Fair and Square

The concept of fairness in financial lending is thorny, with banks frequently accused of using deceptive or abusive practices. Add machine-learning algorithms into the mix and things can get downright dicey. 

How can a financial institution guarantee that its artificial intelligence is making fair determinations? And what if a rejected borrower asks for the reason she was turned down by the algorithm?

A newsworthy solution

The Associated Press (AP) publishes 2,000 news stories each day, covering everything from international politics to incendiary pop stars, but those articles are only effective if readers can find them.

To help content appear in relevant web searches, the AP applies metadata tags to upwards of 100,000 pieces of news media each day. But, with about 200,000 different tags to choose from (out of billions of possible people, places, and things), and no clear way to ensure metadata accuracy, the tags are sometimes ineffective, preventing relevant stories from reaching readers.

Destination data

With its 28-mile-long azure lake, framed by the snow-capped Alps, the picturesque city of Como, Italy, is a popular vacation destination for entertainment royalty, including George Clooney, Madonna, and Sting.

Paparazzi keep track of these famous visitors, but how can municipal officials monitor the way the other 1 million tourists who visit Como each year travel among the districts and attractions in this medieval city?

Predicting Alzheimer’s

Alzheimer’s disease is the sixth leading cause of death among U.S. adults, yet the chronic neurodegenerative disease has no cure or effective treatment. The key to developing successful therapies likely lies in early detection, since Alzheimer’s causes neurons and synapses to deteriorate slowly and irreversibly.

Pushing boundaries

“Be the smart girl. . .The best way to fight implicit bias is through hard work.”

That was the advice Margo Seltzer, Hershel Smith Professor of Computer Science, offered to more than 100 female technologists, researchers, and students who gathered in Cambridge, Mass., for the global Women in Data Science Conference (WiDS), a daylong event to inspire, connect, and educate women in tech.


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