Tech Inclusion and Diversity in Machine Learning
Published: 2017-03-27 19:20:37

---
title: Tech Inclusion and Diversity in Machine Learning 
published: true
description: 
cover_image: http://blog.clarifai.com/wp-content/uploads/2016/06/pexels-photo-26135-1-uai-1440x957.jpg
tags: artificial intelligence, machine learning, deep learning, neural networks
---

Inclusion means embracing diversity - people from different backgrounds with different skill sets coming together to make our company stronger. While we believe a culture of [inclusion should be table stakes for any tech company](https://thenextweb.com/opinion/2016/01/12/the-elephant-in-the-valley/#.tnw_ZuBOdZ6K), we think itâ€™s particularly salient for machine learning and artificial intelligence (A.I.) companies. After all, weâ€™re building the next generation of smart apps and machines that are supposed to amplify human intelligence - and you canâ€™t amplify human intelligence without including perspectives from all facets of human life.

![](http://blog.clarifai.com/wp-content/uploads/2016/06/zuckerberg.png)

Itâ€™s not news that [the tech industry has a diversity problem](http://mashable.com/2016/01/18/silicon-valley-diversity/#dSAimx4U5Zqn). However, artificial intelligence represents a new homogeneity problem in the tech sector. A.I. relies on human training to get â€œsmart.â€ Thus, human biases can factor into artificial intelligence and how computers see the world. The danger, of course, being that biased views can be amplified with a far greater reach through A.I. than through any one human being.

> â€œIf everyone teaching computers to act like humans are men, then the machines will have a view of the world that's narrow by default and, through the curation of data sets, possibly biased.â€

With that in mind, we try to build diversity into every part of our business at Clarifai, from hiring to building to shipping product. Diversity in A.I. doesnâ€™t just mean hiring more diverse people. For image recognition, in particular, data scientists need a set of well-labeled images to train a model, which means someone has the manual task of categorizing those images in the first place. Building a diverse data team means looking at the problem as a whole, from the time that someone labels an image to the time a data scientist trains the model. Thatâ€™s why we source data from men and women across six continents!

Weâ€™re excited that the [Global Entrepreneurship Summit](https://www.tech-inclusion.org/#pledge) has given us the opportunity to commit officially to our goal of promoting inclusion and diversity, and serve as an example to others in the machine learning space. As part of our pledge, weâ€™re bound to:

* implement and publish company-specific goals to recruit, retain, and advance diverse technology talent, and operationalize concrete measures to create and sustain an inclusive culture;
* annually publish data and progress metrics on the diversity of our technology workforce across functional areas and seniority levels;
* invest in partnerships to build a diverse pipeline of technology talent to increase our ability to recognize, develop and support talent from all backgrounds

If you believe in building **#RobotsNotBrobots**, [take the pledge](https://www.tech-inclusion.org/#pledge) with us and let us know!