As artificial intelligence becomes embedded in commercial, administrative, and professional decision-making, disputes involving opaque or unpredictable system behaviour are becoming increasingly common. Traditional litigation struggles to address these conflicts, which blend technical uncertainty with evolving legal and regulatory principles. Mediation offers a proportional, flexible, and forward-looking process capable of bringing clarity to the “unexplainable” and helping parties navigate accountability in an AI-driven environment.
Although the term AI was originally used in 1955, our awareness and use of it have rapidly accelerated through the launch of OpenAI’s GPT series. OpenAI released the GPT-3.5 model in November 2022, and its latest version, ChatGPT 5.1, was released on August 7, 2025. The functionality differences between Chat-GPT 3.5 and Chat-GPT 5.1 are genuinely astonishing, and GPT now has over 800 million weekly active users.
But there is another, not so well-documented aspect to this. As adoption of AI systems accelerates, disputes involving them will become more common. These conflicts blend legal, ethical, and technical questions in ways that traditional processes struggle to address. Going to court should not be the automatic first response for disputes in a field that is still being defined by case law precedents are few in number with results varying between jurisdictions on similar topics.
Mediation offers a practical and flexible way to resolve these disputes before they escalate into lengthy litigation, inspiring confidence that a constructive resolution is possible even in complex AI conflicts.
Another significant benefit of mediation is the potential for speedy resolution in a rapidly changing field. Waiting for years for your case to be heard in court does not work in these types of disputes in such a fast-changing environment.
Understanding the unique characteristics of AI disputes is essential for mediators to feel confident and focused, enabling them to guide discussions effectively and foster a sense of mastery over complex issues.
Understanding the Nature of AI Disputes
AI disputes typically arise at the intersection of technology, data governance, and legal responsibility. They often involve overlapping issues that can be challenging to separate even for sophisticated parties. Common sources of conflict include lack of trust and accountability including:
- “Black-box” decision-making: Parties lack insight into how a result was generated by algorithms.
- Data governance and compliance: Issues related to collection, consent, transparency, and jurisdiction.
- Fairness and bias: Concerns about inequitable or harmful outputs.
- Performance expectations: Questions about accuracy, reliability, and meeting regulatory or contractual requirements.
- Liability for outputs: Including hallucinations, misinformation, deepfakes, or agent-driven decisions.
- Human vs. automated accountability: Determining responsibility where human judgment and automated processing overlap.
These disputes are often intensified by information and power imbalances among developers, deployers, and end users. One party may understand the technical system; another may experience only its impacts.
The Mediator’s Role
The mediator does not need deep technical expertise. As in any mediation, the mediator’s task is to manage the process and ensure informed participation. Clarifying the mediator’s responsibilities in process design, fairness, and informed decision-making helps mediators understand their role and stay focused on guiding parties effectively.
Instead, the role is grounded in process design, fairness, and informed participation. In AI disputes, this work becomes even more vital.
Key responsibilities include:
- Structuring the presentation of technical information into manageable, purposeful stages.
- Managing information exchange while respecting confidentiality and intellectual property boundaries.
- Addressing power imbalances, especially where there are significant expertise gaps.
- Reality testing assumptions regarding causation, system reliability, and regulatory exposure.
- Ensuring informed decision-making.
Effective mediation in this space depends on clarity, proportionality, and disciplined process management.
Preparing for Mediation
Preparation is essential for any mediation, and especially for disputes involving unfamiliar technologies. Effective preparation is the key to any mediation and requires clarity on both the legal and technical dimensions of the dispute.
Parties and counsel should:
- Identify the system functions or behaviours in dispute.
- Clarify the boundaries between automated and human decision-making.
- Assess potential operational, financial, reputational, and regulatory implications if the dispute remains unresolved.
- Determine whether neutral experts will be required and define their role early.
Mediators can support this stage by encouraging parties to establish shared definitions, clarify contested concepts, and managing the dispute in a way that avoids later confusion.
Lawyers preparing for an AI dispute should consider:
- What, precisely, went wrong or is alleged to have gone wrong?
- What documentation exists about the system’s purpose, training data, and limitations?
- What regulatory obligations are engaged?
- What assumptions about the technology are driving party expectations?
- What technical information is required for informed negotiation?
- What risks arise if the system continues operating unchanged during the dispute?
Working With Experts
Given the complexity of AI disputes, independent technical expertise is often essential to clarify technical concepts.
A constructive approach includes:
- Using joint experts wherever possible.
- Ensuring plain-language summaries of documents are available.
- Providing opportunities for direct expert questioning by all parties.
- Sequencing expert input to support comprehension and avoid overload.
Avoiding turning technical input into an adversarial contest promotes understanding and builds trust in the process.
A Structured Model for Mediating AI Disputes
A structured framework is needed to help parties navigate both the legal and technical dimensions of AI conflicts. The goal in mediation should be to assist the parties in developing an agreement that meets an acceptable level of fairness, can be implemented, and is sustainable.
AI disputes benefit from a clear, staged framework. The following model supports both technical exploration and interest-based negotiation:
- Issue Identification
Clarify disputed behaviours or system outputs. Separate technical from contractual issues.
- Establish a Shared Technical Baseline
Develop a neutral description of the model’s purpose, data sources, limitations, and expected behaviour. Create a shared glossary if needed.
- Process Design and Information Management
Determine how sensitive or proprietary information will be disclosed. Sequence technical detail to maintain comprehension.
- Interest and Risk Exploration
Identify regulatory, operational, and reputational risks. Test assumptions about system performance and data quality.
- Option Development
Explore potential remedies such as governance reforms, retraining, monitoring mechanisms, contractual updates, or process changes.
- Negotiation
Assess each option’s feasibility, cost, regulatory implications, and impacts on ongoing relationships.
- Agreement and Implementation
Translate agreed-upon solutions into actionable, measurable commitments.
- Review and Follow-Up
Encourage post-implementation review and establish the option to return to mediation if new issues arise.
Are AI Disputes Regular Technology Disputes?
AI disputes share features with traditional technology conflicts, but important differences make them distinct. AI Conflicts will be far more complex than most traditional technology disputes. AI conflicts often require forward-looking remedies, such as model retraining, governance reforms, and monitoring, rather than just technical fixes. Understanding how AI conflicts differ from conventional technology disputes helps both parties and mediators design a mediation process tailored to the specific dispute.
Similarities
- Technical complexity requiring expert input
- Contract-based expectations
- Confidentiality and IP concerns
- Relationship management
- Time pressures in a dynamic environment
Key Differences
- System evolution: AI models change rapidly.
- Opacity: Many systems cannot readily explain their outputs ( Black Box).
- Regulatory uncertainty: Emerging laws create shifting obligations.
- Ethical considerations: Fairness, transparency, and bias move to the forefront.
- Forward-looking remedies: AI disputes often require governance reforms, retraining, or monitoring, not just technical fixes.
AI disputes often require complex, multi-part remedies such as model retraining, governance reforms, and ongoing monitoring, rather than just technical fixes. Recognizing these differences is essential to designing proportionate and effective mediation processes.
Conclusion
AI disputes present challenges that extend far beyond traditional technology conflicts. Their blend of technical complexity, data-dependence, and ethical implications demands a mediation process that is structured, transparent, and adaptable. With thoughtful preparation and well-designed process management, mediation can provide a highly effective path for resolving AI disputes that promote fairness, support ongoing collaboration, and ensure accountability in an increasingly complex legal environment.
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