research-article

What Makes an Image Tagger Fair?

Authors:

Styliani Kleanthous,

Kyriakos Kyriakou,

Jahna OtterbacherAuthors Info & Claims

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Pages 95 - 103

https://doi.org/10.1145/3320435.3320442

Published: 07 June 2019 Publication History

Abstract

Image analysis algorithms have been a boon to personalization in digital systems and are now widely available via easy-to-use APIs. However, it is important to ensure that they behave fairly in applications that involve processing images of people, such as dating apps. We conduct an experiment to shed light on the factors influencing the perception of "fairness." Participants are shown a photo along with two descriptions (human- and algorithm-generated). They are then asked to indicate which is "more fair" in the context of a dating site, and explain their reasoning. We vary a number of factors, including the gender, race and attractiveness of the person in the photo. While participants generally found human-generated tags to be more fair, API tags were judged as being more fair in one setting - where the image depicted an "attractive," white individual. In their explanations, participants often mention accuracy, as well as the objectivity/subjectivity of the tags in the description. We relate our work to the ongoing conversation about fairness in opaque tools like image tagging APIs, and their potential to result in harm.

References

[1]

Oscar Alvarado and Annika Waern. 2018. Towards Algorithmic Experience: Initial Efforts for Social Media Contexts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 286.

Digital Library

[2]

Arthur Aron, Gary W Lewandowski Jr, Debra Mashek, and Elaine N Aron. 2013. The self-expansion model of motivation and cognition in close relationships. The Oxford handbook of close relationships (2013), 90--115.

[3]

Pinar Barlas, Kyriakos Kyriakou, Styliani Kleanthous, and Jahna Otterbacher. 2019. Social B(eye)as: Human and Machine Descriptions of People Images. In Proceedings of the 13th Annual Conference on Web and Social Media (ICWSM '19). AAAI.

[4]

Reuben Binns. 2018. Fairness in Machine Learning: Lessons from Political Philosophy. In Conference on Fairness, Accountability and Transparency. 149--159.

[5]

Rebecca J Brand, Abigail Bonatsos, Rebecca D'Orazio, and Hilary DeShong. 2012. What is beautiful is good, even online: Correlations between photo attractiveness and text attractiveness in men's online dating profiles. Computers in Human Behavior, Vol. 28, 1 (2012), 166--170.

Digital Library

[6]

Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91.

[7]

Trudy Hui Hui Chua and Leanne Chang. 2016. Follow me and like my beautiful selfies: Singapore teenage girls' engagement in self-presentation and peer comparison on social media. Computers in Human Behavior, Vol. 55 (2016), 190--197.

Digital Library

[8]

Michael A DeVito, Jeremy Birnholtz, Jeffery T Hancock, Megan French, and Sunny Liu. 2018. How People Form Folk Theories of Social Media Feeds and What It Means for How We Study Self-Presentation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 120.

Digital Library

[9]

Nicholas Diakopoulos. 2016. Accountability in algorithmic decision making. Commun. ACM, Vol. 59, 2 (2016), 56--62.

Digital Library

[10]

Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. I always assumed that I wasn't really that close to {her}: Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, 153--162.

Digital Library

[11]

Tarleton Gillespie. 2014. The relevance of algorithms. Media technologies: Essays on communication, materiality, and society, Vol. 167 (2014).

[12]

Matthew Kay, Cynthia Matuszek, and Sean A Munson. 2015. Unequal representation and gender stereotypes in image search results for occupations. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 3819--3828.

Digital Library

[13]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.

Digital Library

[14]

Debbie S Ma, Joshua Correll, and Bernd Wittenbrink. 2015. The Chicago face database: A free stimulus set of faces and norming data. Behavior research methods, Vol. 47, 4 (2015), 1122--1135.

[15]

Evelyn P Meier and James Gray. 2014. Facebook photo activity associated with body image disturbance in adolescent girls. Cyberpsychology, Behavior, and Social Networking, Vol. 17, 4 (2014), 199--206.

[16]

Cathy O'Neil. 2017. Weapons of math destruction: How big data increases inequality and threatens democracy .Broadway Books.

Digital Library

[17]

Luiz Pizzato, Thomas Chung, Tomek Rej, Irena Koprinska, Kalina Yacef, and Judy Kay. September 2010 a. Learning user preference in online dating. http://www.ke.tu-darmstadt.de/events/ PL-10/papers/8-Pizzato.pdf In: Hüllermeier, E., Fürnkranz, J. (eds.), Proceedings of the Preference Learning (PL-10) Tutorial and Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). http://www.ke.tu-darmstadt.de/events/ PL-10/papers/8-Pizzato.pdf.

[18]

Luiz Pizzato, Tomasz Rej, Joshua Akehurst, Irena Koprinska, Kalina Yacef, and Judy Kay. 2013. Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, Vol. 23, 5 (01 Nov 2013), 447--488.

Digital Library

[19]

Luiz Pizzato, Tomek Rej, Thomas Chung, Irena Koprinska, Kalina Yacef, and Judy Kay. 2010b. Reciprocal Recommender System for Online Dating. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10). ACM, New York, NY, USA, 353--354.

