AI in disputes: How predictive analytics in Anywhere helps estimate arbitration outcomes

The Concept of Predictive Analytics in Disputes and Arbitration

Predictive analytics in disputes is based on the idea that historical decisions and procedural data contain structured patterns that, with the use of machine learning, can be transformed into estimates of how a court or arbitral tribunal is likely to rule in a future case. In the judicial environment, tools already exist that analyze tens of millions of records from past proceedings, profile judicial behavior, estimate the success rate of procedural motions, and empower lawyers to plan strategies based on data, not just intuition. In the arbitration environment, the situation is more complex, primarily due to the confidentiality of arbitral awards, differing legal cultures of participants, and less availability of structured data. Nevertheless, a range of solutions are emerging, focusing on investor-state arbitrations, sector-specific B2B disputes, or internal data repositories of larger institutions and law firms.

For commercial disputes, in some specific contexts and jurisdictions, machine learning is reported to achieve accuracy of roughly 80–90% in certain types of cases (e.g., contractual disputes, labor law, or patent disputes). This can be significantly higher than traditional estimates by lawyers based on experience. One analysis, for example, states that for contractual disputes, models achieve accuracy of approximately 85–92%, for labor law disputes 82–88%, and for patent disputes 78–85%, while estimates by experienced lawyers typically range from about 60–75%. Such figures, of course, do not mean that AI "knows" how a specific dispute should turn out in a legal sense. Rather, they suggest that in historical data, there are repeatable patterns of behavior of deciding bodies, parties, and representatives that can be statistically captured and used as supporting information for strategic decision-making.

Arbitration differs from court proceedings in several fundamental respects that directly impact the usability of predictive analytics. Arbitration is typically contractual, based on an arbitration clause, flexible in procedural setup, and often confidential, meaning that a large portion of awards are not publicly available. Moreover, it does not involve permanent judges but arbitrators whose profiles, decision-making styles, and consistency in decisions are less mapped than for courts, although in some segments, such as investment arbitrations, extensive empirical analyses already exist. Predictive models in arbitration therefore often combine more limited publicly available data with internal data from institutions or law firms and expert inputs, which complement the statistical picture with the context of a specific dispute, often with regard to the specifics of different jurisdictions and legal cultures.

At the same time, it must be considered that predictive analytics in arbitration cannot reduce decision-making to a simple "score" of claimant or respondent success. Arbitrators and tribunals retain the discretionary power to evaluate evidence, interpret contracts, and apply legal norms with regard to principles of international and domestic law, procedural fairness, and specific circumstances. The usefulness of predictive models in practice is thus manifested primarily in supporting party decision-making—for example, in choosing a forum in an international context, budgeting, negotiating settlements, or timely re-evaluating procedural strategy—rather than in automating the arbitral award itself.

The perception of AI in arbitration is still evolving. While some institutions and commentators emphasize the importance of AI as a "smart assistant" for analyzing case files and drafting decisions, which always undergo human review, other research projects experiment with digital arbitrators and smart contracts that can automatically resolve simple claims using rules encoded in blockchain systems. In both cases, however, the quality of input data, the transparency of models, and the procedural framework in which they are used are crucial for the legitimacy of the outcome and the enforceability of the award in various jurisdictions.

In this context, it is important to understand platforms like Anywhere.legal not as a "black box" that issues verdicts, but as robust procedural infrastructure that allows legal teams and their clients to better structure a case, collect documents, use predictive models as a supporting tool, and coordinate the work of experts across jurisdictions, with full awareness of the international dimension and complexity. In practice, it often turns out that the greatest added value of AI lies not only in the outcome estimation itself but in forcing the team to systematically identify key factual and legal premises, clarify disputed points, and realistically set expectations regarding the chances and costs of a dispute based on data. This is particularly valuable in cross-border cases where different legal and tax systems intersect.

Fundamentals of Predictive Analytics in Law and Its Accuracy

To seriously consider predictive analytics in arbitration, it is first useful to understand how it works in court litigation, where data is more available and methodologies are more developed. Modern platforms for predicting court decisions use a combination of machine learning, advanced natural language processing (NLP), and statistical modeling of the behavior of judges, parties, and legal representatives. These systems process extensive corpuses of decisions, procedural filings, metadata about parties, and data about the course of proceedings to identify patterns that correlate with a certain type of outcome, such as the success of a motion to dismiss, the awarded amount of damages, or the duration of proceedings.

