Technology | January 6th, 2024

The Flawed Impact of Police AI Racial Recognition on Minority Communities

By: Tianna Fannell

Facial recognition technology has become an increasingly prevalent tool in our society, with applications ranging from law enforcement and security to social media platforms.

While the potential benefits of this technology are evident, there is a darker side to its use that cannot be ignored.

As artificial intelligence continues to develop, the flaws in facial recognition technology have become more apparent, particularly in its impact on minority communities. One of facial recognition technology’s most pressing concerns is its inherent bias.

Candice Simms-Rose is a mother fraught with anxiety over the societal challenges her son may face as he grows older, especially in a world where the misuse of AI could potentially bring him harm because of the color of his skin. 

Her fears are not unfounded, as technology has been shown to have discrimination issues, which could affect her son as he becomes a Black man in society.

“It’s crazy that this can be an additional concern,” Simms-Rose said. “I am so worried – as a mother of a future Black man, I’m already scared.” 

Simms-Rose highlights the added layer of distress the potential misuse of AI technology adds to the preexisting fears of parenting a child in a minority community.

Understanding the Biases within Police AI Racial Recognition

The implications of these biases are significant, especially in the context of law enforcement. Police departments across the country have increasingly turned to facial recognition technology as a tool for identifying and apprehending suspects.

A study conducted by the National Institute of Standards and Technology regarding facial recognition algorithms and racial biases is crucial in understanding the drawbacks of police AI racial recognition. The study revealed the inherent biases within these algorithms, particularly in their higher misidentification rates among people of color. 

This troubling revelation has profound implications for law enforcement agencies utilizing these technologies.

 For instance, a case in New Jersey gained national attention when a man was wrongfully arrested due to a flawed facial recognition match. The man, a person of color, was mistakenly identified as a suspect in a crime he was not involved in. This incident underscores the potential consequences of racial biases within facial recognition technology, particularly when it comes to policing. 

The use of these flawed algorithms has the potential to perpetuate systemic injustices and disproportionately impact marginalized communities.

According to the research conducted by NIST on Facial Recognition Technology, it was found that there are higher false positive rates for women, African Americans, and especially for African American women in most algorithms. However, the study also found that some one-to-many algorithms demonstrated similar false positive rates across these specific demographics.

The reliance on biased AI systems within law enforcement can have detrimental effects on the trust between police and the communities they serve.

This issue is a serious concern as it can lead to unjust treatment and unwarranted suspicion of individuals solely based on their gender or race. It not only perpetuates discrimination and prejudice but also undermines the fairness and effectiveness of law enforcement. 

It is imperative for law enforcement agencies to address and rectify these biases in facial recognition technology to ensure equal and just treatment of all individuals, regardless of their demographic characteristics. It is also crucial for policies and regulations to be put in place to prevent the misuse and abuse of such technology. 

Only through these measures can we hope to rebuild and strengthen the trust between law enforcement and minority communities.

 The study’s results serve as a wake-up call for the law enforcement community to address the inherent racial biases within their AI technologies. 

The fact that flawed facial recognition technology has led to wrongful arrests, as highlighted in a report published by the American Civil Liberties Union, underscores the pressing need for rectification. The implications of these biased algorithms go beyond mere misidentification. They have real-life consequences that can result in the unjust targeting and persecution of individuals from marginalized communities.

The Human Cost: Injustices and Unintended Consequences

The use of flawed AI facial recognition poses significant risks to individuals, particularly those from marginalized communities. 

Inaccurate identification of suspects can lead to wrongful accusations and arrests, perpetuating systemic injustices and deepening the mistrust between law enforcement and minority groups. 

This violates the rights of innocent individuals and undermines the integrity of the criminal justice system as a whole. When individuals within minority communities are disproportionately targeted or wrongfully accused due to flawed facial recognition technology, it further damages the already delicate relationship between law enforcement and these communities.

The surveillance in these areas exacerbates the stigmatization and intimidation experienced by minority groups, leading to a chilling effect on their freedom of movement and expression. Misusing facial recognition technology can potentially entrench existing power imbalances and discrimination further, making it imperative to address these issues before they become even more ingrained in our society. 

Twenty-four-year-old Trinity Joubert shed light on the profound impact that wrongful convictions have on individuals and families, with a particular emphasis on Black communities. These miscarriages of justice are not only a theft of freedom but can also cause irreparable damage to family bonds and undermine the unity of communities. 

Joubert drew attention to the fact that wrongful convictions are only one aspect of a spectrum of systemic issues that disproportionately affect minority groups.

“I didn’t even know about this, but see, this is another setback for Black families to lose their fathers, sons, brothers, mothers, and so on,” Joubert said. “This is a setback for minorities because everything is against us, including this, which needs to be addressed.”

Ethical Quandaries: Unveiling the Need for Stringent Regulations

The lack of comprehensive regulations in other parts of the world, such as the United States, leaves a gap in protecting an individual’s rights and privacy in the context of AI-driven systems. 

While some states have introduced their own regulations, such as the California Consumer Privacy Act, a national framework still needs to be developed. This patchwork of regulations creates challenges for businesses and organizations operating in multiple jurisdictions, as they must navigate different compliance requirements.

 To address this issue, there is a growing call for the implementation of federal regulations in the United States to govern the use of AI in law enforcement and other sectors. These regulations must incorporate principles like those outlined in the GDPR, including a lawful basis for processing, data minimization and purpose limitation, transparency and accountability, data subject rights, and data protection by design and default.

Developing ethical guidelines and standards for using AI in law enforcement is essential. These guidelines should address issues such as bias and fairness, accountability, transparency, and protecting individuals’ rights. 

Additionally, there is a need for mechanisms to ensure independent oversight and auditing of AI systems used in law enforcement to prevent abuses and ensure compliance with regulations and ethical standards.

Cultural Competency in Policing: Bridging Gaps and Fostering Trust

Civil rights activist Martin Luther King Jr. once said, “True peace is not merely the absence of tension; it is the presence of justice.”

This quote encapsulates the essence of advocating for equitable practices in utilizing AI facial recognition technology. 

As we navigate through an increasingly digital world, we must remain mindful of the potential biases ingrained within these innovative tools. By integrating diversity training and cultural competency programs, officers can better understand the diverse communities they serve. 

This heightened awareness can lead to more accountable and transparent AI technology practices. It can also foster trust and understanding between law enforcement and minority communities, paving the path for equitable and just treatment.

The flawed impact of police AI racial recognition on minority communities demands immediate attention and action. 

It necessitates robust reforms, including diverse datasets, regulations, and cultural sensitivity training. We can recalibrate our technological advancements to serve society without perpetuating discrimination and injustice through collaborative efforts among communities, policymakers, and technology developers.