The Pros and Cons of Using Facial Recognition for Tenant Screening

Landlords Using Facial Recognition for Tenant Screening

From unlocking smartphones to airport security, the convenience and efficiency of facial recognition technology is undeniable. However, as this technology finds its way into various sectors, including tenant screening, questions arise about its reliability and potential for bias.

Facial Recognition Technology in the Housing Market

In the housing market, tenant screening plays a crucial role in determining the eligibility of applicants for rental properties. Traditionally, this process involved background checks, credit history reviews, and references. Some landlords and property management companies use automated systems, including facial recognition technology, to streamline the screening process. While this may seem like a step forward regarding efficiency, it also raises significant concerns regarding accuracy and fairness.

Facial Recognition in Rental Screening Process

One of the primary issues with facial recognition in tenant screening is the potential for errors. Facial recognition algorithms rely on machine learning to identify individuals based on their facial features. While these algorithms have improved over time, they are still prone to inaccuracies. Facial recognition results can be impacted by factors such as lighting conditions, facial expressions, and variations in facial features.

Consequences of Inaccurate Facial Recognition

Inaccurate facial recognition results can have severe consequences for prospective tenants. False positives could lead to qualified applicants being wrongly denied housing opportunities. In contrast, false negatives could result in unqualified individuals gaining access to rental properties. Such errors harm individuals’ rights and contribute to systemic inequalities within the housing market.

Bias in facial recognition technology

Moreover, facial recognition technology has been shown to exhibit bias, particularly concerning race and gender. Studies like the gender shades project have demonstrated that some facial recognition algorithms perform less accurately when identifying individuals with darker skin tones or non-binary gender presentations. This bias can lead to discrimination in tenant screening, marginalizing vulnerable populations.

Proceed with Caution Using Facial Recognition Technology in Tenant Screening

In light of these concerns, policymakers, landlords, and technology developers need to approach the integration of facial recognition in tenant screening with caution. Transparency and accountability are paramount, with clear guidelines to ensure facial recognition systems are used ethically and responsibly. This includes regular audits of algorithms to identify and address biases, as well as mechanisms for individuals to contest and appeal screening decisions.

Don’t Eliminate Human Judgement from the Rental Process

We must also recognize that facial recognition should not entirely replace human judgment in the tenant screening process. While technology can aid efficiency, human oversight is necessary to contextualize screening results and mitigate the risk of errors or biases. By combining the strengths of technology with human judgment, landlords can ensure a more fair and equitable tenant screening process.

Dealing with Errors in Facial Recognition for Tenant Screening

While facial recognition technology holds promise in streamlining tenant screening processes, landlords should cautiously approach this process. The importance of transparency, accountability, and human oversight in facial recognition algorithms is highlighted by the potential for errors and biases. By addressing these challenges, we can harness the benefits of technology while safeguarding individuals’ rights and promoting fairness in the housing market.

If you have been denied an apartment or housing due to errors in tenant screening facial recognition or an inaccurate background check, call us at 1-877-735-8600 or fill out our online form to get a free case review. You may be entitled to damages.