Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade.

We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images.

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Commercial face-analyzing systems have been critiqued by scholars and activists alike over the past decade, if not longer. A paper last fall by University of Colorado, Boulder researchers showed that facial recognition software from Amazon, Clarifai, Microsoft, and others was 95% accurate for cisgender men but often misidentified trans people. Furthermore, independent benchmarks of vendors' systems by the Gender Shades project and others have revealed that facial recognition technologies are susceptible to a range of racial, ethnic, and gender biases. Companies say they're working to fix the biases in their facial analysis systems, and some have claimed early success.

"Computer vision and machine learning have really started to take off, but for most people, the whole idea of what a computer is seeing when it's looking at an image is relatively obscure." The wonderful field of Computer Vision has soared into a league of it's own in recent years. There are an impressive number of applications already in wide use around the world – and we are just getting started! One of my favorite things in this field is the idea of our community embracing the concept of open source. Even the big tech giants are willing to share new breakthroughs and innovations with everyone so that the techniques do not remain a "thing of the rich".