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Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors [article]

Hong Shen, Alicia DeVos, Motahhare Eslami, Kenneth Holstein
2021 arXiv   pre-print
A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors.  ...  We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors.  ...  ACKNOWLEDGMENTS This work was supported by the National Science Foundation (NSF) program on Fairness in AI in collaboration with Amazon under Award No.  ... 
arXiv:2105.02980v1 fatcat:ixm6ry5kr5ezjogxmvkvoyqdfq

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media [article]

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios
2017 arXiv   pre-print
In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter.  ...  However, bias does not emerge from an algorithm alone.  ...  Auditing Black Boxes Recently, the rise of algorithmic platforms' influence on users' online experience has motivated many studies to audit these platforms and understand their biases.  ... 
arXiv:1704.01347v1 fatcat:f7oaysnuqjhxnnq67oczypwcd4

Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [article]

Basileal Imana, Aleksandra Korolova, John Heidemann
2022 arXiv   pre-print
But auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms, and thus far there has been no concrete technical proposal that can provide such auditing.  ...  Legislations have been proposed in both the U.S. and the E.U. that mandate auditing of social media algorithms by external researchers.  ...  In 2021, Twitter held an algorithmic bias bounty challenge, through which Twitter made the code for their image salience algorithm suspected of bias [88] available to researchers [14] .  ... 
arXiv:2207.08773v1 fatcat:wbtwwqnii5dvzcamk7tfyew6tu

Understanding and Designing around Users' Interaction with Hidden Algorithms in Sociotechnical Systems

Motahhare Eslami
2017 Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW '17 Companion  
The political bias of search results on Twitter Search, regardless of the candidates political leaning, was mostly democratic.  ...  To detect and quantify such biases, we collected thousands of search results for the names of 17 candidates of the 2016 US presidential election on Twitter Search in December 2015 during a week in which  ... 
doi:10.1145/3022198.3024947 fatcat:ldp3sgdmljgcddse27emlvy2zi

Search bias quantification: investigating political bias in social media and web search

Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, Karrie Karahalios
2018 Information retrieval (Boston)  
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities.  ...  In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away  ...  Search engines are an important set of algorithms that users interact with on daily basis and these algorithms' susceptibility to bias has resulted in several audit studies in recent years.  ... 
doi:10.1007/s10791-018-9341-2 fatcat:st7rnexr3nby5jboqnry6xhxp4

Algorithmic amplification of politics on Twitter

Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew Schlaikjer, Moritz Hardt
2021 Proceedings of the National Academy of Sciences of the United States of America  
Content on Twitter's home timeline is selected and ordered by personalization algorithms.  ...  We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts  ...  study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content.  ... 
doi:10.1073/pnas.2025334119 pmid:34934011 pmcid:PMC8740571 fatcat:ng76ttqo7rcbpc6ycqe2peq5fa

An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring [article]

Alene K. Rhea, Kelsey Markey, Lauren D'Arinzo, Hilke Schellmann, Mona Sloane, Paul Squires, Falaah Arif Kahn, Julia Stoyanovich
2022 arXiv   pre-print
Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social  ...  Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems.  ...  Acknowledgements We thank Dhara Mungra for her work on data collection and preliminary analysis, and Daphna Harel and Joshua Loftus for their advice on statistical methods.  ... 
arXiv:2201.09151v2 fatcat:g2dxwjmefjadfhy2sj7thla62i

YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations [article]

Muhammad Haroon, Anshuman Chhabra, Xin Liu, Prasant Mohapatra, Zubair Shafiq, Magdalena Wojcieszak
2022 arXiv   pre-print
Despite these concerns, prior evidence on this algorithmic radicalization is inconsistent.  ...  Recommendations algorithms of social media platforms are often criticized for placing users in "rabbit holes" of (increasingly) ideologically biased content.  ...  Some of these biases are especially pronounced for right-leaning users. Based on the audit, we investigate principled interventions to mitigate bias in YouTube's recommendations.  ... 
arXiv:2203.10666v2 fatcat:3qckzrxilvhzxj7hwargrebpja

Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner-Tal, Alan Hartman, Tsvi Kuflik
2021 Zenodo  
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences.  ...  Given the complexity of the problem and the involvement of multiple stakeholders – including developers, end-users and third-parties – there is a need to understand the landscape of the sources of bias  ...  [118] propose an auditing technique where queries are submitted on Twitter, to measure bias on Twitter results as compared to search engines.  ... 
doi:10.5281/zenodo.6240582 fatcat:vftoi4woebhrrp5tlmkclabgf4

Toward User-Driven Algorithm Auditing: Investigating users' strategies for uncovering harmful algorithmic behavior

Alicia DeVos, Aditi Dhabalia, Hong Shen, Kenneth Holstein, Motahhare Eslami
2022 CHI Conference on Human Factors in Computing Systems  
invaluable in user-driven algorithm audits.  ...  Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic behaviors that formal auditing approaches fail to detect.  ...  ACKNOWLEDGMENTS This work was supported by the National Science Foundation (NSF) program on Fairness in AI in collaboration with Amazon under Award No.  ... 
doi:10.1145/3491102.3517441 fatcat:obypclcgqzfwjjnst5rwgs24z4

Making Transparency Clear: The Dual Importance of Explainability and Auditability

Aaron Springer, Steve Whittaker
2019 International Conference on Intelligent User Interfaces  
Auditability is more exhaustive; providing third-parties with the ability to test algorithmic outputs and diagnose biases and unfairness.  ...  On the other hand, scaffolding user mental models with selective transparency will not provide enough information to audit these systems for fairness.  ...  ACKNOWLEDGMENTS We would like to thank Victoria Hollis, Ryan Compton, and Lee Taber for their feedback on this project.  ... 
dblp:conf/iui/SpringerW19a fatcat:twzldrq2i5fezn6mqqfgr2qul4

Mitigating Bias in Algorithmic Systems - A Fish-Eye View

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, BATSUREN KHUYAGBAATAR, Fausto GIUNCHIGLIA, Veronika Bogina, Avital Shulner Tal, Alan Hartman, Tsvi Kuflik
2022 Zenodo  
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences.  ...  Given the complexity of the problem and the involvement of multiple stakeholders – including developers, end users and third-parties – there is a need to understand the landscape of the sources of bias  ...  [116] propose an auditing technique where queries are submitted on Twitter, to measure bias on Twitter results as compared to search engines.  ... 
doi:10.5281/zenodo.6782985 fatcat:oc6qovumv5eszl5ukns4l3t6d4

Mitigating Bias in Algorithmic Systems – A Fish-Eye View [article]

Kalia Orphanou, Jahna Otterbacher, Styliani Kleanthous, Khuyagbaatar Batsuren, Fausto Giunchiglia, Veronika Bogina, Avital Shulner Tal, AlanHartman, Tsvi Kuflik
2022 arXiv   pre-print
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences.  ...  Given the complexity of the problem and the involvement of multiple stakeholders -- including developers, end-users, and third parties -- there is a need to understand the landscape of the sources of bias  ...  [82] propose an auditing technique where queries are submitted on Twitter, to measure bias on Twitter results as compared to search engines.  ... 
arXiv:2103.16953v2 fatcat:b27zb3zusnfmzcspyl2njbivkq

The Effect of Population and "Structural" Biases on Social Media-based Algorithms

Isaac Johnson, Connor McMahon, Johannes Schöning, Brent Hecht
2017 Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI '17  
However, very little is known about how these population biases affect the many algorithms that rely on social media data.  ...  However, we also show that some of this bias can be attributed to the design of algorithms themselves rather than population biases in the underlying data sources.  ...  Finally, it is important to note before continuing that while we find important biases that are robust across two separate algorithms, this is a case study on Twitter geolocation inference rather than  ... 
doi:10.1145/3025453.3026015 dblp:conf/chi/JohnsonMSH17 fatcat:i75xjayynbdljcdnjpcxbifzzm

Algorithmic Amplification of Politics on Twitter [article]

Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien, Luca Belli, Andrew Schlaikjer, Moritz Hardt
2021 arXiv   pre-print
Content on Twitter's home timeline is selected and ordered by personalization algorithms.  ...  We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2M daily active accounts to  ...  SI 1.3 Media bias ratings SI 1.3.1 AllSides Media Bias Ratings To study exposure to politically biased media sources we obtained media bias ratings for news sources from AllSides [1] .  ... 
arXiv:2110.11010v1 fatcat:wweygf6acjb7jfnhgui3hbssay
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