The expanded use of open data, sensors, and artificial intelligence brings new opportunities — and challenges.

Today, all businesses are data businesses, and the ability to recognize meaningful patterns from non-traditional data sources can be a competitive advantage. Data from a traffic and navigation app like Waze can help cities improve emergency response times. Biometric data from a wearable device like an Apple Watch can identify, detect, and manage disease more effectively. Location and payment data from smartphones can help investors understand real-time foot traffic and transactions in retail stores. And we’re just getting started.

With new approaches come new responsibilities. It’s easy to see how data science can be used for good, but it’s also easy to imagine negative consequences. Earlier this month, former U.S. Chief Data Scientist DJ Patil called on data scientists to collaboratively develop a Code of Ethics for data sharing. The movement seeks to empower people working on technology to speak up before dangerous products are released, and would serve as “a kind of digital Hippocratic oath” to “do no harm.”

In advance of our Lab Session, a panel discussion held in partnership with NYC’s Open Data Week, we’ve selected articles and resources that show the potential — and the risks — of using data from unexpected sources in the pursuit of innovation.

Government, policy, and urban life

• Scientists know how you’ll respond to nuclear war — and they have a plan (Wired) — Researchers at the Biocomplexity Institute of Virginia Tech are using data from more than 40 sources — including smartphones, satellites, remote sensors, and census surveys — to simulate how survivors in Washington, D.C., might behave in the 36 hours after the detonation of a nuclear bomb.
• SimPolicy: Smarter policy through simulation (Nesta) — The use of data-driven simulations in government can go beyond disaster preparedness and war games. Simulation technologies, backed by increasingly robust datasets, can help policymakers anticipate the implications of regulations and other complex decisions.
• Traffic’s mind-boggling economic toll (CityLab) — Data can also help policymakers understand a problem before proposing solutions. The largest-ever study of global traffic data found that congestion costs the United States $305 billion each year.
• An AI accurately guessed race and voting patterns by counting cars on Google Street View (Gizmodo) — By using “deep learning-based computer vision” combined with “a training set for a computer model with census data” from 35 cities, Stanford AI researchers have developed a nearly real-time method for estimating income, race, education, and voting patterns in different neighborhoods.
• What 100 cities are learning from each other by sharing their data (Fast Company) — Bloomberg Philanthropies launched its What Works Cities program in 2015, designed to teach cities how to use data to improve residents’ lives. Today, 100 cities are part of the program, sharing data related to issues like homelessness and eroding trust in law enforcement.

Commerce and finance

• The Next Wave: Predicting the future of coffee in New York City (Topos on Medium) — Coffee shops are “a small piece of the much larger puzzle that is contemporary urban living” and Topos is using data from dozens of sources — plus machine learning, spatial economics, urbanism, and some mesmerizing animated GIF maps — to better understand the location and concentration of businesses.
• Foursquare unleashes location data dashboard for retailers and brands (Adweek) — Once a social check-in app, Foursquare now powers location data in other popular apps and uses the data to provide “Google Analytics for the real world” in bricks-and-mortar businesses like stores and restaurants. The location analytics have been used to track a decline in the U.S. share of international tourism and an increase in Whole Foods foot traffic after Amazon’s takeover.
• Selling data to feed hedge fund computers is one of the hottest areas of finance right now (Quartz) — Quantitative investing uses AI to parse large amounts of data to identify meaningful patterns, helping investment banks and hedge funds make better and faster decisions. It’s driving growth in the “alternative data” market, which collects and processes everything from credit card transactions to customer reviews.

Digital biomarkers

• ‘Smart thermometers’ track flu season in real time (New York Times) — More than 500,000 U.S. households now own Kinsa’s smartphone-connected thermometer, and the company gets 25,000 readings each day. Founder Inder Singh says Kinsa can identify flu trends earlier than the CDC and more accurately than Google.
• The emerging influence of digital biomarkers on healthcare (Rock Health) — The increase in connected devices, apps, and sensors has created an enormous amount of physiological and behavioral data. When that data connects to a health outcome, it becomes a “digital biomarker” — a new class of information that could have a huge impact on pharmaceutical companies, healthcare providers, and patients.
• Can your iPhone tell if you’re depressed? (Chicago Tribune) — University of Illinois at Chicago researchers found that smartphone behavior data, including typing speed, correlate with manic and depressive episodes. They developed an app, BiAffect, that went on to win the Robert Wood Johnson Foundation’s Mood Challenge, an open innovation prize competition designed and powered by Luminary Labs.
• This start-up founder suffers chronic pain — now she’s on a mission to figure out how to measure it (CNBC) — Pain is subjective, and measuring it effectively is a persistent challenge. Evidation Health, a digital health startup, is exploring the use of data from wearable devices and other sensors to track chronic pain.
• How companies scour our digital lives for clues to our health (New York TImes) — Digital phenotyping is an emerging field that uses interactions with digital devices to assess health risks. In just one example, Facebook is using AI to scan social posts for language that might indicate suicidal thoughts.

Ethical considerations

• If your Apple Watch knows you’ll get diabetes, who can it tell? (The Outline) — Wearables like the Apple Watch have the potential to transform healthcare. But many popular devices and apps haven’t yet been classified by the FDA as medical devices, and therefore aren’t HIPAA-compliant. Many questions about privacy and security remain unanswered.
• U.S. soldiers are revealing sensitive and dangerous information by jogging (Washington Post) — Strava, a fitness tracking app, made headlines earlier this year when an Australian student discovered the “Global Heat Map” of user activity unwittingly revealed the location and security of U.S. military bases around the world.
• The new ‘digital’ sanctuaries (CityLab) — Cities that have made data easy to access are facing difficult questions about protecting vulnerable communities from surveillance technology. What do responsible data collection policies look like in a sanctuary city?
• Your car company may know more about you than your spouse (Washington Post) — Vehicles have relied on computerized systems for decades, but until recently, the data those systems collected would stay inside the car. Today, 78 million connected cars are collecting personal data — including location information — and drivers are concerned about privacy.

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Photo by Sawyer Bengtson on Unsplash.