Virgin Holidays used AI to prove the value of overhauling its email emarketing as it prepared for a full-scale transformation of how it communicates with consumers. Saul Lopes, Virgin Holidays' customer lifecycle lead, admitted that when he first joined three years ago its email marketing setup was "complex"; it was using three different tools to send emails, it had a marketing and CRM team without any data analysts, it was only able to do limited personalisation and there was "no culture of testing". He wanted to do a full digital transformation of how the function worked, but with his plan costing lots of money and requiring a huge shift in skills and mindset, he was instead tasked with proving what success would look like on a small scale before the company would commit. The AI took away all of the bullsh*t. We are no longer led by human ego or human bias but by numbers and results.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.

In a recent op-ed for The New York Times, Microsoft Principal Researcher Kate Crawford discusses what she sees as the a very real problem with artificial intelligence today: "Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to." Below are a few excerpts from "Artificial Intelligence's White Guy Problem." Police departments across the United States are also deploying data-driven risk-assessment tools in "predictive policing" crime prevention efforts. In many cities, including New York, Los Angeles, Chicago and Miami, software analyses of large sets of historical crime data are used to forecast where crime hot spots are most likely to emerge; the police are then directed to those areas. At the very least, this software risks perpetuating an already vicious cycle, in which the police increase their presence in the same places they are already policing (or overpolicing), thus ensuring that more arrests come from those areas.

Artificial intelligence (AI) may be worsening inequality, given the biases being embedded within the underlying machine-learning algorithms, writes Kate Crawford, a principal researcher at Microsoft and co-chairwoman of a White House symposium on society and AI. She cites one case in which Google's photo application was found to classify images of black people as gorillas as an example of systems with prejudices built in. An even more pernicious example was referenced in a recent ProPublica investigation, which found popular software used to evaluate the probability of criminal recidivism was twice as likely to erroneously assign a high risk to black defendants and a low risk to white defendants. Crawford says AI reflects the values of those who create it, and inclusivity must be accounted for to avoid machine intelligences that mirror a narrow and elite perception of society. "We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future," Crawford warns.

ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about "the singularity" -- when machines become smarter than humans -- have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional.