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An Empirical Analysis on Transparent Algorithmic Exploration in Recommender Systems
[article]
2021
arXiv
pre-print
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To mitigate this risk, recommender systems have mixed items chosen for exploration into a recommendation list, disguising the items as recommendations to elicit feedback on the items to discover the user's ...
Our work paves the way for how to design an interface, which utilizes learning algorithm based on users' feedback signals, giving better user experience and gathering more feedback data. ...
Specifically, revealed exploration not only got higher scores in novelty, diversity, transparency, trust and satisfaction in a user-centric evaluation, but also gathered more implicit feedback on the exploratory ...
arXiv:2108.00151v2
fatcat:copt7dvm55acjaysb2uullzexa
Explanations as Discourse
2020
Australasian Journal of Information Systems
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In this paper, we explore the use of explanations in big data analytics services. ...
We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement ...
Australasian Journal of Information Systems Afrashteh, Someh & Davern 2020, Vol 24, Selected Papers from the IS Foundations Conference Explanations as Discourse ...
doi:10.3127/ajis.v24i0.2519
fatcat:mwlvhcnsofbkvoemwm522otbhu
Trust and Trustworthiness in Social Recommender Systems
[article]
2019
arXiv
pre-print
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recommender systems. ...
The prevalence of misinformation on online social media has tangible empirical connections to increasing political polarization and partisan antipathy in the United States. ...
CONCLUSION Disruption in recommender systems can take one of many forms. ...
arXiv:1903.01780v1
fatcat:qchpnqpbrzevxmjfgm3bdpee2e
The Explanatory Gap in Algorithmic News Curation
[chapter]
2021
Lecture Notes in Computer Science
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2021 pre-print arXiv:2109.15224v1 fatcat:tzjne424evavxcbytjbfpnsbfi
We call this the Explanatory Gap in Machine Learning-based Curation Systems. ...
This paper examines how well different explanations help expert users understand why certain news stories are recommended to them. ...
Despite a large consensus that explanations are helpful and that algorithmic transparency is important [47, 15, 8] , the amount of empirical research that investigates explanations of curation systems ...
doi:10.1007/978-3-030-87031-7_1
fatcat:3l7zc6pvf5hszbjyw7kgrwcrgu
Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective
2012
ACM transactions on interactive intelligent systems (TiiS)
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, but each with a different recommender algorithm. ...
This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. ...
A special mention goes to Sara Negro and Alessandro Papadopoulos for their support in the statistical analysis of the empirical data. ...
doi:10.1145/2209310.2209314
fatcat:mcihbl6a5bdj7bw72xhdltqqce
Amplifying Domain Expertise in Clinical Data Pipelines
2020
JMIR Medical Informatics
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There should be an increased emphasis on the system to optimize the experts' interaction by directing them to high-impact data tasks and reducing the total task completion time. ...
However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. ...
Acknowledgments The research reported in this paper was supported by the National Institute of Allergy and Infectious Diseases of National Institute of Health (NIH) under R01AI116975. ...
doi:10.2196/19612
pmid:33151150
fatcat:3wng2jtq45g75jf2x6yxf4mgyi
Towards Value-Sensitive Learning Analytics Design
2019
Proceedings of the 9th International Conference on Learning Analytics & Knowledge - LAK19
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2018 pre-print arXiv:1812.08335v1 fatcat:tcvz375mmvcfrd66vfyud35hnmOther Versions
By reporting on these two cases, this paper responds to a need of practical means to support ethical considerations and human values in learning analytics systems. ...
The first study applied two methods of Value Sensitive Design, namely stakeholder analysis and value analysis, to a conceptual investigation of an existing learning analytics tool. ...
creation of an algorithm system. ...
doi:10.1145/3303772.3303798
dblp:conf/lak/ChenZ19
fatcat:xq2ckrwp4jhqbiq5atvu43g2xu
Short-Term Satisfaction and Long-Term Coverage
2018
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18
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Our findings provide insights into how to design presentation strategies for algorithmic exploration in interactive recommender systems, mitigating the short-term costs of algorithmic exploration while ...
To enable discovery, a machine learning algorithm typically elicits feedback on items it is uncertain about, which is termed algorithmic exploration in machine learning. ...
