PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.

PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.

Subscribers:
284,000
Published on ● Video Link: https://www.youtube.com/watch?v=M2-BE5JotjA



Duration: 23:33
12,318 views
580


In the recurring debate about bias in Machine Learning models, there is a growing argument saying that "the problem is not in the data", often citing the influence of various choices like loss functions or network architecture. In this video, we take a look at PAIR's AI Explorables through the lens of whether or not the bias problem is a data problem.

OUTLINE:
0:00 - Intro & Overview
1:45 - Recap: Bias in ML
4:25 - AI Explorables
5:40 - Measuring Fairness Explorable
11:00 - Hidden Bias Explorable
16:10 - Measuring Diversity Explorable
23:00 - Conclusion & Comments

AI Explorables: https://pair.withgoogle.com/explorables/

Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
Parler: https://parler.com/profile/YannicKilcher
LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/
BiliBili: https://space.bilibili.com/1824646584

If you want to support me, the best thing to do is to share out the content :)

If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
Patreon: https://www.patreon.com/yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n




Other Videos By Yannic Kilcher


2021-05-11Research Conference ICML drops their acceptance rate | Area Chairs instructed to be more picky
2021-05-08Involution: Inverting the Inherence of Convolution for Visual Recognition (Research Paper Explained)
2021-05-06MLP-Mixer: An all-MLP Architecture for Vision (Machine Learning Research Paper Explained)
2021-05-04I'm out of Academia
2021-05-01DINO: Emerging Properties in Self-Supervised Vision Transformers (Facebook AI Research Explained)
2021-04-30Why AI is Harder Than We Think (Machine Learning Research Paper Explained)
2021-04-27I COOKED A RECIPE MADE BY A.I. | Cooking with GPT-3 (Don't try this at home)
2021-04-19NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)
2021-04-14I BUILT A NEURAL NETWORK IN MINECRAFT | Analog Redstone Network w/ Backprop & Optimizer (NO MODS)
2021-04-11DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
2021-04-07PAIR AI Explorables | Is the problem in the data? Examples on Fairness, Diversity, and Bias.
2021-03-30Machine Learning PhD Survival Guide 2021 | Advice on Topic Selection, Papers, Conferences & more!
2021-03-23Is Google Translate Sexist? Gender Stereotypes in Statistical Machine Translation
2021-03-22Perceiver: General Perception with Iterative Attention (Google DeepMind Research Paper Explained)
2021-03-16Pretrained Transformers as Universal Computation Engines (Machine Learning Research Paper Explained)
2021-03-11Yann LeCun - Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)
2021-03-06Apple or iPod??? Easy Fix for Adversarial Textual Attacks on OpenAI's CLIP Model! #Shorts
2021-03-05Multimodal Neurons in Artificial Neural Networks (w/ OpenAI Microscope, Research Paper Explained)
2021-02-27GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained)
2021-02-26Linear Transformers Are Secretly Fast Weight Memory Systems (Machine Learning Paper Explained)
2021-02-25DeBERTa: Decoding-enhanced BERT with Disentangled Attention (Machine Learning Paper Explained)



Tags:
deep learning
machine learning
arxiv
explained
neural networks
ai
artificial intelligence
paper
deep learning tutorial
bias in machine learning
ai bias
algorithmic bias
bias in algorithms
garbage in garbage out
the problem is in the data
the problem is not in the data
twitter machine learning
machine learning bias
machine learning in society
ethical ai
ai ethics
ai ethics bias
where does bias come from
google ai