Fetching the paper…
Reading the bibliography…
Facial analysis models are increasingly used in applications that have serious impacts on people's lives, ranging from authentication to surveillance tracking.
Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 1906
Earlier work this paper cites.
The validity and practicality of sun-reactive skin types i through vi
Thomas Bernard Fitzpatrick · 1988
Earlier work this paper cites.
Measuring individual differences in implicit cognition: the implicit association test
Anthony G Greenwald, Debbie E McGhee, and Jordan LK Schwartz · 1998
Earlier work this paper cites.
One-factor-at-a-time versus designed experiments
Veronica Czitrom · 1999
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
Evaluating the independence of sex and expression in judgments of faces
Patricia M Le Gal and Vicki Bruce · 2002
Earlier work this paper cites.
Sensitivity & Uncertainty Analysis, Volume 1: Theory
Dan G. Cacuci · 2005
Earlier work this paper cites.
Elected in 100 milliseconds: Appearance-based trait inferences and voting
Christopher Y Olivola and Alexander Todorov · 2010
Earlier work this paper cites.
How to avoid a perfunctory sensitivity analysis
Andrea Saltelli and Paola Annoni · 2010
Earlier work this paper cites.
Face recognition performance: Role of demographic information
Brendan F Klare, Mark J Burge, Joshua C Klontz, Richard W Vorder Bruegge, and Anil K Jain · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Earlier work this paper cites.
Heterogeneity of long-history migration explains cultural differences in reports of emotional expressivity and the functions of smiles
Magdalena Rychlowska, Yuri Miyamoto, David Matsumoto, Ursula Hess, Eva Gilboa-Schechtman, Shanmukh Kamble, Hamdi Muluk, Takahiko Masuda, and Paula Marie Niedenthal · 2015
Earlier work this paper cites.
Wasserstein gan
Martin Arjovsky, Soumith Chintala, and Leon Bottou · 2017
Earlier work this paper cites.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
Cited alongside, same era.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Facial recognition technology: The need for public regulation and corporate responsibility
Brad Smith · 2018
Later among the works it cites.
Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements
Lisa Feldman Barrett, Ralph Adolphs, Stacy Marsella, Aleix M Martinez, and Seth D Pollak · 2019
Closest in time.
Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2019
Closest in time.
Counterfactual fairness in text classification through robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed Chi, and Alex Beutel · 2019
Closest in time.
Statement of clare garvie senior associate, center on privacy & technology at georgetown law before the u.s. house of representatives committee on oversight and reform
Clare Garvie · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Is that a boy or a girl? exploring a neural networks construction of gender., 2017
Kerry Rodden · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
Explaining explanations: An overview of interpretability of machine learning
Leilani Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
Cited alongside, same era.
Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach · 2018
Cited alongside, same era.
Eddie murphy and the dangers of counterfactual causal thinking about detecting racial discrimination
Issa Kohler-Hausmann · 2019
Closest in time.
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
Closest in time.
Toward a theory of race for fairness in machine learning
Emanual Moss · 2019
Closest in time.
Revealing hidden gender biases in competence impressions of faces
DongWon Oh, Elinor A Buck, and Alexander Todorov · 2019
Closest in time.
Name perturbation sensitivity for detecting unintended model biases
Vinodkumar Prabhakaran, Ben Hutchinson, and Margaret Mitchell · 2019
Closest in time.
Mitigating bias in algorithmic employment screening: Evaluating claims and practices
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy · 2019
Closest in time.
Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
Closest in time.
On recent research auditing commercial facial analysis technology
Concerned Researchers · 2019
Closest in time.
Not ready for takeoff: Face scans at airport departure gates
Harrison Rudolph, Laura Moy, and Alvaro M. Bedoya · 2019
Closest in time.
Predictive inequity in object detection
Benjamin Wilson, Judy Hoffman, and Jamie Morgenstern · 2019
Closest in time.