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
Ayanna Howard Hacking the Human Bias in AI Keynote at FAT*2020, Barcelona, January 27th to 30th, 2020 Bio
This video is about the Problem with AI job interviews. Some companies providing such solutions claim these AI tools eliminate human bias in the process, but that claim is very questionable. Join the Rethink.Community: https://www.rethink.community Subscribe to be the 1st to receive event updates: https://www.rethink.community/subscribe​ Objective or Biased by BR.de: https://web.br.de/interaktiv/ki-bewerbung/en/ ⏰TIMESTAMPS⏰ 00:00​​ – Intro: AI “Gaydar” 01:00​​ – Why is there Bias in AI? 01:31​​ – Why is AI bias a problem? 02:06 – Personal example on “selection bias” 03:30​ – Everyone is biased 04:46​ – BR.de journalists’ test on the AI interview product 06:09 – Another possibly harmful application Subscribe to see more videos like this: https://bit.ly/3b2rBt4​​ Watch my most recent upload: https://youtu.be/Ri0i_5ByegQ #AI #artificialintelligence #aiinterview ——————————————————————————- RECOMMENDED PLAYLISTS ——————————————————————————- Digital Marketing for Humans: https://www.youtube.com/playlist?list…​ Business Strategy: https://www.youtube.com/playlist?list…​ Humans & Technology: https://www.youtube.com/playlist?list…​ ——————————————————————————- FOLLOW ME ——————————————————————————- Website: https://www.charlottehan.com​​ Twitter: https://twitter.com/sunsiren​​ Instagram: https://www.instagram.com/iamcharlottehan LinkedIn: https://www.linkedin.com/in/charlottehan
Through a series of case studies, we will consider different types of algorithmic bias and debunk several misconceptions. We then consider several concrete steps towards addressing bias.
This tutorial was recorded at KDD 2020 as a live, hands-on tutorial. The content is available at https://dssg.github.io/fairness_tutorial/
How do algorithms spread bias throughout our culture? In this talk, technology thought leader Corey Patrick White shares the dangers of algorithmic bias, and how high the stakes are for humanity. As a partner and senior vice president at Future Point of View, Corey Patrick White is tasked with helping leaders look out into the future and anticipate how technology will impact their organizations and themselves. He is especially focused on the role that machine intelligence will play in almost every aspect of life: from the decisions we make, to the professions we undertake, to how we interact with the world and each other. Corey began his career as a journalist before joining Future Point of View. As a journalist, he developed investigative skills as well as a desire to understand complicated topics and to explain those topics in a way that everyone can understand. He brings these skills to the speaking stage, offering insights into how complex innovation is dramatically altering the world we live in ways that can be both positive and negative. As a partner and senior vice president at Future Point of View, Corey White is tasked with helping leaders look out into the future and anticipate how technology will impact their organizations and themselves. He is especially focused on the role that machine intelligence will play in almost every aspect of life: from the decisions we make, to the professions we undertake, to how we interact with the world and each other. Corey began his [More]
DropShot is an algorithmic private investment company specializing in machine learning and artificial intelligence. We believe that the investment process should be as scientific as possible and not influenced by human bias. Our goal is create products for our investors that are: 1) Purely Quantitative 2) Liquid 3) Transparent DropShot’s partners have extensive experience building and deploying large scale machine learning solutions. Our systems are constantly sourcing, evaluating, and integrating new alpha sources into our investment decisions, from trading universe selection, to strategy development, to live trading portfolio management and execution. It’s our mission to continuously improve our processes and outcomes for our clients.
