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For full forum click here: https://youtu.be/ucic8cuEd6A INSTAGRAM: https://www.instagram.com/veritasforum FACEBOOK: https://www.facebook.com/veritasforum SUBSCRIBE: https://www.youtube.com/subscription_center?add_user=VeritasForum Find this and many other talks at http://www.veritas.org/engage Over the past two decades, The Veritas Forum has been hosting vibrant discussions on life’s hardest questions and engaging the world’s leading colleges and universities with Christian perspectives and the relevance of Jesus. Learn more at http://www.veritas.org, with upcoming events and over 600 pieces of media on topics including science, philosophy, music, business, medicine, and more!
How seriously is the data science industry taking the issue of bias in machine learning? In this clip taken from a recent CareerFoundry live event senior Data Scientist Tom Gadsby will share some thoughts on the matter! Want more content like this? Check out CareerFoundry’s events page for more deep dive data based content, and much more: https://careerfoundry.com/en/events/ — Looking to start a career in Data? Take your first steps with CareerFoundry’s free data analytics short course: https://bit.ly/CareerFoundryFreeDataAnalyticsShortCourse_023 Want a deeper dive on some key UX topics? Check out CareerFoundry’s blog here: https://careerfoundry.com/en/blog/ Thanks for watching! #DataAnalytics #DataScience #Shorts Want more from CareerFoundry? Check out our other social media channels and blog here: 🔍 https://linktr.ee/CareerFoundry​ For more information on our programs, visit us at: 🖥 https://careerfoundry.com/ Data Science – Bias In Machine Learning Algorithms https://youtu.be/oIEFa1XuDJk
Extensive evidence has shown that AI can embed human and societal bias and deploy them at scale. And many algorithms are now being reexamined due to illegal bias. So how do you remove bias & discrimination in the machine learning pipeline? In this webinar you’ll learn the debiasing techniques that can be implemented by using the open source toolkit AI Fairness 360. AI Fairness 360 (AIF360, https://aif360.mybluemix.net/) is an extensible, open source toolkit for measuring, understanding, and removing AI bias. AIF360 is the first solution that brings together the most widely used bias metrics, bias mitigation algorithms, and metric explainers from the top AI fairness researchers across industry & academia. Trisha Mahoney is an AI Tech Evangelist for IBM with a focus on Fairness & Bias. Trisha has spent the last 10 years working on Artificial Intelligence and Cloud solutions at several Bay Area tech firms including (Salesforce, IBM, Cisco). Prior to that, Trisha spent 8 years working as a data scientist in the chemical detection space. She holds an Electrical Engineering degree and an MBA in Technology Management. https://aif360.mybluemix.net/ https://aif360.slack.com/ http://ibm.biz/Bdqbd2
Authors: Yan Huang; Param Vir Singh, Runshan Fu, Carnegie Mellon University Artificial intelligence (AI) and machine learning (ML) algorithms are widely used throughout our economy in making decisions that have far-reaching impacts on employment, education, access to credit, and other areas. Initially considered neutral and fair, ML algorithms have recently been found increasingly biased, creating and perpetuating structural inequalities in society. With the rising concerns about algorithmic bias, a growing body of literature attempts to understand and resolve the issue of algorithmic bias. In this tutorial, we discuss five important aspects of algorithmic bias. We start with its definition and the notions of fairness policy makers, practitioners, and academic researchers have used and proposed. Next, we note the challenges in identifying and detecting algorithmic bias given the observed decision outcome, and we describe methods for bias detection. We then explain the potential sources of algorithmic bias and review several bias-correction methods. Finally, we discuss how agents’ strategic behavior may lead to biased societal outcomes, even when the algorithm itself is unbiased. We conclude by discussing open questions and future research directions.