ACKNOWLEDGMENTS This work was supported in part through NSF Awards IIS-1513692, IIS-1615706, and a gift from Bloomberg. We thank Pantelis P. ...
doi:10.1145/3159652.3159700
dblp:conf/wsdm/SchnabelBDJ18
fatcat:uuebw4u7m5dghcxoxit3zldoqu
Progressive Disclosure: Designing for Effective Transparency
[article]
2018
arXiv
pre-print
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Prior work has often taken a technocentric approach to transparency. In contrast, we explore empirical user-centric methods to better understand user reactions to transparent systems. ...
Study 2 explored these effects in depth, suggesting that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system ...
In contrast, here we take an empirical user-centric approach to better understand how to design transparent systems. ...
arXiv:1811.02164v1
fatcat:qxs5znpl4fhibmlcmoxsvo64jq
Effects of transparency on pilot trust and agreement in the autonomous constrained flight planner
2016
2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC)
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of transparency in aiding reliance on automated recommendations. ...
We performed a human-in-the-loop study to explore the role of transparency in engendering trust and reliance within highly automated systems. ...
Support for this work was jointly provided by the NASA Safe Autonomous Systems Operation Program (SASO) and the United States Air Force Research Laboratory, through an interagency agreement (NASA SAA2- ...
doi:10.1109/dasc.2016.7777998
fatcat:yda5llgp2jdyfn7hyahhxda2be
Diversity, Fairness, and Data-Driven Personalization in (News) Recommender System (Dagstuhl Perspectives Workshop 19482)
2020
Dagstuhl Reports
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As people increasingly rely on online media and recommender systems to consume information, engage in debates and form their political opinions, the design goals of online media and news recommenders have ...
future research and long-term goals for the emerging topic of fairness, diversity, and personalization in recommender systems. ...
URL https://doi.org/10.1080/21670811.2019.1682938 Main reference Judith Möller, Damian Trilling, Natali Helberger, Bram van Es: "Do not blame it on the algorithm: an empirical assessment of multiple recommender ...
doi:10.4230/dagrep.9.11.117
dblp:journals/dagstuhl-reports/BernsteinVHSZ19
fatcat:5asfiil7ine57pgxecdicrr4ja
Influences of Transparency and Feedback on Customer Intention to Reuse Online Recommender Systems
온라인 추천시스템에서 고객 사용의도를 위한 시스템 투명성과 피드백의 영향
2013
The Journal of Society for e-Business Studies
온라인 추천시스템에서 고객 사용의도를 위한 시스템 투명성과 피드백의 영향
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However, e-commerce has been able to create a technological proxy for the social filtering process, known as online recommender systems (RSs). ...
However, most previous research on RSs has focused on the accuracy of the algorithms, with little emphasis on user interface and perspectives. ...
The researcch model was presented in our previous paper [25] without an empirical result [26] and this paper includes our empirical analysis based on the research model. ...
doi:10.7838/jsebs.2013.18.2.279
fatcat:74d7obgiq5covmcliggh4uypoe
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns
2019
Big Data & Society
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We explore what this requirement entails for artificial intelligence and automated decision-making systems. ...
This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking. ...
The authors are listed in alphabetical order and have contributed equally to this article ...
doi:10.1177/2053951719860542
fatcat:ckdhn6jj3zdntpfhcpglp75yee
Recommender systems and the amplification of extremist content
2021
Internet Policy Review
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In this article, we make two contributions to this debate. Firstly, we provide a novel empirical analysis of three platforms' recommendation systems when interacting with far-right content. ...
There are currently few policy instruments for dealing with algorithmic amplification, and those that do exist largely focus on transparency. ...
We contribute to this debate in two ways: firstly, we conduct an empirical analysis of interactions of recommendation systems and far-right content on three platforms-YouTube, Reddit, and Gab. ...
doi:10.14763/2021.2.1565
fatcat:ljx4odbad5ddxeuzyeu5lgr4ju
Algorithmic Trading Systems: A Multifaceted View of Adoption
2012
2012 45th Hawaii International Conference on System Sciences
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Algorithmic trading has been blamed for an increasing level of volatility in a number of financial markets. ...
This paper explores this growing arena by engaging with senior practitioners in the industry and using interviews and grounded theory (GT) analysis to uncover their adoption concerns. ...
Recommendations for action One issue that was repeatedly mentioned is that the development and deployment of algorithmic trading systems often lacks an adequate risk management framework that supports ...
doi:10.1109/hicss.2012.93
dblp:conf/hicss/BellG12
fatcat:d54pozs2hnepvizvmd4wmibn7e
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