• Berman G, Goyal N and Madaio M. A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. (1-24).

    https://doi.org/10.1145/3613904.3642398

  • Denham B, Lai E, Sinha R and Naeem M. (2022). Witan. Proceedings of the VLDB Endowment. 15:11. (2334-2347). Online publication date: 1-Jul-2022.

    https://doi.org/10.14778/3551793.3551797

  • Galhotra S, Fariha A, Lourenço R, Freire J, Meliou A and Srivastava D. DataPrism: Exposing Disconnect between Data and Systems. Proceedings of the 2022 International Conference on Management of Data. (217-231).

    https://doi.org/10.1145/3514221.3517864

  • Chai C, Wang J, Luo Y, Niu Z and Li G. Data Management for Machine Learning: A Survey. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2022.3148237. (1-1).

    https://ieeexplore.ieee.org/document/9705125/

  • Lai T. Towards the generation of machine learning defect reports. Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering. (1038-1042).

    https://doi.org/10.1109/ASE51524.2021.9678592

  • Li P, Li J, Chen Y, Pei Y, Fu G and Xie H. (2021). Classification and recognition of computed tomography images using image reconstruction and information fusion methods. The Journal of Supercomputing. 77:3. (2645-2666). Online publication date: 1-Mar-2021.

    https://doi.org/10.1007/s11227-020-03367-y

  • Li P, Li J, Xie H, Pei Y and Feng H. (2021). Recognition and Diagnosis of Computed Tomography Images Using Reconstructive Techniques. Frontier Computing. 10.1007/978-981-16-0115-6_1. (1-11).

    https://link.springer.com/10.1007/978-981-16-0115-6_1

  • Wu T, Weld D and Heer J. (2019). Local Decision Pitfalls in Interactive Machine Learning. ACM Transactions on Computer-Human Interaction. 26:4. (1-27). Online publication date: 31-Aug-2019.

    https://doi.org/10.1145/3319616

  • Thirumuruganathan S, Ouzzani M and Tang N. Explaining Entity Resolution Predictions. Proceedings of the Workshop on Human-In-the-Loop Data Analytics. (1-6).

    https://doi.org/10.1145/3328519.3329130

  • Boehm M, Kumar A and Yang J. (2019). Data Management in Machine Learning Systems. Synthesis Lectures on Data Management. 10.2200/S00895ED1V01Y201901DTM057. 14:1. (1-173). Online publication date: 25-Feb-2019.

    https://www.morganclaypool.com/doi/10.2200/S00895ED1V01Y201901DTM057

  • Nashaat M, Ghosh A, Miller J, Quader S, Marston C and Puget J. (2018). Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets 2018 IEEE International Conference on Big Data (Big Data). 10.1109/BigData.2018.8622459. 978-1-5386-5035-6. (46-55).

    https://ieeexplore.ieee.org/document/8622459/

  • Varma P and Ré C. (2018). Snuba. Proceedings of the VLDB Endowment. 12:3. (223-236). Online publication date: 1-Nov-2018.

    https://doi.org/10.14778/3291264.3291268

  • Li T, Zhong J, Liu J, Wu W and Zhang C. (2018). Ease.ml. Proceedings of the VLDB Endowment. 11:5. (607-620). Online publication date: 1-Jan-2018.

    https://doi.org/10.1145/3187009.3177737

  • Li T, Zhong J, Liu J, Wu W and Zhang C. (2018). Ease.ml. Proceedings of the VLDB Endowment. 11:5. (607-620). Online publication date: 1-Jan-2018.

    https://doi.org/10.1145/3177732.3177737