A reading list for some interesting papers on Human-centered AI, with a focus on computer vision

Data collection and annotation

How training data are collected by human subjects and how can we speed up the annotation process.

• Kovashka, A., Russakovsky, O., Fei-Fei, L. and Grauman, K., 2016. Crowdsourcing in computer vision. Foundations and Trends in Computer Graphics and Vision
• Tsipras, D., Santurkar, S., Engstrom, L., Ilyas, A. and Madry, A., 2020. From imagenet to image classification: Contextualizing progress on benchmarks. In International Conference on Machine Learning
• Acuna, D., Ling, H., Kar, A. and Fidler, S., 2018. Efficient interactive annotation of segmentation datasets with polygon-rnn++. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition
• Mandlekar, Ajay, et al. "Roboturk: A crowdsourcing platform for robotic skill learning through imitation." Conference on Robot Learning. PMLR, 2018.

Weak supervision and self-supervision

How we reduce the human supervision in different learning schemes.

• Li, B., Weinberger, K.Q., Belongie, S., Koltun, V. and Ranftl, R., 2022. Language-driven Semantic Segmentation. ICLR'22
• Lan, S., Yu, Z., Choy, C., Radhakrishnan, S., Liu, G., Zhu, Y., ... & Anandkumar, A. (2021). DISCOBOX: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision. ICCV 2021
• Ke, Tsung-Wei, Jyh-Jing Hwang, and Stella X. Yu., 2021. Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning. ICLR, 2021
• He, K., Chen, X., Xie, S., Li, Y., Dollár, P. and Girshick, R., 2021. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377.
• Weakly supervised object localization: https://github.com/xiaomengyc/Weakly-Supervised-Object-Localization
• Sun, C., Shrivastava, A., Singh, S. and Gupta, A., 2017. Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE international conference on computer vision

Explainable ML

How we visualize features and explain the prediction of AI models.

• Bau, D., Zhu, J.Y., Strobelt, H., Lapedriza, A., Zhou, B. and Torralba, A., 2020. Understanding the role of individual units in a deep neural network. Proceedings of the National Academy of Sciences
• Olah, C., Mordvintsev, A. and Schubert, L., 2017. Feature visualization. Distill, 2(11)
• Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K. and Mordvintsev, A., 2018. The building blocks of interpretability. Distill
• CAM, grad-CAM, and many other CAM variants. https://github.com/frgfm/torch-cam , https://github.com/jacobgil/pytorch-grad-cam
• Ghorbani, A., Wexler, J., Zou, J.Y. and Kim, B., 2019. Towards automatic concept-based explanations. Advances in Neural Information Processing Systems,

Debate on explainable ML

Is expalinable ML really meaningful and useful?

• Adebayo, J., Muelly, M., Abelson, H. and Kim, B., 2021, Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. In International Conference on Learning Representations.
• Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M. and Kim, B., 2018. Sanity checks for saliency maps. arXiv preprint arXiv:1810.03292.
• Leavitt, M.L. and Morcos, A., 2020. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016, on the importance of single directions for generalization (https://arxiv.org/pdf/1803.06959.pdf), revisiting the importance of individual units in CNNs via ablation (https://arxiv.org/pdf/1806.02891.pdf)
• Ghassemi, Marzyeh, Luke Oakden-Rayner, and Andrew L. Beam. "The false hope of current approaches to explainable artificial intelligence in health care." The Lancet Digital Health 3.11 (2021): e745-e750.
• Zachary Lipton. The Mythos of Model Interpretability. 2017

AI Bias (dataset bias and algorithmic bias)

How we identify the various biases in AI models and data collection processes.

• Hendricks, L.A., Burns, K., Saenko, K., Darrell, T. and Rohrbach, A., 2018. Women also snowboard: Overcoming bias in captioning models. In Proceedings of the European Conference on Computer Vision (ECCV)
• Hooker, S., 2021. Moving beyond “algorithmic bias is a data problem”. Patterns
• Torralba, A. and Efros, A.A., 2011, June. Unbiased look at dataset bias. In CVPR 2011
• Buolamwini, J. and Gebru, T., 2018, January. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency
• Algorithmic bias: https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/

AI robustness

How AI models are robust to adversarial samples and the real-world perturbations.

