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@aylin_cim
My research interests lie in artificial intelligence (AI) ethics, AI bias, computer vision, natural language processing, and machine learning. I study the transfer of information from society to AI and the impact of machine intelligence on society, especially threats to equity. I investigate the reasoning* behind biased AI representations and decisions by developing theoretically grounded statistical methods that uncover and quantify human-like biases learned by machines. Building these transparency enhancing algorithms involves the use of machine learning, natural language processing, and computer vision to interpret AI's co-evolution with society and gain insights into artificial and natural intelligence.
*A note about my approach to research, teaching, and intellectual growth: I tend to ask a lot of questions that start with 'why,' 'how,' and 'what if.'
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Historical Representations of Social Groups Across 200 Years of Word Embeddings from Google Books Proceedings of the National Academy of Sciences (PNAS 2022) Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2022) American == White in Multimodal Language-and-Image AI AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2022) Measuring Gender Bias in Word Embeddings of Gendered Languages Requires Disentangling Grammatical Gender Signals AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2022) Evidence for Hypodescent in Visual Semantic AI The 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) Markedness in Visual Semantic AI The 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022) Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022) Detecting Emerging Associations and Behaviors With Regional and Diachronic Word Embeddings 16th IEEE International Conference on Semantic Computing (ICSC 2022) Learning to Behave: Improving Covert Channel Security with Behavior-Based Designs Privacy Enhancing Technologies Symposium (PETS 2022) Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models Empirical Methods in Natural Language Processing (EMNLP 2021) ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries Empirical Methods in Natural Language Processing (EMNLP 2021) Social biases in word embeddings and their relation to human cognition Book Chapter in The Handbook of Language Analysis in Psychology. Guilford Press, 2021 Editors Morteza Dehghani and Ryan Boyd Detecting and mitigating bias in natural language processing Brookings 2021 Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2021) Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2021) Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases The 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2021) A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild AI and Ethics, 2021 Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter 15th IEEE International Conference on Semantic Computing (ICSC 2021) |