Artificial Intelligence and the Reconfiguration of Justice: A Culturally Grounded, Globally Responsive Framework for Algorithmic Governance

Artificial Intelligence and the Reconfiguration of Justice: A Culturally Grounded, Globally Responsive Framework for Algorithmic Governance

Fatemeh Ahangari¹, Reza Mokhtar^*2, Amir Masoud mazaheri³

¹Department of law, Arak University, Arak, Iran

²Department of Chemical Engineering, Arak University, Arak, Iran

³Department of Law, University of Tehran, Iran

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000253

Received: 01 September 2025; Accepted: 04 September 2025; Published: 06 October 2025

ABSTRACT

Artificial intelligence (AI) has the power to reshape how justice is delivered—speeding up decisions, leveling the playing field, and widening access to courts. But without thoughtful guidance, it could just as easily amplify biases and chip away at the foundations of legal fairness. That’s where our Artificial Intelligence and Justice (AAJ) Framework comes in. Built on real-world evidence, insights from judges across the globe, and a deep respect for local legal traditions, this framework offers a way to make AI both accountable and understandable. We put three big ideas to the test:

– Does stronger oversight really cut down on AI bias?

– Can tailored explanations shave at least 15% off error rates in courts worldwide?

– Will adapting AI to local legal cultures boost trust among judges and litigants by 20% or more?

To find out, we crunched the numbers on 1.2 million cases and sat down with 120 legal experts. The results? All three ideas held up, with flying colors (p<0.01). We learned that a cookie-cutter approach to AI in justice is like trying to navigate with a compass that ignores local magnetic quirks—it’ll lead you astray. The AAJ Framework, by weaving in the unique threads of each legal system, offers a practical, fair-minded way to bring AI into courts everywhere.

Keywords:Artificial Intelligence(AI), Judicial Systems, Algorithmic Governance, Cultural Adaptation, Explainable AI, Bias Reduction, Trust in AI, Human Oversight, AI Ethics

INTRODUCTION

The march of artificial intelligence into courtrooms and legal offices is nothing short of a game-changer. Tools that weigh the odds of someone reoffending or suggest sentences are popping up everywhere, promising quicker, steadier justice. But here’s the catch: if we don’t steer these systems carefully, they could deepen the very biases we’re trying to root out. Picture strapping a turbocharged engine onto a rickety old carriage—if the frame and wheels aren’t up to the task, you’re in for a bumpy, if not disastrous, ride.

That’s why we created the Artificial Intelligence and Justice (AAJ) Framework. Our mission? To marry the best of global tech know-how with the distinct flavors of local legal cultures. We zeroed in on three goals:

1. Figure out how AI performs across different legal landscapes—think common law, civil law, and religious law.
2. Craft explanations for AI decisions that actually make sense to the people using them, wherever they are.
3. Blend technical checks with human oversight so AI supports, rather than sidelines, human judgment.

We had three hunches to test:

– Tougher oversight means less bias in AI outputs.

– Smart, context-aware explanations can trim errors by at least 15%.

– Tuning AI to local legal vibes lifts trust by 20% or more.

We tackled these questions with a two-pronged approach: simulations of 1.2 million anonymized cases from four continents, paired with in-depth chats with 120 judges, prosecutors, and defense lawyers. What we found was eye-opening:

– Bias in AI isn’t a one-size-fits-all problem—it shifts with local data and rules.

– Explanations need to speak the local legal language, or they fall flat.

– The best accountability comes from mixing tech smarts with human wisdom.

The AAJ Framework pulls all this together, offering a roadmap for AI in justice that’s efficient, clear, and rooted in the places it serves.

JURISDICTIONAL DISPARITIES IN ALGORITHMIC ERROR PATTERNS

First, we wanted to see how AI tools stack up across different legal systems. Table 1 lays out the numbers from our simulations, drawn from real case records around the world.

Table 1. Comparative Algorithmic Error Rates by Legal Jurisdiction

Bias Disparity Index: ratio of highest to lowest error rates across protected groups (values > 0.20 signal urgent intervention).

Here’s what stood out:

– Iran’s Bias Disparity Index (0.25) is waving a red flag—spotty data and weak oversight are likely culprits.

– Places like the EU and U.S., rooted in common law, show less bias but aren’t immune to fairness hiccups.

– The stats (p<0.05) back this up: there’s no magic fix that works everywhere.

Next, we’ll dive into how custom explanations can help iron out these wrinkles.

