Aylin Caliskan

My first name is pronounced as Eye-lynn

Assistant Professor
Information School
University of Washington
Nonresident Fellow in Governance at Brookings
  aylin@uw.edu        @aylin_cim

Aylin

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.'

News

  • We are presenting two papers at EMNLP 2021 on evaluating word embeddings and bias in neural language models.
  • My paper on AI bias is published in Science. Semantics derived automatically from language corpora contain human-like biases.
    source code
  • I am the moderator of Computer Science - Computers and Society on arXiv.
  • Research

  • Tessa Charlesworth, Aylin Caliskan, and Mahzarin R. Banaji
    Historical Representations of Social Groups Across 200 Years of Word Embeddings from Google Books
    Proceedings of the National Academy of Sciences (PNAS 2022)
  • Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth, Robert Wolfe, and Mahzarin R. Banaji
    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)
  • Robert Wolfe and Aylin Caliskan
    American == White in Multimodal Language-and-Image AI
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2022)
  • Shiva Omrani Sabbaghi and Aylin Caliskan
    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)
  • Robert Wolfe, Mahzarin R. Banaji, and Aylin Caliskan
    Evidence for Hypodescent in Visual Semantic AI
    The 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022)
  • Robert Wolfe and Aylin Caliskan
    Markedness in Visual Semantic AI
    The 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2022)
  • Robert Wolfe and Aylin Caliskan
    Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations
    60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)
  • Robert Wolfe and Aylin Caliskan
    VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models
    Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022)
  • Robert Wolfe and Aylin Caliskan
    Detecting Emerging Associations and Behaviors With Regional and Diachronic Word Embeddings
    16th IEEE International Conference on Semantic Computing (ICSC 2022)
  • Ryan Wails, Andrew Stange, Eliana Troper, Aylin Caliskan, Roger Dingledine, Rob Jansen, and Micah Sherr
    Learning to Behave: Improving Covert Channel Security with Behavior-Based Designs
    Privacy Enhancing Technologies Symposium (PETS 2022)
  • Robert Wolfe and Aylin Caliskan
    Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
    Empirical Methods in Natural Language Processing (EMNLP 2021)
  • Autumn Toney-Wails and Aylin Caliskan
    ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries
    Empirical Methods in Natural Language Processing (EMNLP 2021)
  • Aylin Caliskan and Molly Lewis
    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
  • Aylin Caliskan
    Detecting and mitigating bias in natural language processing
    Brookings 2021
  • Akshat Pandey and Aylin Caliskan
    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)
  • Wei Guo and Aylin Caliskan
    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)
  • Ryan Steed and Aylin Caliskan
    Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
    The 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2021)
  • Ryan Steed and Aylin Caliskan
    A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
    AI and Ethics, 2021
  • Autumn Toney, Akshat Pandey, Wei Guo, David Broniatowski, and Aylin Caliskan
    Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
    15th IEEE International Conference on Semantic Computing (ICSC 2021)