But what happens when artificial intelligence is biased? What if it makes mistakes on important decisions -- from who gets a job interview or a mortgage to who gets arrested and how much time they ultimately serve for a crime? "These everyday decisions can greatly affect the trajectories of our lives and increasingly, they're being made not by people, but by machines," said UC Davis computer science professor Ian Davidson. A growing body of research, including Davidson's, indicates that bias in artificial intelligence can lead to biased outcomes, especially for minority populations and women. Facial recognition technologies, for example, have come under increasing scrutiny because they've been shown to better detect white faces than they do the faces of people with darker skin.

This is what makes them dangerous.' Previously, I talked about how AI could change the human-machine relationship. Who should be making the decisions in an autonomous car? Should humans always be able to overrule robot decisions? What if you only have a split second to react?

The world of work is evolving, whether we like it or not, and we need to rethink our trust equation with our machines. Kevin Frankowski, a specialist in helping companies with innovation, and I are exploring the evolving world of work. Our most recent consulting assignments bring into sharp relief the work of today, and the likely work of the future. It raises troubling questions about the readiness of industry to address these changes, and how the attitudes of people towards automation are a key block towards advancement. Recall earlier articles in this series where I sketched out a framework for thinking about digital innovation.

The healthcare industry has been around for millennia. Although much has changed in the way it currently functions, it needs to transform into something better to serve the sick. To put it in simpler terms, the healthcare industry needs a new nervous system to function efficiently. This is where artificial intelligence comes into the picture. In healthcare, artificial intelligence has seen a consistent rise in adoption.

In this presentation, I answer the question "when do we trust machines?" In answer to this question, I use a "trust heatmap" in order to illustrate how the answer depends on two key elements: how often machines make mistakes and the costs or consequences of these mistakes. Figure 1: The AI Heat Map: Predictability vs Cost per Error I show that automation occurs when problems cross an "automation frontier" when the risks are sufficiently reduced either through better data and algorithms, or because of regulation or an expression of our preferences. When used in this way, the heatmap can be used to predict what kinds of tasks are currently amenable to automation and those where humans should maintain control. This ideas are presented in TEDx talk, entitled "When Do We Trust Machines?"

Imagine lying on a hospital bed. One leans down to tell you that you are terribly sick and says they recommend a risky procedure as your best hope. You ask them to explain what's going on. Your trust in the doctors ebbs away. Replace the doctors with a computer program and you more or less have the state of artificial intelligence (AI) today.

The healthcare industry has been around for millennia. Although much has changed in the way it currently functions, it needs to transform into something better to serve the sick. To put it in simpler terms, the healthcare industry needs a new nervous system to function efficiently. This is where artificial intelligence comes into the picture. In healthcare, artificial intelligence has seen a consistent rise in adoption.