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One important topic for the field of machine learning is fairness in AI, which has become a table-stake for ML platforms and services, driven by customer / business needs, regulatory / legal requirements and societal expectations. Researchers have been actively studying how to address disparate treatment caused by bias in the data and the resulting amplification of such bias by ML models, and how to ensure that the learned model does not treat subgroups in the population unfairly. During NeurIPS 2020, five Amazon scientists working on these types of challenges gathered for a 45-minute virtual session to address the topic. Watch the recorded panel discussion here, where the scientists discuss how fairness applies to their areas of AI / ML research, the interesting studies and advancements happening in the space, and the collaborations they’re most excited to see occurring across the industry in an effort to advance fairness in AI. Learn more: https://www.amazon.science/videos-webinars/amazon-panel-to-host-virtual-event-on-fairness-in-ai Follow us: Twitter: https://twitter.com/AmazonScience Facebook: https://www.facebook.com/AmazonScience Instagram: https://www.instagram.com/AmazonScience LinkedIn: https://www.linkedin.com/showcase/AmazonScience Newsletter: https://www.amazon.science/newsletter
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
Fairness, Accountability and Transparency in Machine Learning November 18, 2016 Presented By: Google, Microsoft, the National Science Foundation, Data Transparency Lab, NYU Information Law Institute, and NYU Technology Law and Policy Institute 0:00 – Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings – Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai 32:20 – Semantics Derived Automatically from Language Corpora Necessarily Contain Human Biases – Aylin Caliskan-Islam, Joanna J. Bryson, and Arvind Narayanan 1:08:30 – How to be Fair and Diverse? – L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth Vishnoi 1:28:30 – Exploring or Exploiting? Social and Ethical Implications of Autonomous Experimentation in AI – Sarah Bird, Solon Barocas, Fernando Diaz, Hanna Wallach, and Kate Crawford 1:48:10 – Rawlsian Fairness for Machine Learning – Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth
This tutorial was recorded at KDD 2020 as a live, hands-on tutorial. The content is available at https://dssg.github.io/fairness_tutorial/
On Monday, April 15, NYU Stern’s Fubon Center for Technology, Business and Innovation hosted a talk on “AI in Business: Machine Learning, Ethics, and Fairness” by Dr. Solon Barocas.
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!!
Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more. We want to hear from you! Send your thoughts on the year that was 2019 below in the comments, or via twitter @samcharrington or @twimlai. The complete show notes for this episode can be found at twimlai.com/talk/336. Check out the rest of the series at twimlai.com/rewind19!
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
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.
Today we’re joined by Hanna Wallach, a Principal Researcher at Microsoft Research. Hanna and I really dig into how bias and a lack of interpretability and transparency show up across machine learning. We discuss the role that human biases, even those that are inadvertent, play in tainting data, and whether deployment of “fair” ML models can actually be achieved in practice, and much more. Along the way, Hanna points us to a TON of papers and resources to further explore the topic of fairness in ML. You’ll definitely want to check out the notes page for this episode, which you’ll find at twimlai.com/talk/232. We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.
PyData London 2018 Machine learning and data science applications can be unintentionally biased if care is not taken to evaluate their effect on different sub-populations. However, by using a “fair” approach, machine decision making can potentially be less biased than human decision makers. — www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.