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Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery.
Fan, J., Han, F., Liu, H.: Challenges of big data analysis. National science review 1
2014
Earlier work this paper cites.
Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: Quantifying interpretability of deep visual representations. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6541–6549 (2017)
2017
Earlier work this paper cites.
Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Buolamwini, J., Gebru, T.: Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency. pp. 77–91. PMLR (2018)
2018
Earlier work this paper cites.
Hendricks, L.A., Burns, K., Saenko, K., Darrell, T., Rohrbach, A.: Women also snowboard: Overcoming bias in captioning models. In: Proceedings of the European conference on computer vision (ECCV). pp. 771–787 (2018)
2018
Earlier work this paper cites.
Kim, B., Han Lee, Y., Jung, H., Cho, C.: Distinctive-attribute extraction for image captioning. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops. pp. 0–0 (2018)
2018
Earlier work this paper cites.
Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., West, S.M., Richardson, R., Schultz, J., Schwartz, O., et al.: AI now report 2018. AI Now Institute at New York University New York (2018)
2018
Earlier work this paper cites.
Zhou, B., Sun, Y., Bau, D., Torralba, A.: Interpretable basis decomposition for visual explanation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 119–134 (2018)
2018
Earlier work this paper cites.
Chiappa, S.: Path-specific counterfactual fairness. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 7801–7808 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Ghorbani, A., Wexler, J., Zou, J.Y., Kim, B.: Towards automatic concept-based explanations. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Kurita, K., Vyas, N., Pareek, A., Black, A.W., Tsvetkov, Y.: Measuring bias in contextualized word representations. In: Proceedings of the First Workshop on Gender Bias in Natural Language Processing. pp. 166–172 (2019)
2019
Earlier work this paper cites.
Liu, B., Deng, W., Zhong, Y., Wang, M., Hu, J., Tao, X., Huang, Y.: Fair loss: Margin-aware reinforcement learning for deep face recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10052–10061 (2019)
2019
Earlier work this paper cites.
Sokol, K., Flach, P.A.: Counterfactual explanations of machine learning predictions: Opportunities and challenges for ai safety. SafeAI@ AAAI pp. 1–4 (2019)
2019
Earlier work this paper cites.
Wu, Y., Zhang, L., Wu, X.: Counterfactual fairness: Unidentification, bound and algorithm. In: Proceedings of the twenty-eighth international joint conference on Artificial Intelligence (2019)
2019
Earlier work this paper cites.
Abbasnejad, E., Teney, D., Parvaneh, A., Shi, J., Hengel, A.v.d.: Counterfactual vision and language learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10044–10054 (2020)
2020
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y., Denuyl, S.: Social biases in nlp models as barriers for persons with disabilities. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5491–5501 (2020)
2020
Earlier work this paper cites.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 conference on fairness, accountability, and transparency. pp. 607–617 (2020)
2020
Earlier work this paper cites.
Park, S., Hwang, S., Hong, J., Byun, H.: Fair-vqa: Fairness-aware visual question answering through sensitive attribute prediction. IEEE Access 8
2020
Cited alongside, same era.
Shah, D.S., Schwartz, H.A., Hovy, D.: Predictive biases in natural language processing models: A conceptual framework and overview. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 5248–5264 (2020)
2020
Cited alongside, same era.
Wang, Z., Qinami, K., Karakozis, I.C., Genova, K., Nair, P., Hata, K., Russakovsky, O.: Towards fairness in visual recognition: Effective strategies for bias mitigation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8919–8928 (2020)
2020
Cited alongside, same era.
Ahn, J., Oh, A.: Mitigating language-dependent ethnic bias in bert. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 533–549 (2021)
2021
Cited alongside, same era.
2023
Closest in time.
Chen, L., Li, B., Shen, S., Yang, J., Li, C., Keutzer, K., Darrell, T., Liu, Z.: Large language models are visual reasoning coordinators. Advances in Neural Information Processing Systems (2023)
2023
Closest in time.