To watch the Q&A, click here: https://www.youtube.com/watch?v=rRUGSlVOW1k& Iris Bohnet Professor of Public Policy, Harvard Kennedy School Director of the Women and Public Policy Program, HKS Co-chair, Behavioral Insights Group, HKS Cynthia Dwork Gordon McKay Professor of Computer Science, Harvard Paulson School of Engineering Radcliffe Alumnae Professor, Radcliffe Institute for Advanced Study Alex “Sandy” Pentland Professor of Media Arts and Sciences, Massachusetts Institute of Technology Toshiba Professor, MIT Media Lab Entrepreneurship Program Director, MIT Sheila Jasanoff (moderator) Pforzheimer Professor of Science and Technology Studies, Harvard Kennedy School Director, Program on Science, Technology and Society, HKS The Future Society is launching a 12-month civic consultation on the governance of Artificial Intelligence. Run on the collective intelligence platform Assembl, this debate is open to everyone and will involve citizens, experts and practitioners to better understand the dynamics of the rise of AI, its consequences and how to govern this revolution. The debate can be found here https://agora2.bluenove.com/ai-consultation :and soon to be available here www.ai-initiative.org/AI-consultation.
The meeting will focus on considerations to reduce bias and increase fairness in artificial intelligence used to make health care decisions. Speakers will highlight how bias manifests in health care, methods to prevent, mitigate, and test for bias, and promising examples to address bias. The meeting will end with a discussion of how different federal agencies are thinking about bias and fairness in health care
Kriti Sharma explores how the lack of diversity in tech is creeping into our artificial intelligence, offering three ways we can start making more ethical algorithms. This talk was filmed at TEDxWarwick. Watch the full talk here: https://youtu.be/oo5KXFTIAwE Start each day with short, eye-opening ideas from some of the world’s greatest TEDx speakers. Hosted by Atossa Leoni, TEDx SHORTS will give you the chance to immerse yourself in surprising knowledge, fresh perspectives, and moving stories from some of our most compelling talks. Less than 10 minutes a day, everyday. All TEDx events are organized independently by volunteers in the spirit of TED’s mission of ideas worth spreading. To learn more about the TEDx SHORTS podcast, the TEDx program, or give feedback on this episode, please visit http://go.ted.com/tedxshorts.
Myself Shridhar Mankar a Engineer l YouTuber l Educational Blogger l Educator l Podcaster. My Aim- To Make Engineering Students Life EASY. Website – https://5minutesengineering.com 5 Minutes Engineering English YouTube Channel – https://m.youtube.com/channel/UChTsiSbpTuSrdOHpXkKlq6Q Instagram – https://www.instagram.com/5minutesengineering/?hl=en A small donation would mean the world to me and will help me to make AWESOME videos for you. • UPI ID : 5minutesengineering@apl Playlists : • 5 Minutes Engineering Podcast : https://youtube.com/playlist?list=PLYwpaL_SFmcCTAu8NRuCaD3aTEgHLeF0X • Aptitude : https://youtube.com/playlist?list=PLYwpaL_SFmcBpa1jwpCbEDespCRF3UPE5 • Machine Learning : https://youtube.com/playlist?list=PLYwpaL_SFmcBhOEPwf5cFwqo5B-cP9G4P • Computer Graphics : https://youtube.com/playlist?list=PLYwpaL_SFmcAtxMe7ahYC4ZYjQHun_b-T • C Language Tutorial for Beginners : https://youtube.com/playlist?list=PLYwpaL_SFmcBqvw6QTRsA8gvZL3ao2ON- • R Tutorial for Beginners : https://youtube.com/playlist?list=PLYwpaL_SFmcCRFzBkZ-b92Hdg-qCUfx48 • Python Tutorial for Beginners : https://youtube.com/playlist?list=PLYwpaL_SFmcCJu4i6UGMkMx1p3yYZJsbC • Embedded and Real Time Operating Systems (ERTOS) : https://youtube.com/playlist?list=PLYwpaL_SFmcBpuYagx0JiSaM-Bi4dm0hG • Shridhar Live Talks : https://youtube.com/playlist?list=PLYwpaL_SFmcD21x33RkmGvcZtrnWlTDdI • Welcome to 5 Minutes Engineering : https://youtube.com/playlist?list=PLYwpaL_SFmcCwG02L6fm0G5zmzpyw3eyc • Human Computer Interaction (HCI) : https://youtube.com/playlist?list=PLYwpaL_SFmcDz_8-pygbcNvNF0DEwKoIL • Computer Organization and Architecture : https://youtube.com/playlist?list=PLYwpaL_SFmcCaiXeUEjcTzHwIfJqH1qCN • Deep Learning : https://youtube.com/playlist?list=PLYwpaL_SFmcD-6P8cuX2bZAHSThF6AYvq • Genetic Algorithm : https://youtube.com/playlist?list=PLYwpaL_SFmcDHUTN26NXKfjg6wFJKDO9R • Cloud Computing : https://youtube.com/playlist?list=PLYwpaL_SFmcCyQH0n9GHfwviu6KeJ46BV • Information and Cyber Security : https://youtube.com/playlist?list=PLYwpaL_SFmcArHtWmbs_vXX6soTK3WEJw • Soft Computing and Optimization Algorithms : https://youtube.com/playlist?list=PLYwpaL_SFmcCPUl8mAnb4g1oExKd0n4Gw • Compiler Design : https://youtube.com/playlist?list=PLYwpaL_SFmcC6FupM–SachxUTOiQ7XHw • Operating System : https://youtube.com/playlist?list=PLYwpaL_SFmcD0LLrv7CXxSiO2gNJsoxpi • Hadoop : https://youtube.com/playlist?list=PLYwpaL_SFmcAhiP6C1qVorA7HZRejRE6M • CUDA : https://youtube.com/playlist?list=PLYwpaL_SFmcB73J5yO6uSFUycHJSA45O0 • Discrete Mathematics : https://youtube.com/playlist?list=PLYwpaL_SFmcDKuvj-wIgDnHA5JTfUwrHv • Theory of Computation (TOC) : https://youtube.com/playlist?list=PLYwpaL_SFmcDXLUrW3JEq2cv8efNF6UeQ • Data Analytics : https://youtube.com/playlist?list=PLYwpaL_SFmcD_agAK_MpCDJdDXFuJqS9X • Software Modeling and Design : https://youtube.com/playlist?list=PLYwpaL_SFmcD1pjNSpEm2pje3zPrSiflZ • Internet Of Things (IOT) : https://youtube.com/playlist?list=PLYwpaL_SFmcB8fDd64B8SkJiPpEIzpCzC • Database Management Systems (DBMS) : https://youtube.com/playlist?list=PLYwpaL_SFmcBU4HS74xGTK1cAFbY0rdVY • Computer Network (CN) : https://youtube.com/playlist?list=PLYwpaL_SFmcAXkWn2IR-l_WXOrr0n851a • Software Engineering and Project Management : https://youtube.com/playlist?list=PLYwpaL_SFmcCB7zUM0YSDR-1mM4KoiyLM • Design and Analysis of Algorithm : [More]
Director Robin Hauser and tribe of women behind, “bias,” talks to us about the team’s hopes for their film, the importance of confronting our own implicit biases, and their work with bias in Artificial Intelligence (AI). “Bias” confronts unconscious and implicit bias in all walks of life: from CEOs and police enforcement to professional soccer players like Abby Wambach. With the toxic effect of bias making headlines every day, the time for this film is now. Watch the trailer: https://www.imdb.com/title/tt7137804/?ref_=ttpl_pl_tt To read more about the Implicit Association Test and unconscious bias: https://implicit.harvard.edu/implicit/faqs.html Watch Robin Hauser’s TED talk: “Can we protect AI from our biases?”: https://www.ted.com/talks/robin_hauser_can_we_protect_ai_from_our_biases/up-next
Over the past year, discourse about the ethical risks of machine learning has largely shifted from speculative fear about rogue super intelligent systems to critical examination of machine learning’s propensity to exacerbate patterns of discrimination in society. This talk explains how and why bias creeps into supervised machine learning systems and proposes a framework businesses can apply to hold algorithmic systems accountable in a way that is meaningful to people impacted by systems. You’ll learn why it’s important to consider bias throughout the entire machine learning product lifecycle (not just algorithms), how to assess tradeoffs between accuracy and explainability, and what technical solutions are available to reduce bias and promote fairness.
