Too Much Information: The Hidden Liability in AI-Powered Fleet Safety

Recent press has highlighted litigation and other challenges that Flock and some dash camera providers are facing. The reporting has largely focused on two elements: 1) alleged use of AI-enabled readers and cameras to serve as unauthorized Automated License Plate Readers (ALPRs), and 2) alleged privacy violations, including those due to driver-facing cameras capturing drivers’ biometric information.

The allegations point to potential violations of federal and state law and understandably pose broader questions regarding the tradeoff between public safety and individual privacy rights. While the discourse to date has largely focused on these points, there are broader underlying points regarding AI, and information availability in general, that warrant further consideration.

What follows is not intended to say that AI is a bad thing per se. Clearly, the technology has already driven material gains and improvements in many areas. However, this does not mean that thought and caution should be cast to the wind. Not every application will benefit from AI at this stage. The technology is evolving quickly. The potential is significant, but AI’s full implications are still being understood. Until there is greater clarity, a measured approach is likely the prudent one.

AI consumes and analyzes a tremendous amount of data. The pace of AI development is also remarkable. Considering these factors, it is difficult, if not impossible, for mere mortals to fully understand the scope of what data the AI was trained on and what it might be doing in a given application. AI can also bias towards “telling you what you want to hear”, and as demonstrated by recent incidents like the OpenAI / Hugging Face jailbreak, AI may not follow the user’s intended path.

The Other Risk: Too Much Information

There is another aspect of AI that can pose a significant challenge to vehicle risk management: Too Much Information.

Certainly, the litigation referenced above relates to TMI. There is also a less insidious version of TMI.

More Events Mean More Obligation

AI can be leveraged in dash cameras to capture “events” like tailgating, near-misses, cell phone use, drowsiness, traffic signal violations, etc. Many camera manufacturers push to detect more and more events. There is a catch to this event proliferation. Someone needs to review each event to determine whether it represents a legitimate point of concern, and if so, then take corrective action.

Fleets are implementing more and more advanced technology in an effort to improve safety, which is laudable. However, the flip side of this is that doing so is also an implicit acknowledgement that there are issues that need to be addressed. In turn, this acknowledgement creates a de facto obligation for fleets to take action when problems are discovered (and document having done so). Failure to do so can have severe ramifications when an accident takes place.

Let’s say that Driver X gets into an accident and litigation ensues. In discovery, previously collected data shows that Driver X has had a track record of unsafe behavior behind the wheel. Unless documented corrective action had been taken, Driver X’s employer just handed the case to the plaintiff attorney with a bow on it.

The adage that the only thing worse than not having a policy is having a policy and not following it comes to mind.

For a company to avoid adverse outcomes post-accident, someone had to take prior actions based upon the data and events their systems generate. More events create a burden for more action. The potential workload is not trivial, particularly for smaller organizations where the person charged with risk management is wearing multiple other hats.

Certainly, AI did not create this issue. AI has, however, expanded the potential scope of a company’s obligations.

Turning Information Into Action

So, what to do? One potential path is to keep things simple and reduce the volume of information that needs to be examined.

Over the past two decades, we have consistently found that reducing speeding is highly correlated with reducing other driving issues. Our data continues to support this, even in an AI world.

All things AI are not inherently bad. There is tremendous value that it can unlock. Nevertheless, it is vital to understand what else comes along with the AI package and to be thoughtful before enabling additional features. What feature(s) would actually be most helpful? Can you handle the volume of events to review?

Perhaps most importantly, do you have the bandwidth to take action?

These tools are not going away, nor should they. But successful safety programs will not be defined by how much technology they deploy or how much information they collect. They will be measured by how thoughtfully that technology is used and whether the information it generates can be turned into action.

If you don’t take action, you can bet that a plaintiff attorney will.