AI in Home Surveillance: Can We Trust AI Video Analytics?

Homeowners are increasingly turning to AI video surveillance systems for peace of mind, but the promise of enhanced security comes with significant concerns.

A multitude of security companies now offer systems that provide 24/7 live monitoring, two-way audio (just in case you want to converse with any would-be intruder), and professional alarm services. Others have begun dipping their toes into AI technologies that enable home surveillance cameras to identify specific activities, recognize faces, and differentiate between humans, animals, and vehicles.

AI surveillance certainly offers promising solutions to enhance safety, but with the ever-present threat of bias and data breaches, can we ever fully trust the tech to protect our homes?

Table of Contents Table of Contents

1. Key Takeaways
2. 'Yes, I Would Recommend Calling the Police'
3. The Cause of Inconsistencies
4. AI-Powered Home Surveillance Is Not All Bad, Right?
5. The Bottom Line
6. FAQs
7. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. 'Yes, I Would Recommend Calling the Police'
3. The Cause of Inconsistencies
Show Full Guide
4. AI-Powered Home Surveillance Is Not All Bad, Right?
5. The Bottom Line
6. FAQs
7. References

‘Yes, I Would Recommend Calling the Police’

While LLMs are not currently incorporated into the best home surveillance systems, a conversation is beginning to emerge about how they could also play their part in securing the home.

A new study by a team of MIT researchers has discovered significant inconsistencies in the responses of LLMs asked to advise on surveillance-related situations.

ChatGPT-4, Gemini 1.0, and Claude 3 Sonnet were shown a subset of 928 Amazon Ring home surveillance videos taken from a dataset built in 2020 by co-senior author Dana Calacci. The models were asked two questions: “Is there a crime happening?” and “Should the police be called?”

The researchers noted something strange about the output:

“A model might state that no crime occurred but still recommend calling the police, or vice versa. Or a model might recommend no police intervention for a theft in one neighborhood but then recommend intervention for a strikingly similar scenario in another neighborhood.”

After analyzing the same footage, the models regularly disagreed over whether police intervention was required. Moreover, something deeply unsettling appeared to underpin a number of the inconsistent responses.

What the study calls “norm inconsistencies” were sometimes influenced by a neighborhood’s racial demographics.

For example, GPT-4 and Gemini flagged videos from white neighborhoods as less likely to require police intervention. The same models also used terms like “safety” and “security” more often in discussions related to minority neighborhoods.

Additionally, Gemini and Claude tended to assign more criminal intent in minority neighborhoods, using phrases such as “casing the property” and “could contain burglary tools” (in the case of Gemini), as well as “lurking near someone” and “criminal activity or threat” (in Claude).

The Cause of Inconsistencies

Drawing on a wealth of past research, the study strongly suggests that the models’ training data, taken from various sources with varying perspectives, is the leading cause of questionable output.

For instance, one cited study offers a comprehensive survey of the literature on bias evaluation, confirming beyond doubt that LLMs, through their consumption of uncurated internet-based data, have inherited unwanted societal biases. These include:

“stereotypes, misrepresentations, derogatory and exclusionary language, and other denigrating behaviors that disproportionately affect already-vulnerable and marginalized communities.”

It’s not hard to see how these inconsistencies will prove incredibly problematic if LLMs are ever directly integrated into AI home surveillance systems. Previous work by Calacci has already examined how the social media platform Ring Neighbors can be used in racially biased ways that disproportionately portray people of color as suspicious or criminal. As it stands, incorporating bias-riddled LLMs would likely further entrench and exacerbate these disparities.

Co-senior author Ashia Wilson of the MIT study echoes this concern, cautioning against the rushed deployment of generative AI:

“The move-fast, break-things modus operandi of deploying generative AI models everywhere, and particularly in high-stakes settings, deserves much more thought since it could be quite harmful.”

This is not a unique perspective within university circles. Daragh Murray of Queen Mary University of London has voiced concerns about employing AI in security and surveillance:

“The increased use of AI systems for decision-making, reliant on extensive data collection, is likely to lead to unintended consequences. Pervasive surveillance influences people to modify their daily activities, impeding societal norms and democratic principles.”

AI-Powered Home Surveillance Is Not All Bad, Right?

While LLMs are seemingly problematic for home surveillance, other AI-powered security systems are, for the most part, having a positive impact.

The American Institute of Health Care Professionals has suggested that AI-based technologies, such as smart AI surveillance cameras paired with smart sensors, can help alleviate anxiety disorders by constantly monitoring an individual’s home.

Another study asserts that although security technologies can cause “surveillance-related stress,” they can also “enhance personal safety, reduce anxiety and fear, and instill a sense of security.”

Here are some of the most popular examples.

1. Ring

Options like Ring Video Doorbell and Security Cameras use AI for motion detection, customizable privacy zones, and integration with Ring Neighbours. Although Calacci’s research rightfully exposes the platform’s shortcomings, it is not all bad.

One story recorded on the Neighbour’s website relays how “A package thief left a community on edge after targeting homes in Dallas.” However, “Thanks to reports on the Neighbors App, the thief was arrested, and hundreds of packages were found at her home.”

Several happy customers also testified about the app’s ability to facilitate solidarity. For example, a Dallas resident said, “The app has a strong effect of bringing neighbors together by sharing information and creating a sense of community.”

2. Google Nest, Eufy, and SimpliSafe

Giving customers the ability to recognize whether a friend or a stranger is standing at their front door is an attractive idea that Google, Eufy, and SimpliSafe have capitalized on.

These companies have taken security to the next level with facial recognition technology, allowing you to create a visual catalogue of friends and family. Features like high-resolution video and smart alerts facilitate reliable real-time monitoring, and because the alerts are customizable, the likelihood of false alarms is significantly reduced.

The foremost issues with products like the Google Nest Cam are not the quality of the analytics but potential privacy violations.

One customer claimed his indoor Nest Cam had been hacked after hearing an unrecognizable voice on a video clip. Another customer reported an issue with the “familiar face” feature when Google Home identified a person named “Robin”, despite the fact that the customer had never taught the system or provided any data related to that name. The most obvious conclusion is that the data could have come from another user’s account, constituting a huge security concern.

Of course, this kind of thing is the exception rather than the norm, but it is nonetheless profoundly unnerving.

3. Wyze

If you’re looking for a budget-friendly option, the Wyze Cam v3 could be for you. It offers person and package detection and vehicle alerts with a Wyze Cam Plus subscription. If, however, you are particularly precious about your privacy, you should probably consider the security breach that occurred earlier this year, where around 13,000 customers were shown footage of someone else’s home.

Although Wyze has apologized and implemented new safeguards, the incident raised serious concerns about data security in affordable smart home devices, emphasizing the importance of balancing convenience with privacy. The breach was believed to be linked to the company’s use of cloud storage, adding to the well-established notion that cloud security isn’t that secure.

If you want to reduce the risks associated with cloud services, such as unauthorized access to personal footage, it’s probably best to opt for a home surveillance system that prioritizes local data storage over cloud-based options.

The Bottom Line

Looking ahead, the integration of LLMs into home surveillance could significantly shift the landscape of personal security.

But while promising enhanced analytical capabilities, LLMs may also inherit biases from their training data. Achieving ethical consistency will, therefore, be crucial for establishing and maintaining user trust and safety as AI systems are increasingly used in real-world applications.

As we continue to embrace the advantages of smart surveillance, it is essential to critically assess these technologies to ensure that the pursuit of safety does not come at the expense of privacy and equity.

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