A common objection to concerns about bias in machine learning models is to point out that humans are really biased too. This is correct, yet machine learning bias differs from human bias in several key ways that we need to understand.
How can artificial intelligence be biased? Bias in artificial intelligence is when a machine gives consistently different outputs for one group of people when compared to another. Typically these biased outputs follow classic human societal biases like race, gender, biological sex, nationality, or age. Biases can be as a result of assumptions made by the engineers who developed the AI, or they can be as a result of prejudices in the training data that taught the AI, which is what Johann Diedrick explains in the latest edition of Mozilla Explains. Learn more about Diedrick’s project, Dark Matters: https://foundation.mozilla.org/blog/dark-matters-new-project-spotlights-the-inbuilt-bias-in-digital-voice-assistants/ Featured in this video, Survival of the Best Fit is a Mozilla Creative Media awardee built by Jihyun Kim, Gábor Csapo, Miha Klasinc, and Alia ElKattan. Experience it here: https://www.survivalofthebestfit.com/
Dave Gershgorn from OneZero talks about how bias can creep into AI systems and what can be done to prevent it. Subscribe & watch the full Tech News Weekly podcast: https://twit.tv/tnw/131  Hosts: Jason Howell, Mikah Sargent Guest: Dave Gershgorn You can find more about TWiT and subscribe to our full shows at https://shows.twit.tv/ Subscribe: https://twit.tv/subscribe Products we recommend: https://twit.to/amazon TWiT may earn commissions on certain products. Join our TWiT Community on Discourse: https://www.twit.community/ Follow us: https://twit.tv/ https://twitter.com/TWiT https://www.facebook.com/TWiTNetwork https://www.instagram.com/twit.tv/ About us: TWiT.tv is a technology podcasting network located in the San Francisco Bay Area with the #1 ranked technology podcast This Week in Tech hosted by Leo Laporte. Every week we produce over 30 hours of content on a variety of programs including Tech News Weekly, MacBreak Weekly, This Week in Google, Windows Weekly, Security Now, All About Android, and more.
Using real world examples, this session will explore how to understand, measure and systematically mitigate bias in machine learning models. Understanding these principles is an important part of building a machine learning strategy. This session will cover both the business and technical considerations. Session Speakers: Pietro Perona (Session M37)
Belinda Djamson, Accenture’s Data and Analytics Management Lead UK&I, reveals an AI-led solution for interviews that itself revealed bias in the data that fed its ‘insights’, as a warning of the human errors that can still creep into even the most well-built strategies. Filmed at The Studio 2019.
Every year, Amazon gets more than 200 thousand resumes for the various jobs they are hiring for. Google, gets 10 times that, with over 2 million resumes each year. Imagine being the HR managers responsible for vetting all those. That seems like an absolutely daunting task, but, in the modern age we live in, this seems like a task that could be given over to something that could process those resumes nearly instantaneously: an artificial intelligence system. In fact, that’s exactly what companies like Amazon and Google have tried in the past, though the results were not what they expected. Welcome to Data Demystified. I’m Jeff Galak and in this episode we’re going to talk about gender bias in artificial intelligence. To be sure, there are many examples of bias in machine learning and AI systems and I plan to make videos about those too, but, for now, I want to focus on one big example in the world of resume vetting. After all, one of the goals of things like gender and racial equity is to ensure that everyone, regardless of their gender or race, has a fair shake at the most desirable jobs out there. But when companies let AI algorithms have a say in those decisions, bias has a sneaky way of creeping in. In this episode, I’m going to try and provide you with the intuition to understand how this type of bias could emerge, even when a big goal of these systems is to take [More]
The key to understanding AI bias is to see datasets as textbooks for your machine “student” to learn from. Like textbooks, datasets have human authors and reflect the biases of those authors. They’re collected according to instructions made by people. What does this have to do with fighting AI bias? Find out in the video! Learn more: What is AI bias: http://bit.ly/quaesita_biasdef Pay attention to that man behind the curtain: http://bit.ly/quaesita_aibiasm Video clips 12040, and 31772 courtesy of Pixabay.
