Who do you trust? How data is helping us decide

My first lesson in the dangers of trusting strangers came in 1983, not long after I turned five, when an unfamiliar woman entered our house. Doris, from Glasgow, was in her late 20s and starting as our nanny. My mum had found her through a posh magazine called The Lady.

Doris arrived wearing a Salvation Army uniform, complete with bonnet. “I remember her thick Scottish accent,” Mum recalls. “She told me she’d worked with kids of a similar age and was a member of the Salvation Army because she enjoyed helping people. But, honestly, she had me at hello.”

Doris lived with us for 10 months. For the most part she was a good nanny – cheerful, reliable and helpful. There was nothing unusual about her, aside from a few unexplained absences at weekends.

Back then, our neighbours, the Luxemburgs, had an au pair Doris spent a lot of time with. Late one evening, Mr Luxemburg knocked on our door after discovering the pair had been involved in running a drugs ring. “They had even been in an armed robbery,” my father later related, “and Doris was the getaway driver.” The getaway car, it transpired, was our family’s Volvo estate.

My parents decided to search Doris’s room. In a shoebox under her bed, she had stuffed piles of foreign currency, stolen from my parents’ home office. My dad stood on guard by our front door all night with a baseball bat, scared Doris would come home. Thankfully, she didn’t.

“Even as I retell this story, I feel sick,” my mum says. “I left you in the care of a serious criminal. And it took us so long to know who she really was.” Looking back, what would she have done differently? “I wish we’d known more about her.”

My parents are generally smart, rational people. Would they have made the same mistake in today’s digitally connected world? Maybe not. A growing band of technology companies are working on helping us decide who we can and can’t trust – whether hiring a nanny, renting out our home or ordering a taxi. Technology today can dig deeper into who we are than ever before. Can an algorithm determine who is the real deal and who can’t be trusted, better than us?

On a crisp autumn morning, I visit the modest offices of Trooly in Los Altos, a sleepy backwater city north of Silicon Valley. Savi Baveja, Trooly’s CEO, wants to show just how powerful these new trust checks can be. “What do you think of me running you through the Trooly software to see what comes up?’ he says, smiling encouragingly.

I blush, trying to recall all the bad or embarrassing things I’ve ever done. My many speeding and parking tickets? The weird websites I spend time on (for research purposes, of course)? Old photos?

I laugh nervously. “Don’t worry – we can project it on to the large screen so you can see what is happening in real time,” Baveja offers. Somehow I don’t find that reassuring.

Anish Das Sarma, Trooly’s chief technology officer and formerly a senior researcher at Google, types my first and last name into the Instant Trust program, then my email address. That’s it. No date of birth, phone number, occupation or address.

“Trooly’s machine learning software will now mine three sources of public and permissible data,” Baveja explains. “First, public records such as birth and marriage certificates, money laundering watchlists and the sex offender register. Any global register that is public and digitised is available to us.” Then there is a super-focused crawl of the deep web: “It’s still the internet but hidden; the pages are not indexed by typical search engines.” So who uses it? “Hate communities. Paedophiles. Guns. It’s where the weird people live on the internet.”

The last source is social media such as Facebook and Instagram. Official medical records are off limits. However, if you tweeted, “I just had this horrible back surgery,” it could be categorised as legally permissible data and used. Baveja and his team spent nine months weighing up what data they should and should not use. Data on minors was out.