When I first drove the Tesla Model 3 in March 2018, I attracted so much attention from the denizens of Los Angeles that it bordered on embarrassing. The hoi polloi of Beverly Hills walked right by Lamborghinis and Aston Martins to ogle the Tesla. They even wandered into traffic to get a closer look at Elon Musk's new, more affordable, supposedly mainstream sedan. Nine months later, Angelenos have returned to their car-jaded selves. You can't spend five minutes in LA without spotting a Model 3.

Google's AI chief isn't fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care").

Google's AI chief isn't fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care").

Google's AI chief isn't fretting about super-intelligent killer robots. Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care").

Instead, John Giannandrea is concerned about the danger that may be lurking inside the machine-learning algorithms used to make millions of decisions every minute. "The real safety question, if you want to call it that, is that if we give these systems biased data, they will be biased," Giannandrea said before a recent Google conference on the relationship between humans and AI systems. The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. Some experts warn that algorithmic bias is already pervasive in many industries, and that almost no one is making an effort to identify or correct it (see "Biased Algorithms Are Everywhere, and No One Seems to Care"). "It's important that we be transparent about the training data that we are using, and are looking for hidden biases in it, otherwise we are building biased systems," Giannandrea added.

Elon Musk recently commented on the need to regulate AI, citing it as an existential risk for humanity. As is the case with any human creation, the increasing leverage technology affords humans can certainly be used for good or evil, but the premise that we need to fear AI and regulate it this early in its development is not well founded. The first question we might consider is whether what we fear is the apathy or malevolence that AI might evolve. I bring this up because Musk himself has previously referred to the development of AI as "summoning the demon," associating the imagery of evil with it. Any honest assessment of the history of mankind shows us that the most shockingly malevolent intent can arise from human hearts and minds.