AI in the Courts: Addressing Data Security, Reliability, and Fairness Risks in Judicial Adoption

Technological Risks in Judicial Adoption of AI

AI is increasingly embedded in judicial processes, from drafting legal documents to analyzing case elements. This integration spans the entire litigation workflow. The legal technology market now exceeds USD 10 billion globally, with significant investments in modernizing judicial systems, particularly in China. While these advancements boost efficiency, they also introduce notable risks that demand careful management.

Data Security and Privacy Concerns

AI tools in litigation require access to extensive legal data sets, including case files, judicial decisions, contracts, and sensitive client information. Many AI models tailored for the legal sector must connect to internal networks or law firm databases for local deployment. Weak security can lead to data breaches or cyberattacks.

Insufficient anonymization of AI-generated legal documents risks exposing confidential commercial information, which can trigger legal disputes.

Recommended best practices include:

• Multi-layered encryption and access controls: Use end-to-end encryption combined with strict access management when AI systems interact with local databases. Blockchain technology can secure electronic case files by ensuring data immutability. A hybrid approach—maintaining key original paper documents while circulating encrypted electronic files—can reduce leakage risks and control costs.
• Data anonymization and desensitization: Establish a graded data security framework during model training. Implement standardized procedures for collecting, cleansing, and labeling data. Automated rule engines should filter sensitive fields like ID numbers or financial information. Differential privacy techniques can anonymize data while preserving essential case details.

Some courts have piloted "element-based intelligent adjudication systems" that extract case elements and aggregate legal facts automatically. Their work offers useful insights for managing data extraction and structuring.

Reliability and Verification Challenges

Key risks with AI judicial tools involve technical reliability and authenticity of data. Research highlights two main concerns:

• Algorithmic inaccuracies: Generative AI models, including well-known ones like ChatGPT and domestic alternatives such as DeepSeek, sometimes produce incorrect legal information. Examples include fabricated judicial interpretations or erroneous case citations. Delays in updating training data—often by three months or more—worsen this problem, limiting AI’s ability to reflect recent legal developments.
• Limitations in reasoning complex legal matters: AI accuracy declines sharply when interpreting intricate cases—such as multi-layered special purpose vehicles (SPVs), convertible earn-outs, layered cross-border guarantees, or technical unfair competition disputes. AI struggles to analyze non-standard contract clauses, identify core case elements, and determine genuine legal relationships, which impacts dispute resolution involving complex commercial arrangements, financial derivatives, cross-border mergers, and emerging tech.

Judicial Fairness and Algorithmic Bias

The use of AI in judicial settings raises concerns about fairness and potential algorithmic bias. A "computing power gap" is emerging where top law firms leverage resources to build proprietary data and algorithms, creating closed ecosystems. Smaller firms often lack the means to compete, risking entrenched technological monopolies if regulation fails to address this.

Of particular concern is the interplay between opaque algorithms and historical data biases. AI models trained on past judgments can embed structural biases, creating a feedback loop that reinforces unfair outcomes. These models, often seen as "technically neutral," gain influence via online algorithmic recommendations.

This shifts legal fact analysis toward algorithmic logic, potentially distorting standards for identifying case elements and their relative importance. Two key risks arise:

• Algorithmic outputs may become perceived as authoritative legal standards, diminishing judicial discretion.
• The technology gap worsens disparities between litigants, making it harder to challenge AI-driven decisions due to limited transparency, which undermines procedural fairness in adversarial litigation.

AI can support justice only if it aligns with its fundamental principles. Otherwise, it risks becoming a hidden threat that weakens the rule of law.