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Fairness and discrimination in recommendation and retrieval
2019
Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19
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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
2021
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
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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
2022
CHI Conference on Human Factors in Computing Systems Extended Abstracts
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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
2015
Proceedings of the 9th ACM Conference on Recommender Systems
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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
2022
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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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
2022
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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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]
2019
Zenodo
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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 (https://ec.europa.eu/jrc/communities/en ...
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
2019
Proceedings of the VLDB Endowment
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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]
2020
Lecture Notes in Computer Science
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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]
2020
figshare.com
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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
www.esipfed.org
#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]
2021
IEEE Signal Processing Magazine
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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]
2015
Lecture Notes in Computer Science
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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
2013
Bioinformatics
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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
2019
Metabolomics
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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?
2019
SIGIR Forum
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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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