Adam Porter-Price is a leader in our machine intelligence work. He and his team have some concrete steps to share on just how to ethically innovate in the field.
You should review with a diverse group: Technologists should seek opinions from a broad community of technical and business or internal professional users to avoid making an embarrassing or dangerous misapplication of machine intelligence tools. And consider piloting an “inert mode,” running MI tools in pilots parallel to the production environment to compare the results against a human-operated process.
Define easy-to-understand categories of data that are always unacceptable to use. It’s nearly always unacceptable, for example, to include personal health information in a predictive model. This will let both business and technical leaders debate variables using a common language.
Create a mechanism for overseeing the application of MI tools at the executive level, incorporating review by senior business and technology leaders to oversee privacy, security, ethics, assumptions, input data, and operations issues. This group should constantly view review results from MI initiatives and post-mortem pilot programs, and should help promote a common understanding of MI tools and concepts across the organization.
The ethics issues we’ve seen to date in MI have largely been caused by accidents and poor application of techniques, rather than deliberate actions. Doing these three things will substantially reduce the chance of mistakes, and will provide a mechanism for spreading this increasingly useful technology across your enterprise. Machines may be increasing in ability daily, but they will still need to work closely with ingenious humans to answer tough questions over the next century.
So what does MI look like when applied? Here are some of our projects; this year promises to see even more activity, so stay tuned.
We taught a computer to paint Picasso style—and not just images, actual live video. Walking up to the Art Mirror is like falling into a painting, the style of which depends on the filter applied. The most popular is probably Van Gogh’s Starry Night; when you look at yourself in the Mirror, you see your own image inside the world summoned by the artist’s brush – blurry swirls and colors. As you move, you move as some kind of fantastic version of yourself living inside this great work of art. You can also capture an image to email to yourself later.
Our developers took a high-performance machine and told it to look at a series of images and paintings—some by famous artists—others general styles, and filters “learn” how to paint that way. It was computationally intensive to do that and took a couple of days of “machine learning” for the system to process all the data. The inspiration for Art Mirror was an open source project by Gene Kogan, an artist and programmer. Our developers kicked their customized code back to the GitHub community so that others can benefit and improve upon it.
The idea was conceived at Booz Allen’s Summer Games internship, by Michael Jacob, now a consultant, and André Nguyen, now a technologist. The Beat Awake technology pairs up to smartwatches and leverages biometric data to determine anomalies in heartrate, alerting the wearer of the watch when one occurs. The goal is to keep drivers from falling asleep at the wheel. We presented a mobile prototype for an inventive new product at the 2016 Amazon Web Services’ re:Invent conference.
We worked with Topcoder, the largest crowdsourcing community in the world, to host the Topcoder Open at our own DC Innovation Center. This annual event brings together the best coders from across the globe to compete against one another in six different types of competition, including best algorithm, prototype, and user interface design. This year’s Topcoder Open featured 62 competitors representing 26 countries. Six coders walked away winners. Find out who won and more at here.