Bias Traps in AI: A panel discussing how we understand bias in AI systems, highlighting the latest research insights and why issues of bias matter in concrete ways to real people. Solon Barocas, Assistant Professor of Information Science, Cornell University Arvind Narayanan, Assistant Professor of Computer Science, Princeton University Cathy O’Neil, Founder, ORCAA Deirdre Mulligan, Associate Professor, School of Information and Berkeley Center for Law & Technology, UC Berkeley John Wilbanks, Chief Commons Officer, Sage Bionetworks AI Now 2017 Public Symposium – July 10, 2017 Follow AI Now on Twitter: https://twitter.com/AINowInitiative Subscribe to our channel: https://www.youtube.com/c/ainowinitiative Visit our website: https://artificialintelligencenow.com
MIT Introduction to Deep Learning 6.S191: Lecture 8 Algorithmic Bias and Fairness Lecturer: Ava Soleimany January 2021 For all lectures, slides, and lab materials: http://introtodeeplearning.com​ Lecture Outline 0:00​ – Introduction and motivation 1:40 – What does “bias” mean? 4:22 – Bias in machine learning 8:32 – Bias at all stages in the AI life cycle 9:25 – Outline of the lecture 10:00 – Taxonomy (types) of common biases 11:29 – Interpretation driven biases 16:04 – Data driven biases – class imbalance 24:02 – Bias within the features 27:09 – Mitigate biases in the model/dataset 33:20 – Automated debiasing from learned latent structure 37:11 – Adaptive latent space debiasing 39:39 – Evaluation towards decreased racial and gender bias 41:00 – Summary and future considerations for AI fairness Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!
Artificial intelligence might be a technological revolution unlike any other, transforming our homes, our work, our lives; but for many – the poor, minority groups, the people deemed to be expendable – their picture remains the same. “The way these technologies are being developed is not empowering people, it’s empowering corporations,” says Zeynep Tufekci, from the University of North Carolina.. “They are in the hands of the people who hold the data. And that data is being fed into algorithms that we don’t really get to see or understand that are opaque even to the people who wrote the programme. And they’re being used against us, rather than for us.” In episode two of The Big Picture: The World According to AI we examine practices such as predictive policing, predictive sentencing, as well as the power structures and in-built prejudices that could lead to even more harm than the good its champions would suggest. In the United States, we travel to one of the country’s poorest neighbourhoods, Skid Row in Los Angeles, to see first-hand how the Los Angeles Police Department is using algorithmic software to police a majority black community. And in China, we examine the implications of a social credit scoring system that deploys machine learning technologies – new innovations in surveillance and social control that are claimed to be used against ethnic Uighur communities. As AI is used to make more and more decisions for and about us, from targeting, to policing, to social welfare, it raises [More]
Kate Crawford is a leading researcher, academic and author who has spent the last decade studying the social implications of data systems, machine learning and artificial intelligence. She is a Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and a Visiting Professor at the MIT Media Lab. December 5th, 2017
While it’s important to consider the diversity of your dataset and the performance of your model across different demographic group, this is just a narrow slice of the issues we need to consider related to bias and fairness. Using machine learning for medicine as a case study, I’ll illustrate some of the broader considerations related to bias, power, and participation that all data scientists need to take into account. This talk was delivered at the Stanford AI in Medicine & Imaging Symposium on August 5, 2020, as part of a session on Fairness in Clinical Machine Learning. For more on Practical Data Ethics, please check out my free online course at http://ethics.fast.ai/
This talk was presented at PyBay2018 – the Bay Area local Python conference. See pybay.com for more details about PyBay and click SHOW MORE for more information about this talk. Abstract: Algorithms are increasingly used to make life-changing decisions about health care benefits, who goes to jail, and more, and play a crucial role in pushing people towards extremism. Through a series of case studies, I want to debunk several misconceptions about bias and ethics in AI, and propose some healthier principles. Slides: https://goo.gl/ThXJQm Presenter: Rachel Thomas is co-founder of fast.ai, which is making deep learning more accessible, and a researcher-in-residence at University of San Francisco Data Institute. Rachel has a mathematics PhD from Duke and has previously worked as a quant, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright. Rachel was selected by Forbes as one of 20 “Incredible Women Advancing A.I. Research.” She co-created the course “Practical Deep Learning for Coders,” which is available for free at course.fast.ai and more than 50,000 students have started it. Her writing has made the front page of Hacker News 4x, the top 5 list on Medium, and been translated into Chinese, Spanish, & Portuguese. She is on twitter @math_rachel This and other PyBay2018 videos are brought to you by our Gold Sponsor Cisco!
