105,005 Hits in 7.8 sec

Working Towards Understanding the Role of FAIR for Machine Learning

Daniel S. Katz, Fotis E. Psomopoulos, Leyla Jael Castro
2021 Zenodo  
Here we discuss some of the elements of machine learning that lead to the need for some adaptation of the original FAIR principles, along with stakeholders that would benefit from this adaptation.  ...  In this position paper we argue that the FAIR principles also can apply to machine learning tools and models, though a direct application is not always possible as machine learning combines aspects of  ...  • FAIR for Machine Learning Models (June 2021), FAIR Festival • 1st Community call July 21 2021 • BoF at RDA VP18: "Steps towards defining FAIR principles for Machine Learning (ML)" ○ Discuss status and  ... 
doi:10.5281/zenodo.5594990 fatcat:it5xrpjwebfi7jf7cqhbztkrlu

Teaching Responsible Machine Learning to Engineers

Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy
2021 Teaching Machine Learning Workshop  
In this paper, we reflect upon the development of a course on responsible machine learning for undergraduate engineering students.  ...  Moving forward, we call upon educators to focus on the development of realistic case studies that invite students to interrogate the role of an engineer.  ...  In teams of five, students went through all stages of the machine learning development process (except for deployment) and implemented techniques for enhancing fairness and explainability (learning objective  ... 
dblp:conf/teachml/WeertsP21 fatcat:mpbz36udfvctnnrqwdj4on32cy

The FATE System: FAir, Transparent and Explainable Decision Making

Joachim de Greeff, Maaike H. T. de Boer, Fieke H. J. Hillerström, Freek Bomhof, Wiard Jorritsma, Mark A. Neerincx
2021 AAAI Spring Symposia  
The goal of the FATE system is decision support with use of ongoing human-AI co-learning, explainable AI and fair, bias-free and secure usage of data.  ...  These topics are societally very relevant for the update of AI-based support systems, but the manner in which to bring these together into a working system is far from trivial.  ...  Acknowledgements The FATE project is funded by the TNO Appl.AI program (internal AI program).  ... 
dblp:conf/aaaiss/GreeffBHBJN21 fatcat:2akenyicfbfmfcsrd6grxljnoa

Investigating Explanations that Target Training Data

Ariful Islam Anik, Andrea Bunt
2021 International Conference on Intelligent User Interfaces  
However, training dataset information is rarely communicated in these explanations despite the utmost importance of training data to a system trained with machine learning techniques.  ...  To promote transparency in black-box machine learning systems, different explanation approaches have been developed and discussed in the literature.  ...  One of the goals for explanations, in general, is to ensure fairness in machine learning systems by revealing more details about the systems and their decision process.  ... 
dblp:conf/iui/AnikB21 fatcat:2x2zun7n5vhe3jrrm6gjzmg6a4

Towards Generating Consumer Labels for Machine Learning Models

Christin Seifert, Stefanie Scherzinger, Lena Wiese
2019 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)  
These labels primarily target machine learning lay persons, such as the operators of an ML system, the executors of decisions, and the decision subjects themselves.  ...  Machine learning (ML) based decision making is becoming commonplace.  ...  Different from [3] , where explainability is tailored towards all roles in a machine learning ecosystem (cf. Figure 2 ), we specifically focus on the consumers of ML-based recommendations.  ... 
doi:10.1109/cogmi48466.2019.00033 dblp:conf/cogmi/SeifertSW19 fatcat:lochyypdafhyflvcdckzfmxdzm

Development Practices of Trusted AI Systems among Canadian Data Scientists

Jinnie Shin, Okan Bulut, Mark J. Gierl
2020 International Review of Information Ethics  
Developing trusted AI systems requires careful consideration and evaluation of its reproducibility, interpretability, and fairness, which in in turn, poses increased expectations and responsibilities for  ...  The introduction of Artificial Intelligence (AI) systems has demonstrated impeccable potential and benefits to enhance the decision-making processes in our society.  ...  ), whether their employer implements machine learning at work, and the total number of roles they have at work as a data scientist, whether they use Jupyter or IPhython to compile their work, the number  ... 
doi:10.29173/irie377 fatcat:d3sveua3fna4lej3wujdqczeii

Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness [article]

Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis
2021 arXiv   pre-print
Although both machine learning and mechanism design have developed frameworks for addressing issues of fairness and equity, in some complex decision-making systems, neither framework is individually sufficient  ...  We begin to lay the ground work towards this goal by comparing the perspective each discipline takes on fair decision-making, teasing out the lessons each field has taught and can teach the other, and  ...  This project has been part of the MD4SG working group on Bias, Discrimination, and Fairness.  ... 
arXiv:2010.05434v2 fatcat:d6dl3wgsf5fkrkirgpz7a5ul4q

Toward a Bias-Aware Future for Mixed-Initiative Visual Analytics [article]

