Abstract
Why we need to study machine learning fairness, even in an increasingly unfair world.
- Liu, L. T., Dean, S., Rolf, E., Simchowitz, M. and Hardt, M. 2018. Delayed impact of fair machine learning. Proceedings of the 35th International Conference on Machine Learning, in PMLR 80. 2018; https://arxiv.org/abs/1803.04383Google Scholar
- Mikians, J., Gyarmati, L., Erramilli, V., and Laoutaris, N.. Detecting price and search discrimination on the internet. In Proceedings of the 11th ACM Workshop on Hot Topics in Networks {HotNets-XI}. ACM, New York, 2012, 79--84 Google ScholarDigital Library
- Buolamwini, J. and Gebru, T. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in PMLR 81. 2018; http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdfGoogle Scholar
- AAdamson, A. S. and Smith, A. Machine learning and health care disparities in dermatology. JAMA Dermatology 154, 11 {2018}, 1247--1248Google ScholarCross Ref
- Angwin, J., Larson, J., Mattu, S., and Kirchner, L. Machine bias. Pro Publica. {May 23, 2016}; https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencingGoogle Scholar
- Shankar, S. et. al. No classification without representation: Assessing geodiversity issues in open data sets for the developing world. In NIPS 2017 Workshop: Machine Learning for the Developing World. arXiv. 2017; https://arxiv.org/abs/1711.08536Google Scholar
- Diakopoulos, N. Algorithmic Accountability Reporting: On the Investigation of Black Boxes. Tow Center for Digital Journalism Publications. Tow Center for Digital Journalism, Columbia University, New York, 2014.Google Scholar
- Dastin, J. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. {October 9, 2018}; https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08GGoogle Scholar
- Gilbert, D. T. and Hixon, G. J. The trouble of thinking: activation and application of stereotypic beliefs. Journal of Personality & Social Psychology 60, 4 {1991}, 509--517Google ScholarCross Ref
- Sahil, V., S. and Rubin, J.. Fairness definitions explained. In FairWare'18: IEEE/ACM International Workshop on Software Fairness. ACM, New York, 2018 Google ScholarDigital Library
- Fair Housing Act. The 7 Protected Classes Under the Fair Housing Act. {2016}; https://fairhousingact.org/the-7-protected-classes-under-the-fair-housing-act/Google Scholar
Index Terms
- That's not fair!
Recommendations
T2-fair: a two-tiered time and throughput fair scheduler for multi-rate WLANs
MSWiM '06: Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systemsLow throughput due to unfairness is a key problem in multi-rate wireless local area networks. To promote fairness and hence throughput, T2-Fair groups flows according to their average data rate, provides each group fair time allocations and ensures ...
Can CSMA/CA networks be made fair?
MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networkingWe demonstrate that CSMA/CA networks, including IEEE 802.11 networks, exhibit severe fairness problem in many scenarios, where some hosts obtain most of the channel's bandwidth while others starve. Most existing solutions require nodes to overhear ...
Proportional fair throughput allocation in multirate IEEE 802.11e wireless LANs
Under heterogeneous radio conditions, Wireless LAN stations may use different modulation schemes, leading to a heterogeneity of bit rates. In such a situation, 802.11 DCF allocates the same throughput to all stations independently of their transmitting ...
Comments