Skip to end of metadata
Go to start of metadata

Proposal References

Bellamy, Rachel KE, et al. "AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias." arXiv preprint arXiv:1810.01943 (2018).

Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification." Conference on fairness, accountability and transparency. 2018.

Caliskan-Islam, Aylin, Joanna J. Bryson, and Arvind Narayanan. "Semantics derived automatically from language corpora necessarily contain human biases." arXiv preprint arXiv:1608.07187 (2016): 1-14.

Dalton, Craig & Thatcher, Jim (12 May 2014). "What does a Critical Data Studies look like and why do we care?". Society + Space. Retrieved 17 January 2018.

Gebru, Timnit, et al. "Datasheets for datasets." arXiv preprint arXiv:1803.09010 (2018).

Gemignani, Z.  (2014) Data Fluency: Empowering Your Organization with Effective Data Communication. Indianapolis: Wiley

Holland, Sarah, et al. "The dataset nutrition label: A framework to drive higher data quality standards." arXiv preprint arXiv:1805.03677 (2018).

Hussain, Ajaz, Sara Diamond, Sara; Szigeti, Steve; Gordon, Marcus A., Yuan, Feng; Diep, Melissa and Dang, Lan-Xi (2019) HCI Design Principles and Visual Analytics for Media Analytics Platform, Proceedings HCII, Orlando, 2019. Springer. 

Jeong, Dong Heung, Ziemkiewicz, Caroline, Fisher, Brian, Ribarsky, William & Chang, Remco (2009) iPCA: An Interactive System for PCA-based Visual Analytics, Computer Graphics Forum, Volume 28, Issue 3. Pages 767 – 774.

Karawash, Ahmad; Diamond, Sara; Gordon, Marcus; Rabbaa, Jad; Shepko, Roxolyana Greice Mariano; Dong, Lan-Xi; Samaei, Afrooz; and Ritchie, Hugh (2018).  TasteGraph: A Visual Analytics Tool for Profiling Media Audiences' Tastes. Proceedings, IEEEVis, InfoVis, Berlin, 2018

Khosla, Aditya, et al. "Undoing the damage of dataset bias." European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2012.

Li, Yi, and Nuno Vasconcelos. "Repair: Removing representation bias by dataset resampling." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.

Liu, Xin, Gilles Tredan, and Anwitaman Datta. "A Generic Trust Framework for Large‐scale Open Systems Using Machine Learning." Computational Intelligence 30.4 (2014): 700-721.

Lorce, E. (2016) Workshop of Visualization for Deep Learning, icm/

Mariano,  Greice C. , Adnani, Veda; Kewalramani, Iman; Wang Bo; Roorda Matthew J., Bowes, Jeremy; Diamond, S. (Forthcoming) Designing a Dashboard Visualization tool for Urban Planners to assess the completeness of streets. Proceedings HCII, Copenhagen, 2020. Springer.

Mas, Jean-François, et al. "A suite of tools for ROC analysis of spatial models." ISPRS International Journal of Geo-Information 2.3 (2013): 869-887.

Meirelles, 2013] Meirelles, I. (2013). Design for information: an introduction to the histories, theories, and best practices behind effective information visualizations. Rockport publishers.

Mitchell, Margaret, et al. "Model cards for model reporting." Proceedings of the conference on fairness, accountability, and transparency. 2019.

Nguyen, Giang Hoang, Abdesselam Bouzerdoum, and Son Lam Phung. "Learning pattern classification tasks with imbalanced data sets." Pattern recognition (2009): 193-208.

Powers, David Martin. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." (2011).

Rotello, Caren M., Evan Heit, and Chad Dubé. "When more data steer us wrong: Replications with the wrong dependent measure perpetuate erroneous conclusions." Psychonomic Bulletin & Review 22.4 (2015): 944-954.

Thomas, Rachel, and David Uminsky. "The Problem with Metrics is a Fundamental Problem for AI." arXiv preprint arXiv:2002.08512 (2020).

Torralba, Antonio, and Alexei A. Efros. "Unbiased look at dataset bias." CVPR 2011. IEEE, 2011.

Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., Hebert, D., Lyckowski, N. and Manser, E., 2019. Considerations for AI fairness for people with disabilities. AI Matters, 5(3), pp.40-63.

Walny, J., Frisson, C., West, M., Kosminsky, D., Knudsen, S., Carpendale, S., Willett, W. (2019). Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff. IEEE Transactions on Visualization and Computer Graphics. PP. 1-1. 10.1109/TVCG.2019.2934538.

Yeom, Samuel, and Michael Carl Tschantz. "Discriminative but not discriminatory: A comparison of fairness definitions under different worldviews." arXiv preprint arXiv:1808.08619 (2018).

Yin, Ming, Jennifer Wortman Vaughan, and Hanna Wallach. "Understanding the effect of accuracy on trust in machine learning models." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019.

  • No labels