Teixi
Teixi  ·  4 months ago

Content Warning

#reading#ML
Mitigating Bias in Machine Learning

Edited By @drcaberry
Brandeis Hill Marshall

„We dedicate this work to the diverse voices in #AI who work tirelessly to call out bias and work to mitigate it and advocate for #EthicalAI every day.
Some of the trailblazers doing the work are
@ruha9
@timnitGebru
@cfiesler
Joy Buolamwini
@ruchowdh
@safiyanoble
We also dedicate this work to the future engineers, scientists, and sociologists who will use it to inspire them to join the charge.“

Book Cover: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
Book Cover: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
Book Cover: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. Mitigating Bias in Machine Learning addresses: Ethical and Societal Implications of Machine Learning Social Media and Health Information Dissemination Comparative Case Study of Fairness Toolkits Bias Mitigation in Hate Speech Detection Unintended Systematic Biases in Natural Language Processing Combating Bias in Large Language Models Recognizing Bias in Medical Machine Learning and AI Models Machine Learning Bias in Healthcare Achieving Systemic Equity in Socioecological Systems Community Engagement for Machine Learning
Figure 9.9 ML life cycle with bias indicators and mitigation techniques. (inspired by Herhausen & Fahse, 2022; Huang et al., 2022; and van Giffen et al., 2022) • Preprocessing bias mitigation techniques attempt to remove discrimination by adding more data or modifying the available training data. • In-processing bias mitigation techniques affect the algorithm itself and the learning procedure by imposing constraints, updating the objective function, or regularization. • Postprocessing bias mitigation techniques may be implemented following model deployment or during the re-evaluation period in which adjustments are made to the model decision thresholds or the model output, including relabeling. From book: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa
Figure 9.9 ML life cycle with bias indicators and mitigation techniques. (inspired by Herhausen & Fahse, 2022; Huang et al., 2022; and van Giffen et al., 2022) • Preprocessing bias mitigation techniques attempt to remove discrimination by adding more data or modifying the available training data. • In-processing bias mitigation techniques affect the algorithm itself and the learning procedure by imposing constraints, updating the objective function, or regularization. • Postprocessing bias mitigation techniques may be implemented following model deployment or during the re-evaluation period in which adjustments are made to the model decision thresholds or the model output, including relabeling. From book: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa
Figure 9.9 ML life cycle with bias indicators and mitigation techniques. (inspired by Herhausen & Fahse, 2022; Huang et al., 2022; and van Giffen et al., 2022) • Preprocessing bias mitigation techniques attempt to remove discrimination by adding more data or modifying the available training data. • In-processing bias mitigation techniques affect the algorithm itself and the learning procedure by imposing constraints, updating the objective function, or regularization. • Postprocessing bias mitigation techniques may be implemented following model deployment or during the re-evaluation period in which adjustments are made to the model decision thresholds or the model output, including relabeling. From book: Mitigating Bias in Machine Learning Edited by Carlotta A. Berry, Brandeis Hill Marshall https://www.mhprofessional.com/mitigating-bias-in-machine-learning-9781264922444-usa
Mark Guyer
Mark Guyer  ·  2 months ago

Content Warning

Tomes and Talismans is the best post-apocalyptic, dystopian, educatioal series about the Dewey Decimal System ever devised: On a future Earth, we have been colonized by simple-minded, humanoid aliens called The Wipers who have destroyed our technology and our ability to communicate on a massive scale. The only way to stop the only way to stop them? Efficiently navigating a library and reading books! The more our world falls to ignorance and illiteracy, the more this silly show feels like a documentary. THE WIPERS ARE HERE! THEY’RE ALREADY HERE!!! #scifi #sciencefiction #postapocalyptic #appcalypse #publicaccess #libraries #books #reading #librarians #aliens #invasion #horror
Molly White
Molly White  ·  2 months ago

Content Warning

Update on the arm I bought to hold my e-reader for me: have just discovered this enables me to knit (simple patterns) while I read

Last night I finished The Long Way to a Small, Angry Planet and half a sock

#knitting #reading

Calico Jesse
Calico Jesse  ·  4 months ago

Content Warning

“You don't think much of my chances, do you?"
"Not much," agreed the ex-Minister of Education. "You're a Smyrnian."
"That's no legal bar. I've had a lay education."
"Well, come now. Since when does prejudice follow any law but its own.” —Asimov, Foundation, 1952

Sadly still relevant today.

#Reading#SciFi#Asimov#Politics

Miguel Afonso Caetano
Miguel Afonso Caetano  ·  4 months ago

Content Warning

"This month, the OECD released the results of a vast exercise: in-person assessments of the literacy, numeracy and problem-solving skills of 160,000 adults aged 16-65 in 31 different countries and economies. Compared with the last set of assessments a decade earlier, the trends in literacy skills were striking. Proficiency improved significantly in only two countries (Finland and Denmark), remained stable in 14, and declined significantly in 11, with the biggest deterioration in Korea, Lithuania, New Zealand and Poland.

Among adults with tertiary-level education (such as university graduates), literacy proficiency fell in 13 countries and only increased in Finland, while nearly all countries and economies experienced declines in literacy proficiency among adults with below upper secondary education. Singapore and the US had the biggest inequalities in both literacy and numeracy.

“Thirty per cent of Americans read at a level that you would expect from a 10-year-old child,” Andreas Schleicher, director for education and skills at the OECD, told me — referring to the proportion of people in the US who scored level 1 or below in literacy. “It is actually hard to imagine — that every third person you meet on the street has difficulties reading even simple things.”

https://www.ft.com/content/e2ddd496-4f07-4dc8-a47c-314354da8d46

#Literacy#Numeracy#ProblemSolving #OECD#Reading

Nazo
Nazo  ·  5 months ago

Content Warning

Looking a bit for some #books suggestions. I've been thinking of trying to fill out my #ebooks library more. I need to have a huge collection of stuff I'd be able to access on my tablet (which I can easily charge via solar power) if I lost access to a lot of things (such as Internet...)

What I'd really like to look for more of right now are particularly #Fantasy novels, but maybe a bit of #Scifi as well. But one thing I'd really like to look more fore is the stuff that is less "gritty." I'm tired of so many books focusing so hard on suffering, torture, good people driven bad, etc. More of the stuff where you know in the end the good guys will win and they won't compromise on what they know is right, even if it may be really hard along the way.

Maybe some cozy stuff too?

#Reading

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