mlsanityData to 7 Oct 2026

Tribe or Not? Critical Inspection of Group Differences Using TribalGram

arXiv 2303.09664 ↗First posted 16 Mar 2023cs.LG cs.AI cs.HCcited by 0

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Abstract (arXiv)

With the rise of AI and data mining techniques, group profiling and group-level analysis have been increasingly used in many domains including policy making and direct marketing. In some cases, the statistics extracted from data may provide insights to a group's shared characteristics; in others, the group-level analysis can lead to problems including stereotyping and systematic oppression. How can analytic tools facilitate a more conscientious process in group analysis? In this work, we identify a set of accountable group analytics design guidelines to explicate the needs for group differentiation and preventing overgeneralization of a group. Following the design guidelines, we develop TribalGram, a visual analytic suite that leverages interpretable machine learning algorithms and visualization to offer inference assessment, model explanation, data corroboration, and sense-making. Through the interviews with domain experts, we showcase how our design and tools can bring a richer understanding of "groups" mined from the data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 376 of its 1,819 references that are in the map. First posted on arXiv 16 Mar 2023.

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References · 376

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  5. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  6. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  7. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  8. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  9. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  10. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  11. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  12. 2017Graph Attention Networkscited by 8,854
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  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2017Mask R-CNNcited by 7,259
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2017Inductive Representation Learning on Large Graphscited by 6,284
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  8. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  9. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  10. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  11. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  12. 2018How Powerful are Graph Neural Networks?cited by 4,355
  13. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  14. 2014Going Deeper with Convolutionscited by 4,231
  15. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  16. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  17. 2013Auto-Encoding Variational Bayescited by 3,980
  18. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  19. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  20. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  21. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  22. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  23. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  24. 2018YOLOv3: An Incremental Improvementcited by 2,652
  25. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  26. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  27. 2018Deep contextualized word representationscited by 2,285
  28. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  29. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  30. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  31. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  32. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
  33. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  34. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  35. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  36. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  37. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  38. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479

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