Artificial Intelligence in Criminal Investigations: Legal Challenges and Safeguards

By

Laila Mohamed Ali

CyJurII Scholar

On 17 August 2025

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Introduction

Artificial Intelligence (AI) is no longer a futuristic concept; it is already embedded in modern policing. From facial recognition systems that scan massive image databases to predictive policing algorithms that forecast crime hotspots, AI promises to make criminal investigations faster and more efficient.

While AI promises efficiency in modern policing, its rapid adoption raises profound legal, ethical, and human rights concerns, including risks of wrongful arrests, privacy violations, and discrimination.

The challenge for lawmakers, courts, and law enforcement is to strike a balance between embracing the benefits of AI while ensuring it does not undermine civil liberties or due process.

1. Use Cases of AI in Criminal Investigations

1.1 Facial Recognition and Surveillance Tools

Law enforcement agencies increasingly use AI-powered facial recognition software such as Clearview AI to identify suspects by comparing their faces against massive image databases. This can accelerate suspect identification, especially in cases where traditional investigative methods would be too slow.

However, several high-profile incidents have revealed false positives leading to wrongful arrests. Studies show these systems are more likely to misidentify individuals from racial minority groups due to biased training datasets, raising concerns under anti-discrimination laws and constitutional rights.

1.2 Predictive Policing and Risk Assessment

Predictive policing systems like COMPAS analyze criminal records, socio-economic data, and other variables to assess an individual’s likelihood of reoffending or to predict future crime locations.

While proponents argue these tools optimize police resource allocation, independent audits have shown significant racial disparities in their predictions, potentially resulting in over-policing of minority neighborhoods. This raises potential violations of equal protection rights and could even challenge the admissibility of such evidence in court.

1.3 AI-Generated Police Reports and Forensic Analysis

Some police departments now use AI tools to automatically draft incident reports and analyze forensic evidence, such as DNA or digital footprints. These innovations can reduce administrative burdens and speed up investigations.

Yet, they also present due process challenges as defense attorneys may be unable to access or scrutinize the algorithm’s decision-making process. If the AI’s reasoning is opaque, it can be impossible to verify whether the evidence is reliable, undermining the defendant’s right to a fair trial.

2. Key Legal Challenges

2.1 Right to Privacy

The collection and retention of biometric and behavioral data without informed consent can breach privacy laws. For example, under the EU General Data Protection Regulation (GDPR), biometric data is classified as highly sensitive and subject to strict processing conditions.

In the United States, while no equivalent comprehensive federal privacy law exists, courts continue to debate whether AI-powered surveillance violates the Fourth Amendment’s protection against unreasonable searches.

2.2 Algorithmic Bias and Discrimination

AI systems often rely on historical crime data, which may already reflect biased policing patterns. When fed into algorithms, these biases can perpetuate and even amplify discrimination, particularly against racial minorities and lower-income populations. This poses risks of violating anti-discrimination statutes and constitutional equality guarantees.

2.3 Transparency and Explainability

Many AI tools function as “black boxes”, where neither law enforcement nor the courts can fully explain how decisions are made. This lack of explainability can hinder judicial review and make it difficult to challenge AI-generated evidence, eroding accountability in the justice system.

2.4 Accountability and Liability

If an AI system produces flawed results that lead to a wrongful arrest, the question arises: Who is legally responsible?

Should the police department for using the tool?

The software developer for creating it?

Or the government for approving its deployment?

The absence of clear liability frameworks can leave victims without adequate remedies.

3. International Legal Frameworks

3.1 European Union – GDPR & AI Act

The GDPR provides strong protections for biometric data and grants individuals the right to access and delete their personal information. The forthcoming AI Act, expected to take effect in 2026, will classify AI in law enforcement as “high-risk,” imposing mandatory human oversight, transparency requirements, and strict compliance obligations.

3.2 United States – Fragmented Regulation

The U.S. has no single federal AI law. Instead, protections come from constitutional rights and a patchwork of state-level laws, such as Illinois’ Biometric Information Privacy Act (BIPA), which requires consent before collecting biometric data.

3.3 United Nations & Council of Europe

The UN Human Rights Council has called for temporary bans (moratoria) on intrusive AI surveillance until adequate safeguards are in place.

The Council of Europe’s AI Convention emphasizes proportionality, transparency, and accountability in AI deployment.

3.4 Egypt

Egypt’s Cybercrime Law No. 175 (2018) criminalizes certain cyber offenses but does not specifically regulate AI use in law enforcement. This regulatory gap leaves room for uncontrolled surveillance practices and raises questions about compliance with international human rights standards.

4. Recommendations

·   Establish Comprehensive AI Legislation and Specific laws governing biometric data collection, retention, and use in criminal justice.

·   Independent Algorithmic Audits: Third-party evaluations to detect and address bias in AI systems before deployment.

·   Mandatory Algorithmic Transparency: AI systems must provide clear, interpretable explanations of their decisions.

·   Clear Liability Frameworks: Define legal responsibility when AI errors result in harm.

·   Training for Law Enforcement: Equip officers with knowledge on algorithmic bias, responsible AI use, and human oversight best practices.

5. Conclusion

Artificial Intelligence has the potential to revolutionize criminal investigations, making them faster, more efficient, and data-driven. But without robust legal safeguards, these same technologies can erode fundamental rights, exacerbate inequality, and undermine public trust in the justice system.

The path forward lies in balancing innovation with regulation and ensuring AI serves justice, rather than compromising it.

References

·   Washington Post: Police Artificial Intelligence Facial Recognition (2025)

·   New Yorker: Does A.I. Lead Police to Ignore Contradictory Evidence? (2023)

·   ProPublica: Machine Bias (2016)

·   European Union: General Data Protection Regulation (Regulation EU 2016/679)

·   European Commission: Proposal for an AI Act (COM/2021/206)

·   ACLU: AI-Generated Police Reports Raise Concerns (2023).

·   Council of Europe: Framework Convention on AI (2024)

Egypt: Cybercrime Law