Consent Asymmetry And Synthetic Neutrality In Arbitration: When AI Tilts The Scales Of Justice

The Illusion of Neutrality in Digital Systems

Picture an AI ruling on your dispute—fast, flawless, and utterly opaque. Is this justice, or a high-tech illusion?

Artificial intelligence (AI) tools are increasingly used in arAetbitration to streamline case analysis, rank submissions, or generate summaries. These systems promise objectivity, speed, and cost-efficiency. But beneath their polished interfaces lie systemic risks. Can we trust machines to be neutral? And what happens when one party consents to these tools without truly understanding them?

It is imperative to examine Consent Asymmetry and Synthetic Neutrality through core concepts, a real-world-inspired case study, and evidence of structural bias.

Consent Asymmetry Hypothesis

This is a conceptual hypothesis referring to the imbalance in informed participation when one party understands how an AI tool works, while the other consents without fully grasping its implications. It's like signing a contract in a foreign language—valid on paper, but you're in the dark about what you've agreed to.

In arbitration, where procedural trust is vital, this asymmetry can quietly erode fairness. Small firms or parties from non-Western jurisdictions may accept AI tools embedded in procedural rules or online platforms without clarity on how these tools function. Their consent may be formal, but not informed. This silent gap in comprehension creates room for procedural harm that masquerades as efficiency.

Synthetic Neutrality

Synthetic Neutrality is another hypothetical concept which describes a deceptive appearance of impartiality in algorithmic decisions—tools that seem fair but are shaped by biased data, system design, or unacknowledged cultural assumptions. Think of a referee who only understands one team's strategy—impartial in name, but tilted in practice.

AI trained on a narrow subset of legal data may replicate dominant legal assumptions, excluding oral traditions, relational contracts, or culturally contingent norms. These systems can appear objective while silently reinforcing systemic advantages. The illusion of neutrality is more dangerous than open bias because it resists scrutiny.

Case Scenario: The Kenyan Startup vs. the London Giant

Consider a real-world-inspired hypothetical: a Kenyan tech startup enters arbitration with a London-based multinational. The AI-powered platform handling the dispute includes a submission ranker. The startup's CEO uploads WhatsApp voice notes—a handshake deal sealed in Nairobi's vibrant tech scene. The AI, expecting formal briefs, dismissed them as “incoherent.” Meanwhile, the London giant's polished 100-page submission earned top marks.

The arbitrators receive both—but the AI's credibility score subtly nudges them toward one side. For the startup, it wasn't just a lost case—it was their shot at global growth slipping away, judged by a machine that couldn't hear their story. Here, the Consent Asymmetry Hypothesis and Synthetic Neutrality collide: the startup didn't know the rules; the system didn't recognize their voice.

Structural and Data Bias in Arbitration

These biases aren't new. In criminal justice, COMPAS scores misjudged recidivism among Black defendants (Angwin et al., 2016). In hiring, Amazon's AI system penalized women's resumes (Dastin, 2018). Arbitration is not immune.

An AI trained on awards from London or New York may misjudge disputes from Singapore, where common and civil law intermingle. In rural India, where trust often trumps formal documentation, AI may undervalue oral testimony. An AI trained on top-tier law firm submissions may favor their structure and vocabulary in future disputes. These are not technical quirks—they are fault lines in the foundation of digital justice.

Safeguards: From Consent to Oversight

To restore balance, arbitration must embed safeguards—starting with transparency. Parties must receive plain-language disclosures about AI's role, design, and limitations. These should specify: a) Training sources (e.g., “80% UK arbitration awards”), b) Functional scope (e.g., “only summarizing evidence”), c) Known biases (e.g., “prefers formal over informal submissions”), and d) An “explainability sheet” showing how decisions were reached.

Equally important are independent audits. These should stress-test AI tools using diverse hypothetical cases—from Southeast Asia to West Africa—to catch cultural blind spots. Audit summaries must be accessible, not buried in code. Institutions can adopt scorecard-like summaries for each tool. For example, a “Submission Ranker” tool may assess coherence but should be audited for linguistic and cultural bias. A “Case Summarizer” might highlight key facts but should flag informal norms or oral agreements as legally relevant instead of ignoring them.

Legal Ramifications: Enforceability and the New York Convention

Beyond ethical concerns, the risks posed by AI-driven decision-making extend into the enforceability of arbitral awards. Article V(1)(e) of the New York Convention permits refusal of enforcement if the award is contrary to public policy or lacks reasoned justification (United Nations, 1958). If an award is heavily shaped by opaque AI processes—where neither the arbitrator nor the parties can retrace the rationale—the decision may fail to meet the standard of a “reasoned award” required under many jurisdictions (UNCITRAL, 2006).

Imagine an AI-assisted award accepted in Paris but rejected in Mumbai on grounds of opacity; such inconsistency threatens the uniformity and trust arbitration relies upon. Auditable logic and human validation are not optional—they are prerequisites for global enforceability (Yeung, 2018).

The Ethics of Delegated Justice

Delegating decision-making to AI raises a deeper philosophical question: can justice be automated without becoming dehumanized? While machines excel at optimizing, they struggle with moral ambiguity and cultural texture. When justice is reduced to probabilities and weighted inputs, we risk losing the relational, empathetic core that defines fair adjudication.

As scholars like Eubanks (2018) and Barocas et al. (2019) caution, unchecked automation may reinforce existing power imbalances under the guise of efficiency. Arbitration must resist the allure of neutral code and instead cultivate systems that amplify—not replace—human judgment. Fairness cannot be computed; it must be experienced, and experienced justly.

Expanding Arbitrator Capacity in the Age of AI

As AI becomes more integrated into arbitration, arbitrators must be trained not only in legal principles but in technological literacy. Understanding how algorithms function, what biases they carry, and how to question their outputs is essential. This is not about turning arbitrators into coders, but about ensuring they remain informed gatekeepers of fairness. A digitally literate arbitrator is better positioned to scrutinize AI-assisted insights and maintain control over decision-making rather than deferring to automated outputs.

Furthermore, arbitral institutions must establish internal review mechanisms to vet and validate AI tools before deployment. This could involve interdisciplinary panels that include ethicists, computer scientists, and practicing arbitrators. The goal should not be to slow down innovation but to align it with the foundational values of procedural justice, party autonomy, and transparency.

Fairness Beyond Appearance

If you can't question the machine deciding your case, is it really justice?

Consent isn't just a procedural step—it's a moral contract. And neutrality isn't optics—it's how outcomes are earned, not just calculated.

Deals seal with a glance, not a signature. AI can't process that—should it still hold the gavel?

Arbitration's soul lies in its adaptability, its humanity, its ability to hear the unsaid. These traits cannot be coded. Arbitration centres should form AI ethics task forces—blending lawyers, coders, and ethicists—to set transparency rules. Parties should demand AI transparency reports and actively negotiate whether, when, and how such tools will be used in their proceedings.

AI can speed justice, but only we can steer it.

Author: Rishabh Gandhi, Founder of Rishabh Gandhi & Advocates. Views are personal.

References

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine Bias. ProPublica. 
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org
Dastin, J. (2018, October 10). Amazon scrapped 'sexist AI' hiring tool. Reuters. 
Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.
Gandhi, R. (Forthcoming). The Ethics of AI-Driven Arbitration: Balancing Fairness, Transparency, and Accountability in the Digital Dispute Resolution Era.
UNCITRAL. (2006). Model Law on International Commercial Arbitration (with amendments). Article 31(2).
United Nations. (1958). Convention on the Recognition and Enforcement of Foreign Arbitral Awards (New York Convention), Article V(1)(e).
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523.