Digital Library

[20]

Lauren Rhue. 2018. Racial Influence on Automated Perceptions of Emotions. Available at SSRN 3281765 (2018).

[21]

Carol D Ryff. 1989. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of personality and social psychology, Vol. 57, 6 (1989), 1069.

[22]

Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry (2014), 1--23.

[23]

Michael A Stefanone, Derek Lackaff, and Devan Rosen. 2011. Contingencies of self-worth and social-networking-site behavior. Cyberpsychology, Behavior, and Social Networking, Vol. 14, 1--2 (2011), 41--49.

[24]

Michele Wilson. 2017. Algorithms (and the) everyday. Information, Communication & Society, Vol. 20, 1 (2017), 137--150.

[25]

Peng Xia, Shuangfei Zhai, Benyuan Liu, Yizhou Sun, and Cindy Chen. 2016. Design of reciprocal recommendation systems for online dating. Social Network Analysis and Mining, Vol. 6, 1 (10 Jun 2016), 32.

[26]

Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. 2017. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing .

[27]

Yong Zheng, Tanaya Dave, Neha Mishra, and Harshit Kumar. 2018. Fairness In Reciprocal Recommendations: A Speed-Dating Study. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP '18). ACM, New York, NY, USA, 29--34.

Digital Library

Cited By

Pinney CRaj AHanna AEkstrand M(2023)Much Ado About GenderProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578316(269-279)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578316
Alexandropoulou CPanagiotopoulos IKleanthous SDimitrakopoulos GConstantinou IPoliti ENtalaperas DPapageorgiou XStylianides CIoannides NPalazis LPattichis CPanayides A(2023)AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254863(1-9)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254863
Narayanan DNagpal MMcGuire JSchweitzer SDe Cremer D(2023)Fairness Perceptions of Artificial Intelligence: A Review and Path ForwardInternational Journal of Human–Computer Interaction10.1080/10447318.2023.221089040:1(4-23)Online publication date: 26-May-2023
https://doi.org/10.1080/10447318.2023.2210890
Show More Cited By

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What Makes an Image Tagger Fair?

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cover image ACM Conferences

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

June 2019

377 pages

ISBN:9781450360210

DOI:10.1145/3320435

General Chairs:
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
George Samaras
University of Cyprus, Cyprus
,
Stephan Weibelzahl
PFH Private University of Applied Sciences Göttingen, Germany
,
Program Chairs:
Dietmar Jannach
Alpen-Adria-Universität Klagenfurt, Austria
,
Olga C. Santos
aDeNu Research Group - UNED, Spain

Copyright © 2019 ACM.

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Published: 07 June 2019

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Horizon 2020

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UMAP '19

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UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization

June 9 - 12, 2019

Larnaca, Cyprus

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UMAP '19 Paper Acceptance Rate 30 of 122 submissions, 25%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Pinney CRaj AHanna AEkstrand M(2023)Much Ado About GenderProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578316(269-279)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578316
Alexandropoulou CPanagiotopoulos IKleanthous SDimitrakopoulos GConstantinou IPoliti ENtalaperas DPapageorgiou XStylianides CIoannides NPalazis LPattichis CPanayides A(2023)AI-Enabled Solutions, Explainability and Ethical Concerns for Predicting Sepsis in ICUs: A Systematic Review2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254863(1-9)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254863
Narayanan DNagpal MMcGuire JSchweitzer SDe Cremer D(2023)Fairness Perceptions of Artificial Intelligence: A Review and Path ForwardInternational Journal of Human–Computer Interaction10.1080/10447318.2023.221089040:1(4-23)Online publication date: 26-May-2023
https://doi.org/10.1080/10447318.2023.2210890
Orphanou KOtterbacher JKleanthous SBatsuren KGiunchiglia FBogina VTal AHartman AKuflik T(2022)Mitigating Bias in Algorithmic Systems—A Fish-eye ViewACM Computing Surveys10.1145/352715255:5(1-37)Online publication date: 3-Dec-2022
https://dl.acm.org/doi/10.1145/3527152
Oosterhuis H(2021)Learning from user interactions with rankingsACM SIGIR Forum10.1145/3483382.348340254:2(1-2)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3483382.3483402
Li D(2021)Effective collection construction for information retrieval evaluation and optimizationACM SIGIR Forum10.1145/3483382.348340154:2(1-2)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3483382.3483401
Roitero K(2021)Cheap IR evaluationACM SIGIR Forum10.1145/3483382.348340054:2(1-2)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3483382.3483400
Bauer C(2021)Report on the ISMIR 2020 special sessionACM SIGIR Forum10.1145/3483382.348339854:2(1-7)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3483382.3483398
Safavi TMeij EÖzcan FRedi MDemartini GXiong C(2021)Report on the first workshop on bias in automatic knowledge graph construction at AKBC 2020ACM SIGIR Forum10.1145/3483382.348339354:2(1-9)Online publication date: 20-Aug-2021
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Landoni MPera MFails JMurgia EKucirkova NHuibers T(2021)4 KidRec - what does good look likeACM SIGIR Forum10.1145/3483382.348339154:2(1-7)Online publication date: 20-Aug-2021
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