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Fairness and discrimination in recommendation and retrieval

Michael D Ekstrand, Robin Burke, Fernando Diaz
2019 Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19  
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts.  ...  This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on  ...  ACKNOWLEDGMENTS This tutorial is partially based on work supported by NSF grant IIS 17-51278.  ... 
doi:10.1145/3298689.3346964 dblp:conf/recsys/EkstrandBD19 fatcat:s2ing3wffjfxjmuq4a4ktwfgu4

Fair Graph Mining

Jian Kang, Hanghang Tong
2021 Proceedings of the 30th ACM International Conference on Information & Knowledge Management  
fair machine learning, and (2) algorithmic challenge on the dilemma of balancing model accuracy and fairness.  ...  This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and ( 2 ) identify the open challenges and future trends.  ...  on graphs -Connections between group fairness and individual fairness on graphs The related tutorial reviews intrinsic limitations of existing fairness notions in machine learning and sheds light on  ... 
doi:10.1145/3459637.3482030 fatcat:fzg6nb56cjcird7vfkcviqxtni

A Case Study of Integrating Fairness Visualization Tools in Machine Learning Education

Afra Mashhadi, Annuska Zolyomi, Jay Quedado
2022 CHI Conference on Human Factors in Computing Systems Extended Abstracts  
The findings of this study provide insights into the benefits, challenges, and opportunities of integrating fairness tools as part of machine learning education.  ...  As demonstrated by media attention and research, Artificial Intelligence systems are not adequately addressing issues of fairness and bias, and more education on these topics is needed in industry and  ...  METHODOLOGY 3.1 Definition of Fairness The vast majority of work to date on fairness in machine learning has focused on the task of batch classification.  ... 
doi:10.1145/3491101.3503568 fatcat:37cuan2ztzexpia2gul6gv3j44

Replicable Evaluation of Recommender Systems

Alan Said, Alejandro Bellogín
2015 Proceedings of the 9th ACM Conference on Recommender Systems  
del recurso Access to the published version may require subscription ABSTRACT Recommender systems research is by and large based on comparisons of recommendation algorithms' predictive accuracies: the  ...  Comparing the evaluation results of two recommendation approaches is however a difficult process as there are very many factors to be considered in the implementation of an algorithm, its evaluation, and  ...  Alan Said is a Machine Learning engineer at Recorded Future in Gothenburg, Sweden.  ... 
doi:10.1145/2792838.2792841 fatcat:dpcqiod6urfmdcri2rodukujee

Gender Fairness in Information Retrieval Systems

Amin Bigdeli, Negar Arabzadeh, Shirin SeyedSalehi, Morteza Zihayat, Ebrahim Bagheri
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
In this tutorial, we inform the audience of various studies that have systematically reported the presence of stereotypical gender biases in Information Retrieval (IR) systems.  ...  We further classify existing work on gender biases in IR systems as being related to (1) relevance judgement datasets, (2) structure of retrieval methods, and (3) representations learnt for queries and  ...  It also presents some concepts about fairness, diversity, and bias and the ways for creating a fairer retrieval system. (2) Fairness of Machine Learning in Recommender Systems by Yunqi Li, Yingqiang Ge  ... 
doi:10.1145/3477495.3532680 fatcat:b3wsgn6rzvhdzgjzzceiz64geu

Retrieval and Recommendation Systems at the Crossroads of Artificial Intelligence, Ethics, and Regulation

Markus Schedl, Emilia Gómez, Elisabeth Lex
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
of information retrieval and recommender systems.  ...  This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency of AI systems, tailored to the research fields  ...  With the ever increasing adoption ofmostly opaque -machine and deep learning technology in such systems, many ethical questions about their use have emerged.  ... 
doi:10.1145/3477495.3532683 fatcat:dmq7dj4s35h6jjlfq3vgl3qrhi

Fairness, Accountability and Transparency in Music Information Research (FAT-MIR) [article]

Emilia Gomez, Andre Holzapfel, Marius Miron, Bob L. Sturm
2019 Zenodo  
This tutorial focuses on the timely issues of ethics, fairness, accountability and transparency, with particular attention paid to research in applications in music information research.  ...  Machine Learning workshops, and in the context of the HUMAINT project and winter school on ethical, legal, social and economic impact of Artificial Intelligence (  ...  Often no consideration of potential negative impacts on society or interactions with other systems in real use cases (Huff, 2003) .  ... 
doi:10.5281/zenodo.3546227 fatcat:g7si4auptber7badmlwoz57kvm

The ever evolving online labor market

Sihem Amer-Yahia, Senjuti Basu Roy
2019 Proceedings of the VLDB Endowment  
that is interdisciplinary in nature and requires convergence of different research disciplines.  ...  We will discuss how such a framework could bring transformative effect on the nexus of humans, technology, and the future of work.  ...  Acknowledgement The work of Senjuti Basu Roy is supported by National Science Foundation, Grant No. 1814595 and Office of Naval Research, grant no: N000141812838.  ... 
doi:10.14778/3352063.3352114 fatcat:bc2nuyjvqnfphjzprusypdfjly

