Do you remember the 2018 Accident Case of Self Driving Uber Car? The car collided with a pedestrian and caused her death. Since then the scrutiny has raised at another level for the security of such autonomous vehicles. Many have claimed that rolling out self-driving cars in the road at this stage is extremely dangerous and criticized the autonomous tech development. However, considering a different angle from a general perspective, National Transportation Safety Board (NTSB) said, "Had the vehicle operator been attentive, she would likely have had sufficient time to detect and react to the crossing pedestrian to avoid the crash or mitigate the impact."

Artificial intelligence promises to augment human capabilities and reshape companies, yet many organizations are finding that the results are falling far short of their expectations. This is frustrating but not surprising. Too often, companies try to implement AI without having a clear understanding of how the technology will interface with people.1 Over the past decade, we have done a number of studies to examine how companies use digital capabilities to become more competitive, including a recent study on human-machine collaboration in a cross-industry setting, where we sought to better understand the contexts in which organizations use particular digital systems.2 In this research, which included more than 20 case studies, we found that many organizations underestimated the value of teaming the predictive capabilities of algorithms with the expertise and intuitions of humans, especially in decision-framing.

Privacy concerns get most of the attention from tech skeptics, but powerful predictive algorithms can generate serious resistance by threatening consumer autonomy. The nexus of big data analytics, machine learning, and AI may be the brightest spot in the global economy right now. McKinsey Global Research estimates that the use of AI will add as much as $13 trillion to global GDP by 2030.1 The noneconomic benefits to humankind will be equally dramatic, leading to a world that is safer (by reducing destructive human error) and offers people a better quality of life (by reducing the time they spend on tedious tasks such as driving and shopping). Even if the coming automation-driven disruption of labor markets is as serious as many fear, we are still, on balance, likely to be better off than today. But not everyone is convinced.

Operational AI systems (for example, self-driving cars) need to obey both the law of the land and our values. We propose AI oversight systems ("AI Guardians") as an approach to addressing this challenge, and to respond to the potential risks associated with increasingly autonomous AI systems.a These AI oversight systems serve to verify that operational systems did not stray unduly from the guidelines of their programmers and to bring them back in compliance if they do stray. The introduction of such second-order, oversight systems is not meant to suggest strict, powerful, or rigid (from here on'strong') controls. Operations systems need a great degree of latitude in order to follow the lessons of their learning from additional data mining and experience and to be able to render at least semi-autonomous decisions (more about this later).

Operational AI systems (for example, self-driving cars) need to obey both the law of the land and our values. We propose AI oversight systems ("AI Guardians") as an approach to addressing this challenge, and to respond to the potential risks associated with increasingly autonomous AI systems.a These AI oversight systems serve to verify that operational systems did not stray unduly from the guidelines of their programmers and to bring them back in compliance if they do stray. The introduction of such second-order, oversight systems is not meant to suggest strict, powerful, or rigid (from here on'strong') controls. Operations systems need a great degree of latitude in order to follow the lessons of their learning from additional data mining and experience and to be able to render at least semi-autonomous decisions (more about this later).