Addressing Racial Bias in AI: A Guide for Curious Minds

It’s no secret that humans are fallible and susceptible to biases, nor it is a secret that these biases can influence algorithms to behave in discriminatory ways. However, it is hard to get a sense of how pervasiveness these biases are in the technology that we use in our everyday lives. In today’s technology-driven world, we need to critically think about the impact of artificial intelligence on society and how it intersects gender, class and race.

Speaking of race — just imagine being wrongfully arrested because of racial biases in algorithms. That’s exactly what happened to Robert Julian-Borchak Williams — an African American in Michigan who was arrested from his home in front of his wife and young children. He didn’t commit any crime but a facial recognition software used by the police suspected him for shoplifting. His experience of being wrongfully jailed epitomizes how flawed technology in the hands of law enforcement can magnify the discrimination against black communities.

Biases in facial recognition technology is now trending all over the media. Recently, a number of tech companies (including giants like Amazon, IBM, Microsoft etc.) made announcements of ceasing the design and development of facial-recognition services or products and stop selling them to state and local police departments and law-enforcement agencies. Several researchers have pointed out the limitations and inaccuracies of these technologies and voiced concerns on how it can perpetuate discrimination and racial profiling. Robert Junior’s case clearly shows that, while these decisions by technology giants can be considered as baby steps towards the right direction, these will clearly not solve the problem of racism of science that is deeply rooted in its history.

#equity #society #ethics #facial-recognition #ai

What is GEEK

Buddha Community

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Addressing Racial Bias in AI: A Guide for Curious Minds

It’s no secret that humans are fallible and susceptible to biases, nor it is a secret that these biases can influence algorithms to behave in discriminatory ways. However, it is hard to get a sense of how pervasiveness these biases are in the technology that we use in our everyday lives. In today’s technology-driven world, we need to critically think about the impact of artificial intelligence on society and how it intersects gender, class and race.

Speaking of race — just imagine being wrongfully arrested because of racial biases in algorithms. That’s exactly what happened to Robert Julian-Borchak Williams — an African American in Michigan who was arrested from his home in front of his wife and young children. He didn’t commit any crime but a facial recognition software used by the police suspected him for shoplifting. His experience of being wrongfully jailed epitomizes how flawed technology in the hands of law enforcement can magnify the discrimination against black communities.

Biases in facial recognition technology is now trending all over the media. Recently, a number of tech companies (including giants like Amazon, IBM, Microsoft etc.) made announcements of ceasing the design and development of facial-recognition services or products and stop selling them to state and local police departments and law-enforcement agencies. Several researchers have pointed out the limitations and inaccuracies of these technologies and voiced concerns on how it can perpetuate discrimination and racial profiling. Robert Junior’s case clearly shows that, while these decisions by technology giants can be considered as baby steps towards the right direction, these will clearly not solve the problem of racism of science that is deeply rooted in its history.

#equity #society #ethics #facial-recognition #ai

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
• Slowing Down the Weight Norm Increase in Momentum-based Optimizers
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
• Coherent Reconstruction of Multiple Humans from a Single Image
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
• Are we done with ImageNet?
• How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
• Background Modeling via Uncertainty Estimation for Weakly-supervised Action Localization
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
• Learning a Unified Sample Weighting Network for Object Detection

Articles

• Deepfake Detection Challenge Results: An open initiative to advance AI
• Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
• NeoML Released as TensorFlow Alternative
• Google Meet takes on Zoom with AI-powered noise cancellation
• Open Compound Domain Adaptation
• Dense pose for animal classes with transfer learning
• 10 Ways Enterprises Are Getting Results From AI Strategies
• An understanding of AI’s limitations is starting to sink in
• Why we switched from Flask to FastAPI for production machine learning
• Simple Transformers
• The key differences between rule-based AI and machine learning
• Deep learning-based surrogate models outperform simulators and could hasten scientific discoveries
• HANDS-ON GUIDE TO OBJECT DETECTION USING YOLO
• Artificial neural networks that mimic the flexibility and computing power of the brain
• MIT researchers train AI to predict how humans paint works of art
• What Do Machine Learning Engineers Do Anyway?
• AI Is Amazing But Complicated, And We Don’t Necessarily Need To Plunge In Headfirst
• Deep learning networks track up to 100 animals simultaneously
• Is artificial intelligence changing art?

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap