Apple today launched a new tool for iPhones to help reduce what a thief with your phone and passcode can access. The feature, called Stolen Device Protection, adds extra layers of protection to your iPhone when someone tries to access or change sensitive settings on your device. If someone tries to access passwords stored in Apple's keychain, for instance, they won't be able to unless they also use a fingerprint or the phone's face recognition to prove they're the legitimate owner. You don't need to look far to find stories of stolen phones. In London, a phone is stolen every six minutes.

Advocates of algorithmic justice have begun to see their proverbial "days in court" with legal investigations of enterprises like UHG and Apple Card. The Apple Card case is a strong example of how current anti-discrimination laws fall short of the fast pace of scientific research in the emerging field of quantifiable fairness. While it may be true that Apple and their underwriters were found innocent of fair lending violations, the ruling came with clear caveats that should be a warning sign to enterprises using machine learning within any regulated space. Unless executives begin to take algorithmic fairness more seriously, their days ahead will be full of legal challenges and reputational damage. In late 2019, startup leader and social media celebrity David Heinemeier Hansson raised an important issue on Twitter, to much fanfare and applause.

This Week Top Automation Articles highlights the potential of a low-code/no-code platform in Business and why Kate Crawford, writing in his book that technology experts are misunderstanding the concept of Artificial Intelligence. The introduction of the new Apple card family is really an exciting thing and why big brands like Gucci are not realizing the worth of Cryptocurrency. There is much more to explore. Let's dive into the Automation World! The potential for low-code/no-code platforms is enormous.

Today, machine learning and artificial intelligence systems, trained by data, have become so effective that many of the largest and most well-respected companies in the world use them almost exclusively to make mission-critical business decisions. The outcome of a loan, insurance or job application, or the detection of fraudulent activity is now determined using processes that involve no human involvement whatsoever. In a past life, I worked on machine learning infrastructure at Uber. From estimating ETAs to dynamic pricing and even matching riders with drivers, Uber relies on machine learning and artificial intelligence to enhance customer happiness and increase driver satisfaction. Frankly, without machine learning, I question whether Uber would exist as we know it today.

Few leaders would dispute the fact that business today is driven by data and smart algorithms. Yet, rather than real digital transformation, many instead pursue digital incrementalism, using automation to cut costs or, worse -- cut jobs. Doing so might buy you some time from impatient shareholders, but it will be short-lived unless you can face the challenge: How do you reimagine what you do for a new era of AI-powered competition? The high unemployment numbers of the Covid-19 recession have obscured a systemic problem: the accelerating effect of automation on the workforce. We have been here before.

When we think of artificial intelligence, the first things that usually come to mind are depictions from popular culture. Artificial intelligence (AI), however, doesn't just exist in futuristic movies. It's already a part of the way we live, shop, work and bank. Rather than a robot gone rogue, AI is simply technology programmed by humans with the ability to memorize information, learn from experience, communicate facts, and/or make decisions. And because humans are the ones creating AI, we must ask the question: what are we teaching our machines, and what are they learning from us?

Business and IT leaders are realizing that artificial intelligence should be implemented carefully to avoid unwanted results. In the race to implement AI over the past several years, most organizations have not addressed risk management adequately, and in some cases, the oversight has resulted in headline news that the company could have avoided if it had exercised more care. People are paying attention to those headlines and taking them to heart. AI has moved from the initial hype phase, in which proponents tend to focus only on the positive aspects, to what Gartner calls the "trough of disillusionment" or Geoffrey Moore called "the chasm" in which the drawbacks of the technology become too apparent to ignore and cause organizations to proceed with greater caution. Bias is AI's Achille's heel, and it's been the 800-pound gorilla in the room waiting to be recognized by people other than data scientists.

Can a credit card be sexist? It's not a question most people would have thought about before this week, but on Monday, state regulators in New York announced an investigation into claims of gender discrimination by Apple Card. The algorithms Apple Card used to set credit limits are, it has been reported, inherently biased against women. Tech entrepreneur David Heinemeier Hansson (@DHH) claimed that the card offered him 20 times more credit than his wife, even though she had the better credit score, while Apple's own co-founder Steve Wozniak went to Twitter with a similar story, despite he and his wife sharing bank accounts and assets. Goldman Sachs, the New York bank that backs the Apple Card, released a statement rejecting this assertion, saying that when it comes to assessing credit, they "have not and will not make decisions based on gender."

Jamie Heinemeier Hansson had a better credit score than her husband, tech entrepreneur David. They have equal shares in their property and file joint tax returns. Yet David was given permission to borrow 20 times the amount on his Apple Card than his wife was granted. The situation was far from unique. Even Apple's co-founder Steve Wozniak tweeted that the same thing happened to him and his wife despite having no separate bank accounts or separate assets.

Catalyst president and CEO Lorraine Hariton, who works with Fortune 500 companies to eliminate bias in their technology and systems, gives her thoughts on the controversy surrounding gender and the new Apple Card's algorithm. She says artificial intelligence can become biased if leaders and teams aren't diverse and inclusive. The Apple Card gender bias allegation is a lesson for Silicon Valley, which has suffered from sexism issues for a long time, according to one tech expert. Apple made headlines Sunday when the artificial intelligence algorithm behind its new Apple Card, in partnership with Goldman Sachs, was accused of gender discrimination after Apple co-founder Steve Wozniak and another male tech entrepreneur said they got much higher lines of credit for their card applications than their wives did. Catalyst president and CEO Lorraine Hariton, who works with Fortune 500 companies to eliminate bias in technology and systems, joined FOX Business' Liz Claman on Friday and said she was not surprised by the accusation.