Mar.26 — Microsoft Post Doctoral Researcher Timnit Gebru discusses the effects of bias in artificial intelligence. She speaks with Emily Chang on “Bloomberg Technology.”
Algorithms encode data, and that data can be affected by human bias. Industry luminaries explore what this means for artificial intelligence (AI) in the enterprise – and how we can work together to minimize bias and maximize accuracy. Subscribe: http://www.youtube.com/user/adobe LET’S CONNECT Facebook: http://facebook.com/adobe Twitter: http://twitter.com/adobe Instagram: http://www.instagram.com/adobe
Check out my collab with “Above the Noise” about Deepfakes: https://www.youtube.com/watch?v=Ro8b69VeL9U Today, we’re going to talk about five common types of algorithmic bias we should pay attention to: data that reflects existing biases, unbalanced classes in training data, data that doesn’t capture the right value, data that is amplified by feedback loops, and malicious data. Now bias itself isn’t necessarily a terrible thing, our brains often use it to take shortcuts by finding patterns, but bias can become a problem if we don’t acknowledge exceptions to patterns or if we allow it to discriminate. Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Efrain R. Pedroza, Matthew Curls, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, DAVID NOE, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore — Want to find Crash Course elsewhere on the internet? Facebook – http://www.facebook.com/YouTubeCrashCourse Twitter – http://www.twitter.com/TheCrashCourse Tumblr – http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids #CrashCourse #ArtificialIntelligence #MachineLearning
MIT grad student Joy Buolamwini was working with facial analysis software when she noticed a problem: the software didn’t detect her face — because the people who coded the algorithm hadn’t taught it to identify a broad range of skin tones and facial structures. Now she’s on a mission to fight bias in machine learning, a phenomenon she calls the “coded gaze.” It’s an eye-opening talk about the need for accountability in coding … as algorithms take over more and more aspects of our lives. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world’s leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design — plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Ethics of AI Lab Centre for Ethics, University of Toronto, March 20, 2018 http://ethics.utoronto.ca Kathryn Hume intergrate.ai
#YouTube #HumanBias #AI How do we know what is real and what is honest in this world of super information and connectivity? Human bias, computer algorithms and social media influencers are becoming ever more part of our human existence and the ability to critically evaluate and understand how they work and what they mean transcends the purchasing of gold and silver that my channel has traditionally focused on and is in my opinion relevant to nearly every decision we make in our modern lives. Almost everything we do in the physical modern 2020 pandemic world we now live in is connected to the online universe – I choose the word universe because it is almost too vast to comprehend how big this online space is. We get our news, socialise, learn, interact, work and many more day to day activates online and collectively we are being disconnected from the physical world more and more every day. Most importantly (to the big corporations around the world at least) our money is managed and spent mostly online and the ability to influence or manipulate our purchasing decisions is worth trillions and trillions of dollars. When you see something on YouTube that claims something radical and outlandish, just ask yourself is that right? What are the qualifications of the person making these claims? What ulterior motives are there at play here, what this machine wants me to think? These are critical questions and they are critical for a reason. The how’s, what’s, why’s, [More]
Original post: https://www.gcppodcast.com/post/episode-114-machine-learning-bias-and-fairness-with-timnit-gebru-and-margaret-mitchell/ This week, we dive into machine learning bias and fairness from a social and technical perspective with machine learning research scientists Timnit Gebru from Microsoft and Margaret Mitchell (aka Meg, aka M.) from Google. They share with Melanie and Mark about ongoing efforts and resources to address bias and fairness including diversifying datasets, applying algorithmic techniques and expanding research team expertise and perspectives. There is not a simple solution to the challenge, and they give insights on what work in the broader community is in progress and where it is going.