Some platforms, such as Pre/Dicta in the US federal courts environment, work with databases spanning twenty years of court data, millions of pages of filings, and detailed information on tens of thousands of judges. This allows them to predict whether a specific judge will dismiss or approve a certain type of motion with an accuracy of around 85%, even without detailed analysis of the merits of the dispute. Other solutions, like Lex Machina or other analytical tools, focus on specific areas, such as patent disputes, contractual disputes, or employment claims, where depending on data quality and methodology, they achieve similar levels of accuracy. These figures are not universal and depend on data quality and jurisdictional specifics; achieving them in international arbitrations is further complicated by confidentiality and data fragmentation. Nevertheless, they illustrate that predictive models can provide lawyers with robust statistical guidance.

From a methodological perspective, both traditional machine learning algorithms, such as logistic regression, random forests, or gradient boosting, as well as neural networks including transformer models, which are capable of working directly with the text of extensive legal documents, are applied. Transformers, which are also behind modern large language models, prove particularly effective in tasks where it is necessary to understand complex legal arguments, identify relevant legal questions, and predict how a court or tribunal is likely to approach them. In combination with structured data about judges, parties, and procedural steps, a multi-dimensional model emerges that captures both the content of the dispute and the "style" of decision-making.

In the field of dispute outcome prediction, however, it is important not to confuse correlation with causation. The fact that a model can "guess" the outcome with high probability based on historical patterns does not mean that it has identified the legal reasons for the decision in the sense that a court or arbitrator would formulate them in their reasoning. Predictive analytics is primarily a decision-support tool, not a substitute for legal judgment. Its outputs should therefore always be interpreted critically, taking into account what data the model saw and did not see, and what assumptions were encoded into it, while also considering the specifics of a particular national or international legal system.

In arbitration, it must also be taken into account that the data sample is smaller and less representative than in judicial databases. Many arbitral awards are not public, some disputes end in settlement without an award, and arbitrators come from different legal cultures, complicating the creation of consistent profiles. Nevertheless, even here, it appears that with appropriately chosen data granularity—for example, at the level of sectors, claim types, or procedural issues—models can be created that provide useful information about the probability of success, expected duration of proceedings, or the risk of failure when setting aside an award in a specific seat of arbitration.

Finally, it must be recognized that predictive analytics does not operate in a vacuum. It generates the most value in combination with well-established legal department processes, quality document management, and clearly defined KPIs, such as average costs per dispute, duration of proceedings, success rate of settlements, or return on investment in legal defense. Platforms like Anywhere.legal can play an integrative role in this regard, where data from many disputes across jurisdictions, structured briefers, documents, and predictive outputs converge, which can be gradually calibrated to a specific corporate or institutional environment and the specifics of international trade.

Predictive Analytics in the Dispute Lifecycle: From Prevention to Arbitral Award

Early Case Assessment and Forum Selection

One of the most practical uses of predictive analytics is early case assessment, even before a party decides whether to initiate proceedings, in which forum, and with what procedural strategy. In the international environment, this typically involves choosing between court and arbitration, selecting an arbitral institution, the seat of arbitration, the governing law, and also considering whether the dispute has the potential to be effectively resolved through mediation or other ADR mechanisms. Predictive models can here consider historical data on the success rate of similar types of claims in different jurisdictions, the duration of proceedings in a given forum, costs, the rate of successful enforcement of awards, and potential regulation in the countries concerned, including regulatory or tax specifics.

In the field of litigation, tools already exist that, based on information about the judge, case type, parties, and procedural stage, can estimate the probability of success of key filings, such as motions to dismiss, motions for preliminary injunctions, or appeals. Analogously, in arbitrations, data on arbitrators, institutional rules, sector-specifics, and statistics on setting aside awards in individual seats of arbitration can be used. Here, it is crucial to also consider the probability of recognition and enforcement of the award in target jurisdictions. A study of investment arbitrations, for example, shows that models based primarily on case variables—i.e., the nature of claims, legal basis, and factual circumstances—achieved better predictive capability than models focused primarily on arbitrators or the place of proceedings. This suggests that for a realistic estimate of an arbitration outcome, careful structuring of the factual and legal framework of the case is essential, with regard to the specific international contractual and regulatory environment.