Chen, Z., Gao, Q., Bosselut, A., Sabharwal, A., Richardson, K.: Disco: distilling counterfactuals with large language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 5514–5528 (2023)
2023
Closest in time.
Cho, J., Zala, A., Bansal, M.: Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3043–3054 (2023)
2023
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Balakrishnan, G., Xiong, Y., Xia, W., Perona, P.: Towards causal benchmarking of biasin face analysis algorithms. In: Deep Learning-Based Face Analytics, pp. 327–359. Springer (2021)
2021
Cited alongside, same era.
Feder, A., Oved, N., Shalit, U., Reichart, R.: Causalm: Causal model explanation through counterfactual language models. Computational Linguistics 47
2021
Cited alongside, same era.
Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., Ureña-López, L.A.: A survey on bias in deep nlp. Applied Sciences 11
2021
Cited alongside, same era.
Kasirzadeh, A., Smart, A.: The use and misuse of counterfactuals in ethical machine learning. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. pp. 228–236 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Cited alongside, same era.
Seyyed-Kalantari, L., Zhang, H., McDermott, M.B., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine 27
2021
Cited alongside, same era.
Wu, T., Ribeiro, M.T., Heer, J., Weld, D.S.: Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 6707–6723 (2021)
2021
Cited alongside, same era.
Zhao, E., Huang, D.A., Liu, H., Yu, Z., Liu, A., Russakovsky, O., Anandkumar, A.: Scaling fair learning to hundreds of intersectional groups (2021)
2021
Cited alongside, same era.
Closest in time.
2023
Closest in time.
2023
Closest in time.
Ghosh, S., Caliskan, A.: ‘person’== light-skinned, western man, and sexualization of women of color: Stereotypes in stable diffusion. In: Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 6971–6985 (2023)
2023
Closest in time.
Hamidieh, K., Zhang, H., Hartvigsen, T., Ghassemi, M.: Identifying implicit social biases in vision-language models (2023)
2023
Closest in time.
2023
Closest in time.
Luccioni, S., Akiki, C., Mitchell, M., Jernite, Y.: Stable bias: Evaluating societal representations in diffusion models. In: Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2023)
2023
Closest in time.
Meister, N., Zhao, D., Wang, A., Ramaswamy, V.V., Fong, R., Russakovsky, O.: Gender artifacts in visual datasets. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4837–4848 (2023)
2023
Closest in time.
Ovalle, A., Subramonian, A., Gautam, V., Gee, G., Chang, K.W.: Factoring the matrix of domination: A critical review and reimagination of intersectionality in ai fairness. In: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. p. 496–511. AIES ’23 (2023)
2023
Closest in time.
2023
Closest in time.
Shukla, P., Bharati, S., Turk, M.: Cavli-using image associations to produce local concept-based explanations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3749–3754 (2023)
2023
Closest in time.
Singh, J., Zheng, L.: Divide, evaluate, and refine: Evaluating and improving text-to-image alignment with iterative vqa feedback. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
2023
Closest in time.
Wang, A., Russakovsky, O.: Overwriting pretrained bias with finetuning data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3957–3968 (2023)
2023
Closest in time.
Wang, J., Liu, X.G., Di, Z., Liu, Y., Wang, X.: T2iat: Measuring valence and stereotypical biases in text-to-image generation. In: Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics (2023)
2023
Closest in time.
Wang, Z.J., Montoya, E., Munechika, D., Yang, H., Hoover, B., Chau, D.H.: Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models. In: The 61st Annual Meeting Of The Association For Computational Linguistics (2023)
2023
Closest in time.
Zhang, C., Chen, X., Chai, S., Wu, C.H., Lagun, D., Beeler, T., De la Torre, F.: Iti-gen: Inclusive text-to-image generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3969–3980 (2023)
2023
Closest in time.
Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic chain of thought prompting in large language models. In: The Eleventh International Conference on Learning Representations (ICLR 2023) (2023)
2023
Closest in time.
Bhatt, G., Das, D., Sigal, L., N Balasubramanian, V.: Mitigating the effect of incidental correlations on part-based learning. Advances in Neural Information Processing Systems 36
2024
Closest in time.