METHODS

Data Acquisition and Curation

We pulled together 1.2 million anonymized court cases from the EU, U.S., Iran, Brazil, and India—everything from defendant profiles to final rulings. To compare apples to apples, we mapped each region’s legal codes onto a shared system, following Ref.[1]. We scrubbed the data, tossing out duplicates (less than 0.5%) and spotting oddballs with a clever algorithm[2].

Simulation Framework

Using Python, we built a testing ground for three AI tools: COMPAS[3], LSI-R [4], and a homegrown model trained on European cases[5]. Each one predicted things like recidivism, tweaked to match local error rates. We ran the numbers through multiple rounds (10-fold cross-validation, 100 repeats) to track false positives and negatives.

Adaptive Explainability Protocol Design

We cooked up a system to explain AI decisions in ways that fit each legal tradition—think precedent for common-law courts, statutes for civil-law systems, and sacred texts for religious ones. We scored these explanations for clarity, relevance, and brevity, tweaking them with feedback from 20,000 cases [6].

Field Interviews and Qualitative Coding

We sat down with 120 legal pros—judges, prosecutors, defenders—from ten countries, asking how they feel about AI in their work. Using grounded theory [7], we sifted through their answers, finding patterns around fairness and trust. Our coding held up well, with strong agreement between researchers [8].

Statistical Analysis

We crunched the data with t-tests and regression models, adjusting for multiple factors[9]. All p-values got a reality check with the Benjamini-Hochberg method[10], and we stuck to 95% confidence.

RESULTS

Aggregate Error Reduction

Our custom explanations slashed errors by 17.8% on average (95% CI: 16.2–19.4%). This wasn’t a fluke—it worked across the board: 16.4% in common-law systems, 18.7% in civil-law, and 18.2% in religious-law settings.

Bias Disparity Improvements

Table 2 shows how we tamed bias. The Bias Disparity Index dropped 22% overall (p<0.001), with Iran’s score falling from 0.25 to 0.19—safely below the danger zone.

Stakeholder Perceptions of Fairness

From our interviews, 84% of folks found our explanations crystal clear, and 78% said they trusted AI more because of them. Common-law judges loved the nod to past cases; civil-law ones liked the legal tie-ins (Fig. 1). The data backs this up—clarity drives trust (β=0.67, p<0.001).

Figure 1.Summarizes findings from a post-implementation stakeholder survey of judges, lawyers, and administrators.

Institutional Oversight Synergy

In places with solid oversight (EU, U.S.), our framework added a modest 5% bias reduction. But in regions still finding their footing (Iran, India), it slashed bias by 12%. It’s clear: tech and human oversight are a winning duo.

DISCUSSION

Theoretical Contributions

This work proves that fairness in AI isn’t just about code—it’s about culture and law too. By tailoring explanations to local ways, we’ve built on past ideas[11, 12] and brought AI closer to courtroom realities.

Policy and Practical Implications

Lawmakers, take note: work with local voices to shape AI, not just slap on a global template. The AAJ Framework’s flexibility makes it a handy tool for courts anywhere, a starting point for regulators worldwide.

Figure 2. illustrates the efficacy of the Accountability, Auditability, and Justifiability (AAJ) framework.

Limitations and Future Directions

Our simulations are broad, but real life might throw curveballs, especially in blended legal systems. Down the road, we’d love to test this in action and expand to more places.

CONCLUSION

With the AAJ Framework, we’ve shown how to weave local legal traditions into AI, boosting accuracy, fairness, and trust. It’s a careful balance of tech precision and human insight. As AI steps further into justice, we need to keep both eyes open—embracing its promise while dodging its pitfalls. This framework is a solid step toward making sure AI serves justice, not the other way around.

ACKNOWLEDGEMENTS 

We declare no competing financial or non-financial interests. No external technical or financial support was received. All project costs were covered personally by Dr. Reza Mokhtar, the first and corresponding author.

AUTHOR CONTRIBUTIONS 

Dr. Reza Mokhtar: data collection; project management; data analysis; molecular and materials simulation; funding acquisition; manuscript drafting, compilation, and final revision.

All other authors: experimental work, methodology development, validation, and manuscript review.

DATA AND CODE AVAILABILITY STATEMENT 

Raw experimental data and analysis code will be provided to the journal reviewers upon acceptance and upon their request. Following publication, these materials will be deposited in a public repository and made accessible to the wider research community upon request, in accordance with reviewer guidance.

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