dialect features.
Preliminary analysis of the dataset reveals significant differences in syntax and in the use of discourse markers. The dataset includes more than 20 hours of audio and more than 200,000 orthographically-transcribed tokens, and is made publicly available at \url{https://www.kaggle.com/datasets/jacobeis99/md3en}.
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