In one city, the police scan hours of surveillance footage and identify a criminal within minutes. In another, prosecutors use software to predict whether a suspect is likely to violate parole. Meanwhile, corporate lawyers rely on automated programs to sift through stacks of legal documents in seconds. A decade ago, these scenarios would have seemed futuristic. Today, they highlight the growing influence of Artificial Intelligence (AI) in policing, legal practice and courtrooms worldwide.
AI enthusiasts praise its speed and reliability, while critics argue that algorithms may simply reflect and reproduce human biases in a new digital form. Ultimately, an important question arises: can AI truly deliver fair justice or is it just a high-tech way of reinforcing human bias?
Over the past decade, police forces worldwide have increasingly relied on advanced algorithms for tasks that once took days or even weeks of manual work. Predictive policing tools gather and analyze crime reports, demographic data or social media activity to forecast where illegal activity might flare up next. The result? Commanders can deploy officers to “hotspots” more strategically. For instance, technologies such as facial recognition, image matching and automated license plate readers help track suspects or locate stolen cars much faster than human teams. In theory, these tools can reveal connections that even the most qualified detectives or experts might overlook. Similarly, automated scanning of bank transfers can expose money laundering operations, behind complex layers of shell companies.
This seemingly positive use of AI positions it as a potential antidote to human fallibility: the “perfect” automated assistant, free from emotional or extra-legal influences. Speed and accuracy, then, lie at the heart of AI’s appeal to law enforcement.
As is widely observed, AI thrives on data – the more, the better. Machine learning models typically improve when trained on a large amount of relevant data, prompting law enforcement to collect as much information as possible on individuals, places and activities. This may include sensitive details about people’s whereabouts, social networks or personal history; however, gathering extensive private data poses risks to civil liberties, raising concerns regarding potential leaks of information or misuse beyond the original crime-fighting purpose. Therefore, questions around data sharing persist: will the police share these records with intelligence agencies or even private entities? In response to these concerns, legal scholars emphasize that robust data protection rules and strong oversight are essential to prevent law enforcement from crossing ethical lines.
If past policing patterns were shaped by prejudicial assumptions, the historical data used to train AI systems will inevitably mirror those patterns, resulting in “dirty” data. As a consequence, individuals from disadvantaged backgrounds may be subjected more frequently to police stops or harsher sentences simply because they belong to communities labeled as “high-risk”. Additionally, the notion of machine objectivity might only be a myth. In theory, a computer cannot be “racist” or “sexist” in a moral sense, yet machine-learning tools can pick up patterns of unfairness if trained on biased data. This raises a critical concern: judges or police officers, using a presumably neutral legal tool, may trust it blindly.
The hallmark of the rule of law lies in ensuring that every individual is subject to impartial justice. When rendering decisions, the judiciary considers not only the facts but also questions of equity, compassion and legal principles such as proportionality. However, neither humans nor AI-driven systems are immune to bias. On one hand, judges can be influenced by subconscious prejudice, emotions or even fatigue, leading to inconsistent rulings. On the other hand, AI promises consistency and data-driven precision, yet it may simply reflect historically biased data rooted in discriminatory policing or past unfair judgements.
These concerns strike at the core of courtroom decision-making. If AI alone were to render verdicts, we risk losing the essential human element of justice. Conversely, if humans remain the sole authorities, deep-seated biases and subjectivity could persist unchecked. So, whom do we trust when neither is infallible? Each side has its own strengths: humans bring empathy, while AI can rapidly and accurately analyze vast amounts of information.
Accountability adds another layer of complexity. The adoption of AI in law enforcement raises pressing questions of accountability:
who bears ultimate responsibility for erroneous or unjust outcomes when decisions are made by automated systems?
Is it the software developer? The police force or prosecutorial team using the system? Or does liability rest with the supervisory authority that initially approved the technology? Scholars emphasize that an AI must remain subject to “human-in-the-loop” oversight, making sure that decision-making must still be traceable to individuals who can be held accountable under the law.
In this sense, the future is unlikely to be defined by AI dominance, but rather by meaningful hybrid systems where humans are held responsible for final judgments and policy decisions, while AI provides comprehensive analysis. The challenge lies in integrating these tools wisely, harnessing their speed and pattern-detection capabilities without compromising the moral dimension of justice. Ensuring robust lines of accountability requires courts and law enforcement agencies to systematically integrate third-party audits, transparent design protocols and mandatory “human override” mechanisms, particularly in cases where an AI’s recommendation would profoundly affect an individual’s liberty or rights.
AI in law enforcement and courts reveals a paradox: on one side, advanced algorithms promise greater efficiency, accuracy and consistency; on the other, they risk magnifying already existing issues, particularly discriminatory policing and ambiguous judicial decisions, that threaten core legal principles such as equality before the law, due process and the presumption of innocence.
Rather than fully embracing or rejecting AI, a balanced approach appears to be the best decision. Thorough testing, continuous audits, and a robust legal framework for data protection and accountability are essential. Legal professionals must be trained to interpret AI outputs critically, ensuring that technology supports human judgment, rather than replaces it. Ultimately, while algorithms excel at sorting and analyzing data, only human empathy and moral insight can uphold the deeper principles of justice.
Written by Karem Parraga Espinoza, Edited by Xenia Oana Cojocaru