It is precisely at this stage that the practical value of platforms like Anywhere.legal becomes apparent. They allow opening a case, uploading related contracts, correspondence, and other supporting documents, and using AI to automatically extract key factual circumstances, disputed points, and relevant legal questions across multiple jurisdictions. Generative and analytical tools can then process the initial dispute scenario: for example, indicating whether a typical arbitration clause in a given industry tends to direct disputes to institutional or ad hoc arbitration, what the statistical differences in duration and costs are between choosing a particular institution and a court, and whether, given the opposing party's profile and the value of the dispute, it makes sense to pursue a faster proceeding. Thanks to this, legal and tax strategies can be effectively coordinated across affected countries.

In practice, it often turns out that the decision on the forum is not just a legal question, but also a business and reputational one, with potential tax implications. Predictive models can, for example, show that in a certain jurisdiction, arbitral awards are frequently suspended or not recognized in regulated industries, which can influence the choice of the seat of arbitration and governing law. Similarly, models can reveal that court proceedings in a specific country generally take significantly longer than arbitration at an international institution, which can be crucial for management in situations where capital needs to be released or investor relations stabilized. If all documentation, communication, and predictive outputs are concentrated in one platform, it is then significantly easier to defend the chosen strategy internally to company management and externally to partners and investors, even in an international context.

Settlement Negotiations and Upstream Dispute Resolution

Predictive analytics plays a role not only when a dispute is already ongoing, but it increasingly moves "upstream," to the stage where a conflict has not yet been formally initiated, which is especially important in cross-border business relationships. In several industries—for example, insurance, construction, or labor law—systems exist that analyze historical claims, communication patterns, typical signals of dissatisfaction, and procedural steps. Already upon initial claim filing, these systems assign a probability score to a case that it will escalate into a dispute. This allows claims teams or legal departments to target timely intervention, offer mediation, modify contractual terms, or implement another conflict resolution mechanism before it becomes irreversible.

Experience shows that where parties have a realistic idea of their chances in potential proceedings, there is also greater motivation to seek a rational settlement. Predictive models that estimate the probable outcome of a dispute and the expected scope of compensation can therefore be valuable groundwork for negotiations. Studies and practical experience suggest that better informedness about procedural risk can increase the rate of settlements, especially if parties are able to share a common understanding or agree on independent mediation that works with similar data. For example, data from an American arbitration institution shows that the vast majority of cases—over 80%—end in settlement before an award is issued, with AI tools increasingly contributing to earlier identification of settlement opportunities.

Moreover, mediation and facilitative negotiations are a natural environment for generative AI, which can summarize complex case files, reframe arguments, propose creative solution variants, and help parties find common ground without replacing the human judgment of the mediator. In an international environment, where different legal cultures, legislations, and language barriers meet, AI can also help bridge communication differences, for example, by automatically generating multilingual summaries or detecting areas where parties' arguments actually overlap more than unilateral submissions might suggest. This is crucial for coordinating cross-border solutions.

A platform like Anywhere.legal enables such an "upstream" approach by allowing parties to open a case even in the pre-litigation phase, upload relevant documents, contracts, communication, and internal analyses, and have AI initially structure the disputed points and risks. Based on this, predictive scenarios can be generated, for example, what is the probability of success in arbitration versus court proceedings in various jurisdictions, what are the typical median amounts awarded in similar cases, or what would be the expected duration of proceedings. In practice, this creates space for a more rational settlement, motivated not only by momentary negotiating power but also, at least partially, reflecting data, with consideration for the international context.

Dispute Portfolio Management, Triage, and Case Management

In larger corporate groups or in the insurance industry, it's not about isolated disputes, but about entire portfolios of claims where limited resources—lawyer time, external legal service budgets, and managerial attention—need to be allocated where it makes the most sense. Predictive analytics here is a tool for triage, i.e., dividing cases into those that can be quickly and routinely processed, those requiring specialized care, and strategic disputes with potentially significant impact. In insurance, for example, systems use a combination of structured data (type of damage, jurisdiction, claimant history) and unstructured texts (adjuster notes, call transcripts) to assign each case a score indicating the probability of litigation and recommend an appropriate procedural approach.

In the arbitration environment, a similar approach can be used to filter potential arbitral disputes, where AI identifies contracts with a higher risk of dispute, analyzes the content of arbitration clauses, and highlights clauses that may be unenforceable or procedurally problematic under the laws of various countries. Contract management tools already highlight high-risk clauses, inconsistent language, or unusual risk allocations in contract texts, thereby helping lawyers prevent future disputes even at the negotiation stage. In combination with predictive analytics of outcomes, it is then possible to model what types of disputes most frequently arise from certain types of clauses and how they are subsequently resolved through arbitration, with consideration for international practice.