• Song, D., Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Tramer, F., Prakash, A. and Kohno, T., 2018. Physical adversarial examples for object detectors. In 12th USENIX workshop on offensive technologies (WOOT 18). Another relevant paper on LiDAR adversarial attack: Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
• Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B. and Madry, A., 2019. Adversarial examples are not bugs, they are features. Advances in neural information processing systems, 32
• Xiao, K.Y., Engstrom, L., Ilyas, A. and Madry, A., 2020, September. Noise or Signal: The Role of Image Backgrounds in Object Recognition. In International Conference on Learning Representations.
• Hendrycks, D. and Dietterich, T., 2018, September. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. In International Conference on Learning Representations.
• Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A. and Brendel, W., 2018. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231.

Human-AI Collaboration

How we collaborate with AI models to accomplish tasks.

• Cai, Carrie J., et al. "Human-centered tools for coping with imperfect algorithms during medical decision-making." CHI'19
• Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J. and Paoli, J., 2020. Human–computer collaboration for skin cancer recognition. Nature Medicine, 26(8), pp.1229-1234.
• OpenAI Codex (Github Copilot): AI Pair-programming: https://openai.com/blog/openai-codex/
• Buçinca, Z., Malaya, M.B. and Gajos, K.Z., 2021. To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction

Human-AI Creation

How generative models such as GAN and diffusion models can be used for interactive content creation.

• Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors (https://arxiv.org/pdf/2203.13131.pdf)
• Wang, S.Y., Bau, D. and Zhu, J.Y., 2021. Sketch your own gan. ICCV. (https://peterwang512.github.io/GANSketching/)
• Park, Taesung, et al. "Semantic image synthesis with spatially-adaptive normalization." CVPR 2019.
• Unsupervised discovery of steerable dimensions: https://genforce.github.io/sefa/, GANSpace (https://github.com/harskish/ganspace), LatentCLR (https://github.com/catlab-team/latentclr)
• Yan, Chuan, et al. "FlatMagic: Improving Flat Colorization through AI-driven Design for Digital Comic Professionals." CHI Conference on Human Factors in Computing Systems. 2022.
• DALLE2 paper Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125.

Human-in-the-loop autonomy

Howe humans work together with AI for accomplishing control tasks.

• Reddy, S., Dragan, A.D. and Levine, S., Shared Autonomy via Deep Reinforcement Learning. RSS 2018
• Li, Q., Peng, Z. and Zhou, B., 2022. Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization. ICLR'22
• Lee, K., Smith, L.M. and Abbeel, P., 2021, July. PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training. In International Conference on Machine Learning
• Spencer, J., Choudhury, S., Barnes, M., Schmittle, M., Chiang, M., Ramadge, P. and Srinivasa, S., 2021. Expert Intervention Learning. Autonomous Robots
• Zhang, R., Torabi, F., Warnell, G. and Stone, P., 2021. Recent advances in leveraging human guidance for sequential decision-making tasks. Autonomous Agents and Multi-Agent Systems
• Reddy, Sid, Anca Dragan, and Sergey Levine. "Pragmatic Image Compression for Human-in-the-Loop Decision-Making." Advances in Neural Information Processing Systems
• Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S. and Amodei, D., 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems (https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/ )
• Hilton, J., Cammarata, N., Carter, S., Goh, G. and Olah, C., 2020. Understanding rl vision. Distill, 5(11), p.e29.

Knowledge generation from superhuman AI

How we acquire knowledge from superhuman AI.

• McGrath, T., Kapishnikov, A., Tomašev, N., Pearce, A., Hassabis, D., Kim, B., ... & Kramnik, V. (2021). Acquisition of Chess Knowledge in AlphaZero. arXiv preprint arXiv:2111.09259.
• Wurman, P.R., Barrett, S., Kawamoto, K., MacGlashan, J., Subramanian, K., Walsh, T.J., Capobianco, R., Devlic, A., Eckert, F., Fuchs, F. and Gilpin, L., 2022. Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature
• Vinyals, Oriol, et al. "Grandmaster level in StarCraft II using multi-agent reinforcement learning." Nature 575.7782 (2019): 350-354.