Adam Coscia
2020 arXiv   pre-print
We then suggest informed opportunities for domain experts to take initiative toward addressing cognitive biases in light of their existing contributions to the field.  ...  As these systems consider whether to take initiative towards achieving user goals, many current systems address the potential for cognitive bias in human initiatives statically, relying on fixed initiatives  ...  As mixed-initiative VA research balances human and machine initiatives towards this role of guidance, another role is being investigated: one that concurrently considers the effects of cognitive biases  ... 
arXiv:2011.09988v1 fatcat:ttuendzirzcgnk5zquazdpjcxu

The Next Decade of Data Science

Justin D. Weisz, Michael J. Muller
2020 Knowledge Discovery and Data Mining  
We present a vision of how the practice of data science will evolve over the next decade due to advancements in automated machine learning (known as AutoML or AutoAI).  ...  Based on our experiences in building and studying user interfaces for automated machine learning tools, we propose seven new specializations of the future data scientist: translators, label-wranglers,  ...  These roles are an outgrowth of our work examining current data science practices [17, 29] and adoption and use of automated machine learning technologies [6, [25] [26] [27] .  ... 
dblp:conf/kdd/WeiszM20 fatcat:i74l2wgqwbgzpj7tbqxg5v6jzq

You never fake alone. Creative AI in action

Katja de Vries
2020 Information, Communication & Society  
In order to conceptualize the societal role of creative AI a new conceptual toolbox is needed. The paper provides metaphors and concepts for understanding the functioning of creative AI.  ...  It shows how the role of creative AI in relation to FAT ideals can be enriched by a dynamic and constructivist understanding of creative AI.  ...  One clear example of a supportive role in relation to classificatory machines is that the generation of synthetic data can increase the learning process of such systems exponentially.  ... 
doi:10.1080/1369118x.2020.1754877 fatcat:lbfhogs7s5hrfderhfahoxbkdm

Data Science and Machine Learning in Education [article]

Gabriele Benelli, Thomas Y. Chen, Javier Duarte, Matthew Feickert, Matthew Graham, Lindsey Gray, Dan Hackett, Phil Harris, Shih-Chieh Hsu, Gregor Kasieczka, Elham E. Khoda, Matthias Komm (+11 others)
2022 arXiv   pre-print
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at  ...  the heart of HEP research.  ...  Role of Open Data and AI Models Adhering to FAIR Principles The FAIR principles (findable, accessible, interoperable, and reusable) were originally proposed to inspire scientific data management for reproducibility  ... 
arXiv:2207.09060v1 fatcat:winusbvpajdwjha2df2jbc3fbe

Bringing the People Back In: Contesting Benchmark Machine Learning Datasets [article]

Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, Morgan Klaus Scheuerman
2020 arXiv   pre-print
In this work, we outline a research program - a genealogy of machine learning data - for investigating how and why these datasets have been created, what and whose values influence the choices of data  ...  In response to algorithmic unfairness embedded in sociotechnical systems, significant attention has been focused on the contents of machine learning datasets which have revealed biases towards white, cisgender  ...  Finally, this project points towards understanding the role of interrogating the invisible and undervalued labor plays in that goes into the construction development of datasets which amount -as we will  ... 
arXiv:2007.07399v1 fatcat:r7ajjzji7bg73i33ydn5qmdv3m

Algorithmic Fairness from a Non-ideal Perspective [article]

Sina Fazelpour, Zachary C. Lipton
2020 arXiv   pre-print
fair machine learning algorithms reflect broader troubles faced by the ideal approach.  ...  In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed  ...  Funding was provided by Social Sciences and Humanities Research Council of Canada (No. 756-2019-0289) and the AI Ethics and Governance Fund.  ... 
arXiv:2001.09773v1 fatcat:df65zfcrl5dqzjflqpqkyd57vq

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 (+19 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  ...  Machine Learning Tutorials Introducing the development of interactive machine learning tutorials for Earth sciences supported by 2019 FUNding Friday.  ...  Machine Learning Engaging with different organizations to discuss how ESIP community can support/promote the adoption of machine learning for all Earth science domains.  ... 
doi:10.6084/m9.figshare.12804821.v1 fatcat:kiaou6ajabag7eeqqnbfa3sbsi

Machine Learning in Population and Public Health [article]

Vishwali Mhasawade, Yuan Zhao, Rumi Chunara
2020 arXiv   pre-print
We present here a very brief introduction into research in these fields, as well as connections to existing machine learning work to help activate the machine learning community on such topics and highlight  ...  Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities  ...  Stephanie Cook and Harvineet Singh for helpful discussions on this topic. The work was partially supported under National Science Foundation grant 1845487.  ... 
arXiv:2008.07278v1 fatcat:7oehaukknbawfepczcg2vgywly
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