International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020) [chapter]

Ludovico Boratto, Mirko Marras, Stefano Faralli, Giovanni Stilo
2020 Lecture Notes in Computer Science  
Both search and recommendation algorithms provide results based on their relevance for the current user.  ...  In order to do so, such a relevance is usually computed by models trained on historical data, which is biased in most cases.  ...  His research interests focus on algorithmic bias in machine learning for educational platforms, specifically in the context of semantic-aware systems, recommender systems, biometric systems, and opinion  ... 
doi:10.1007/978-3-030-45442-5_84 fatcat:d3qzkywr5zhsfpfrfy24vcrwru

2020 ESIP Summer Meeting Highlights [article]

Megan Carter, Erin Robinson, Annie Burgess, Susan Shingledecker, Edmund Molder, Tom Parris, Christopher Lynnes, Renée F. Brown, Leslie Hsu, Bill Teng, Nancy Hoebelheinrich, Ben Roberts-Pierel (+20 others)
This presentation, originally given during a webinar on August 13th, 2020, provides an overview of plenary and breakout sessions from the 2020 Earth Science Information Partners (ESIP) Summer Meeting held  ...  Tags: Machine Learning, Tutorials, Learning & training Takeaways: • We need more Earth science oriented tutorials for machine learning; • Plan to expand ESIP machine learning tutorials through  ...  by #ESIPfed Machine Learning Tutorials Introducing the development of interactive machine learning tutorials for Earth sciences supported by 2019 FUNding Friday.  ... 
doi:10.6084/m9.figshare.12804821.v2 fatcat:gbz3urw3lfhjlhc42sp4zv6izi

Intelligent Signal Processing for Affective Computing [From the Guest Editors]

Bjorn W. Schuller, Rosalind Picard, Elisabeth Andre, Jonathan Gratch, Jianhua Tao
2021 IEEE Signal Processing Magazine  
A major breakthrough in the field-as has been the case in many related pattern-recognition problems-came with the advent and increasing usage of deep learning and other novel techniques in machine learning  ...  Broader than the previous view on these crucial aspects, it considers a more general machine learning flow.  ...  She is the editor-in-chief of IEEE Transactions on Affective Computing. She is a Fellow of IEEE. Jon a t h a n G r a t ch  ... 
doi:10.1109/msp.2021.3096415 fatcat:ny735iki4ncg7hodtcr7vhtmle

Join the Living Lab: Evaluating News Recommendations in Real-Time [chapter]

Frank Hopfgartner, Torben Brodt
2015 Lecture Notes in Computer Science  
Participants of this tutorial learnt how to participate in CLEF NEWSREEL, a living lab for the evaluation of news recommender algorithms.  ...  Satisfying information needs by techniques including preference elicitation, pattern recognition, and prediction, recommender systems connect the research areas information retrieval and machine learning  ...  Format of the Tutorial The tutorial touched on two main research areas: (1) The development of webbased recommendation algorithms and (2) the evaluation of these techniques in real-time using real users  ... 
doi:10.1007/978-3-319-16354-3_95 fatcat:joeelkeqmzfivnqokrqg2ytstu

The International Society of Computational Biology presents: the Great Lakes Bioinformatics Conference, May 16-18, 2014, Cincinnati, Ohio

J. Cavalcoli, L. Welch, B. Aronow, S. Draghici, D. Kihara
2013 Bioinformatics  
Conference educational session topics will include Algorithm Development and Machine Learning, Bioimage Analysis, Biological Networks, Chemical Biology, Disease Models and Molecular Medicine, Evolutionary  ...  Of the 250 attendees of GLBIO 2013, 70% of conference attendees said they would attend the 2014 conference and 96% stated they would recommend the GLBIO conference to a colleague.  ... 
doi:10.1093/bioinformatics/btt673 fatcat:bfrtmdcdxbetdorla6wiwi2m6i

Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing

Kevin M. Mendez, Leighton Pritchard, Stacey N. Reinke, David I. Broadhurst
2019 Metabolomics  
A lack of transparency and reporting standards in the scientific community has led to increasing and widespread concerns relating to reproduction and integrity of results.  ...  To enable FAIR data science in metabolomics, methods and results need to be transparently disseminated in a manner that is rapid, reusable, and fully integrated with the published work.  ...  KMM & DIB developed the tutorial Jupyter notebook code. LP and SNR edited the learning tutorials to align to current pedagogical best practices. KMM wrote the initial draft of the review section.  ... 
doi:10.1007/s11306-019-1588-0 pmid:31522294 pmcid:PMC6745024 fatcat:riwra7xbxzav7cb7nz44kanpgi

What Should We Teach in Information Retrieval?

Ilya Markov, Maarten de Rijke
2019 SIGIR Forum  
Modern Information Retrieval (IR) systems, such as search engines, recommender systems, and conversational agents, are best thought of as interactive systems.  ...  We notice that current teaching materials in IR focus mostly on search and on the offline development phase.  ...  All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.  ... 
doi:10.1145/3308774.3308780 fatcat:ixkmjqqmsjeavictyumi7xkxsu
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