Fetching the paper…
Reading the bibliography…
Machine Learning or Artificial Intelligence algorithms have gained considerable scrutiny in recent times owing to their propensity towards imitating and amplifying existing prejudices in society.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 1908
Earlier work this paper cites.
On the apparent conflict between individual and group fairness
Reuben Binns · 1912
Earlier work this paper cites.
Equal employment opportunity under the civil rights act of 1964
Richard K Berg · 1964
Earlier work this paper cites.
On simpson’s paradox and the sure-thing principle
Colin R Blyth · 1972
Earlier work this paper cites.
Adoption of questions and answers to clarify and provide a common interpretation of the uniform guidelines on employee selection procedures
Equal Employment Opportunity Commission et al · 1979
Earlier work this paper cites.
Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics
Kimberlé Crenshaw · 1989
Earlier work this paper cites.
Justice as fairness: A restatement
John Rawls · 2001
Earlier work this paper cites.
Multi–objective evolutionary algorithms for the risk–return trade–off in bank loan management
Amitabha Mukerjee, Rita Biswas, Kalyanmoy Deb, and Amrit P Mathur · 2002
Earlier work this paper cites.
Multilinear image analysis for facial recognition
M Alex O Vasilescu and Demetri Terzopoulos · 2002
Earlier work this paper cites.
A systematic review of empirical research on self-reported racism and health
Yin Paradies · 2006
Earlier work this paper cites.
Indigenous health part 2: the underlying causes of the health gap
Malcolm King, Alexandra Smith, and Michael Gracey · 2009
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Adult development and quality of life of transgender and gender nonconforming people
Walter Bockting, Eli Coleman, Madeline B Deutsch, Antonio Guillamon, Ilan Meyer, Walter Meyer III, Sari Reisner, Jae Sevelius, and Randi Ettner · 2016
Cited alongside, same era.
Priorities for transgender medical and health care research
Jamie Feldman, George R Brown, Madeline B Deutsch, Wylie Hembree, Walter Meyer, Heino FL Meyer-Bahlburg, Vin Tangpricha, Guy T’Sjoen, and Joshua D Safer · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Advancing methods for us transgender health research
Sari L Reisner, Madeline B Deutsch, Shalender Bhasin, Walter Bockting, George R Brown, Jamie Feldman, Rob Garofalo, Baudewijntje Kreukels, Asa Radix, Joshua D Safer, et al · 2016
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Later among the works it cites.
Fairness of exposure in rankings
Ashudeep Singh and Thorsten Joachims · 2018
Later among the works it cites.
Using the data agreement criterion to rank experts’ beliefs
Duco Veen, Diederick Stoel, Naomi Schalken, Kees Mulder, and Rens Van de Schoot · 2018
Later among the works it cites.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
Later among the works it cites.
Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi · 2019
Later among the works it cites.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
European union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich · 2017
Cited alongside, same era.
Help wanted: An examination of hiring algorithms, equity, and bias
Miranda Bogen and Aaron Rieke · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum · 2018
Cited alongside, same era.
Later among the works it cites.
Fairness-aware learning for continuous attributes and treatments
Jérémie Mary, Clément Calauzènes, and Noureddine El Karoui · 2019
Later among the works it cites.
Public sphere 2.0: Targeted commenting in online news media
Ankan Mullick, Sayan Ghosh, Ritam Dutt, Avijit Ghosh, and Abhijnan Chakraborty · 2019
Later among the works it cites.
Quantifying the impact of user attentionon fair group representation in ranked lists
Piotr Sapiezynski, Wesley Zeng, Ronald E Robertson, Alan Mislove, and Christo Wilson · 2019
Later among the works it cites.
An intersectional definition of fairness
James R Foulds, Rashidul Islam, Kamrun Naher Keya, and Shimei Pan · 2020
Later among the works it cites.
A New Metric for Quantifying Machine Learning Fairness in Healthcare
Joseph Gartner · 2020
Later among the works it cites.