Software makers offer more transparent machine-learning tools—but there’s a trade-off.
Artificial intelligence software can recognize faces, translate between Mandarin and Swahili, and beat the world’s best human players at such games as Go, chess, and poker. What it can’t always do is explain itself.
AI is software that can learn from data or experiences to make predictions. A computer programmer specifies the data from which the software should learn and writes a set of instructions, known as an algorithm, about how the software should do that—but doesn’t dictate exactly what it should learn. This is what gives AI much of its power: It can discover connections in the data that would be more complicated or nuanced than a human would find. But this complexity also means that the reason the software reaches any particular conclusion is often largely opaque, even to its own creators.
For software makers hoping to sell AI systems, this lack of clarity can be bad for business. It’s hard for humans to trust a system they can’t understand—and without trust, organizations won’t pony up big bucks for AI software. This is especially true in fields such as health care, finance, and law enforcement, where the consequences of a bad recommendation are more substantial than, say, that time Netflix thought you might enjoy watching The Hangover Part III.
Regulation is also driving companies to ask for more explainable AI. In the U.S., insurance laws require that companies be able to explain why they denied someone coverage or charged them a higher premium than they did their neighbor. In Europe, the General Data Protection Regulation, which took effect in May, gives EU citizens a “right to a human review” of any algorithmic decision affecting them. If the bank rejects your loan application, it can’t just tell you the computer said no—a bank employee has to be able to review the process the machine used or conduct a separate analysis.
Continue reading your story on the app
Continue reading your story in the magazine
The Novo Abides
Mike Novogratz, former Goldman Sachs partner and the loudest crypto bull on Wall Street, has made and lost a fortune on Bitcoin. He’s still all-in.
Weaponizing Uncertainty
Trump’s tariffs were meant to make the U.S. a safe option for businesses looking to expand. It’s not working out that way.
The Immigration Hackers
An Obama-era department of techies is working to streamline paperwork from inside the Trump administration. Results are mixed.
Letter From London, March 2029
A message from post-Brexit Britain about what the 2019 split from the EU hasn’t delivered.
Saudi Weighs More Bank Mergers
Gulf bank consolidation trend gathers pace with Saudi plans.
Money Markets Mixed Over Fed Rate Hikes
There is little agreement among commentators as to whether the Fed will continue to hike rates in 2019.
Hey, Paul Ryan, What's Up? Budget Deficits
The professed fiscal hawk leaves behind a legacy of unsustainable shortfalls.
Dell EMC Bullish On 2019 Outlook
Cloud and technology giant plans further expansion in the Middle East.
Artificial Intelligence Has Some Explaining To Do
Software makers offer more transparent machine-learning tools—but there’s a trade-off.
What Buffalo Got, And Didn't Get, When Tesla Came To Town
After $750 million in subsidies and years of delays, critics say Elon Musk hasn’t done enough for his solar panel factory
Travel
01 Hipcamp
Wellness
01 Peloton
Space
01 / SpaceX For flying past competitors in the space race by launching astronauts for NASA
Robotics
01 DroneSeed
More Than a Startup
LeBron James and Maverick Carter’s SpringHill Company has become a media and branding juggernaut that empowers communities and is built for the future.
FALLING IN LOVE
LEMMINGS still haunts my nightmares almost three decades later.
Energy
01 SparkMeter
QUAKE
The blueprint for 3D shooters has lots to teach in 2021.
Security
Camille François Chief innovation officer at Graphika
LORD OF CHAOS
How XCOM’s JULIAN GOLLOP improved on the board games he loved