The Schwartz Reisman weekly seminar series welcomes Joanna J. Bryson, professor of ethics and technology at the Hertie School in Berlin. She is a globally recognized leader in intelligence broadly, including AI policy and AI ethics. Bryson’s present research focuses on the impact of technology on economies and human cooperation, transparency for and through AI systems, interference in democratic regulation, the future of labour, society, and digital governance more broadly. Her work has appeared in venues ranging from a reddit to Science. As of July 2020, Bryson is one of nine experts nominated by Germany to the Global Partnership for Artificial Intelligence (GPAI). Visit her blog Adventures in NI for more on her work in natural and artificial intelligence. You can find her recommended readings from her blog below under, additional readings. Talk title: “Bias, Trust, and Doing Good: Scientific Explorations of Topics in AI Ethics” Abstract: This talk takes a scientific look at the cultural phenomena behind the #tags many people associate with AI ethics and regulation. I will introduce the concept of public goods, show how these relate to sustainability, and then provide a quick review of three recent results concerning: – What trust is, where it comes from, what it’s for, and how AI might alter it; – Where bias in language comes from, what it’s for, and whether AI might and should be used to alter it; – Where polarization comes from, what it was for historically, and how we should deal with it in the [More]
Looking for a career upgrade & a better salary? We can help, Choose from our no 1 ranked top programmes. 25k+ career transitions with 400 + top corporate companies. Exclusive for working professionals: https://glacad.me/3eO7rXR Get your free certificate of completion for the Analysis of Variance course, Register Now: https://glacad.me/32LaxJT In simple words, bias means how far you have come in predicting the desired value from your actual value. It is an approach that can ultimately make or break the model in favor or against your idea. A straightforward example can be: When we talk about the famous linear regression model, we quantify the relationship between X and Y variable as linear; on the contrary, in reality, the relationship might not be perfectly linear as we had read. Variance is the reverse of bias. It is called the variance when your model performs exceptionally well on the training dataset yet fails to live up to the same standards when running it on an entirely new dataset. In simple words, your model conveys to you that the predicted values are very scattered from the actual values. This concept is similar to the overfitting of the model on the dataset, also called the difference between the model fits when used on different datasets. 01:25 – Agenda 01:56 – Introduction 04:35 – Bias and Variance in Machine Learning 07:42 – Difference between Bias and Variance 08:15 – Bias vs Variance 13:14 – Bias Variance Trade-Off 18:03 – Bias and Variance In Machine Learning 18:34 [More]
Margaret is a Senior Research Scientist in Google’s Research & Machine Intelligence group, working on artificial intelligence. Her research generally involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. This includes research on helping computers to communicate based on what they can process, as well as projects to create assistive and clinical technology from the state of the art in AI. Her work combines computer vision, natural language processing, social media, many statistical methods, and insights from cognitive science. Margaret Mitchell, PhD was a keynote speaker at ODSC East 2020 Virtual Conference → To watch more videos like this, visit https://aiplus.odsc.com ← Do You Like This Video? Share Your Thoughts in Comments Below Also, You can visit our website and choose the nearest ODSC Event to attend and experience all our Trainings and Workshops: https://odsc.com/apac/ https://odsc.com/california/ Sign up for the newsletter to stay up to date with the latest trends in data science: https://opendatascience.com/newsletter/ Follow Us Online! • Facebook: https://www.facebook.com/OPENDATASCI/ • Instagram: https://www.instagram.com/odsc/ • Blog: https://opendatascience.com/ • Linkedin: https://www.linkedin.com/company/open-data-science/ • Learning Videos: https://learnai.odsc.com #ArtificialIntelligence #DataScience #ODSC
Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. … Bias is all of our responsibility. In this video I explain three ways to deal with Bias in AI.
As humans, our bias perspectives are shaped by how we perceive our environments and experiences. AI perceives its experience in the form of data and this affects its bias. What are the different types of AI bias and how can we mitigate their effect. Dr. Seth Dobrin is here to show us how to manage the bias in AI? Seth Dobrin is the Chief Data Officer IBM Cloud and Cognitive Software. He is responsible for the transformation of the Cloud and Cognitive Software business operations using data and AI. Previously, he led the data science transformation of a Fortune 300 company, as well as the company’s Agile transformation and their shift to the cloud, and oversaw efforts to leverage the data science transformation to drive new business models to create new revenue streams. He is a founding member of the International Society of Chief Data Officers and has been a prolific panelist at the East and West Chief Data Officer Summits. Seth holds a Ph.D. in genetics from Arizona State University, where he focused on the application of statistical and molecular genetics toward the elucidation of the causes of neuropsychiatric disorder. Seth Dobrin is the Chief Data Officer IBM Cloud and Cognitive Software. He is responsible for the transformation of the Cloud and Cognitive Software business operations using data and AI. Previously, he led the data science transformation of a Fortune 300 company, as well as the company’s Agile transformation and their shift to the cloud, and oversaw efforts to [More]
Screening and Panel Discussion on Coded Bias Film, March 29 ACM’s Technology Policy Council and Diversity and Inclusion Council sponsored a free screening and public discussion of the film “Coded Bias” and how those in computer science fields can address issues of algorithmic fairness. The discussion occured on Monday, March 29, 2021 from 2:30-4:00 pm EDT (8:30pm CEST). PANELISTS: Dame Prof. Wendy Hall, Regius Professor of Computer Science, University of Southampton Hon. Bernice Donald, Federal Judge U.S. Court of Appeals for the Sixth Circuit Prof. Latanya Sweeney, Daniel Paul Professor of Government & Technology, Harvard University Prof. Ricardo Baeza-Yates, Research Professor, Institute for Experiential AI, Northeastern University MODERATOR: Prof. Jeanna Matthews, Professor of Computer Science, Clarkson University SPONSORS: ACM Technology Policy Council ACM Diversity & Inclusion Council National Science Foundation ADVANCE Grant Clarkson Open Source Institute (COSI), Clarkson University https://www.acm.org/diversity-inclusion/from-coded-bias-to-algorithmic-fairness
How many times a day do you interact with AI in your everyday things? Four leading figures in the future of AI discuss the responsibilities and opportunities for designers using data as material to create social impact through a more inclusive design of products and services. When considering the future of design leveraging artificial intelligence, the mantra can no longer be “move fast and break things”. Featuring: Jennifer Bove, Head of Design for B2B Payments, Capital One Dr. Jamika D. Burge, Head of AI Design Insights, Capital One Co-Founder, blackcomputeHER Ruth Kikin-Gil, Responsible AI strategist and Senior UX Designer, Microsoft Molly Wright Steenson, Senior Associate Dean for Research, College of Fine Arts, Carnegie Mellon University Dive deeper into this issue: https://onblend.tealeaves.com/diversity-bias-ethics-in-ai/ Register for future Nature X Design Events: https://onblend.tealeaves.com/naturexdesign/​ Get to know TEALEAVES Our Sustainability: ​https://www.tealeaves.com/pages/our-ethos Facebook: http://www.facebook.com/TealeavesCo​​ Twitter: http://www.twitter.com/TealeavesCo​​ Instagram: http://www.instagram.com/TealeavesCo