Digital case management systems that centralize documents, communication, procedural actions, and calendars are gradually becoming standard in international arbitrations. Some institutions, such as the ICC with its Case Connect platform, offer integrated solutions that enable online file management, collaboration between parties, the tribunal, and the institution, and in the future, connection to analytical tools. Specialized platforms like Anywhere.legal can fulfill a similar function, combining document workflow with AI tools for automatic reading, categorization, and summarization of filings and evidence, and with the ability to invite experts from multiple jurisdictions without losing oversight of the case file and without compromising data confidentiality.

Because case files are structured and enriched with metadata information, a data foundation is gradually being built upon which predictive models tailored to a specific enterprise or institution can be developed, respecting the specifics of cross-border cases. Legal departments can thus not only estimate the outcomes of individual disputes but also track KPIs associated with arbitrations—average time to award, median costs, success rate of pre-award settlements, or award annulment rates—and accordingly adjust contractual policies, the selection of arbitral institutions, or external representatives, all with regard to international comparisons. Predictive analytics thus becomes part of a broader ecosystem of legal operations.

Arbitrator Analytics, Hybrid AI Arbitrator, and Award Drafting Support

A specific chapter of predictive analytics is judicial or arbitrator analytics, i.e., the analysis of the historical behavior of judges or arbitrators. In the judicial environment, robust tools already exist that create profiles of judges based on their decision-making history, relationship to certain types of claims, acceptance rate of certain procedural motions, or the duration of proceedings. In arbitrations, the situation is more complex due to less data availability. However, in certain segments, especially investment arbitrations and highly specialized industries, empirical studies and arbitrator databases are emerging. Analyzing the composition of arbitral tribunals is particularly relevant in an international context, where arbitrators may come from different legal cultures and have different interpretive approaches to legal norms.

Alongside arbitrator profiling, tools are also emerging that directly support the work of the tribunal. An example is the American Arbitration Association (AAA)'s "AI Arbitrator," designed as an assistive tool for document-only disputes in construction. This system uses AI to analyze party submissions, identify key claims and evidence, create a structured case summary, and generate a draft award, which then undergoes review and finalization by a human arbitrator. The goal is to reduce costs and shorten decision times, with the AAA stating that initial results show a reduction in time to award by approximately 20–25% and cost reduction by 35–45% compared to traditional document-only arbitration.

Similarly experimental is a digital arbitration framework based on smart contracts, where AI in combination with rules encoded in blockchain resolves simple disputes with minimal human intervention. Research in this area indicates that for certain types of disputes, agreement between AI and expert decisions has exceeded 90%, with a dramatic reduction in arbitration duration. However, these approaches are currently more laboratory-based and applied in simple, low-value disputes where parties accept a higher degree of automation in exchange for speed and low costs. Their implementation in complex international disputes is currently limited.

It is important to emphasize that although "AI arbitrator" is sometimes discussed, most serious initiatives work with a hybrid "human-in-the-loop" model, where AI serves as a tool for analysis and drafting, but the final decision and responsibility always rest with a human. This approach is consistent with the requirements for procedural fairness, transparency, and the possibility of justifying the award, which a fully automated system would likely fail to meet in most complex cross-border disputes. Predictive analytics in practice therefore primarily helps by accelerating the tribunal's orientation in the file, allowing faster identification of key legal issues, and potentially comparing its intuitive view with a statistical picture of similar cases, also considering international legal principles.

For platforms like Anywhere.legal, this means that their role is not to replace arbitrators, but to provide tools that facilitate the work of tribunals and parties with the case file, allow for clear structuring of claims, defenses, and evidence, and, if necessary, connect predictive models as a supporting tool for estimating procedural risks. In practice, it often turns out that the very process of structuring a case file using AI—for example, automatic identification of claims, timelines, and factual disputes—leads to more efficient proceedings and sometimes even partial settlement, because parties gain a clearer picture of where their positions converge and where they fundamentally diverge, which is crucial in international disputes for finding a common solution.

Predictive Analytics Specifically for Estimating Arbitration Outcomes

Structural Peculiarities of Arbitrations and Their Impact on Prediction

Estimating an arbitration outcome is a more complex task than predicting the outcome of a typical court case, for several reasons. Arbitration is primarily a contractual mechanism: the tribunal's authority and procedural rules derive from the arbitration clause, the rules of the chosen institution, and the seat of arbitration, which determines the applicable procedural law and has a critical influence on the review and enforcement of the award. In the international environment, factors such as the application of the New York Convention on the Recognition and Enforcement of Foreign Arbitral Awards, differing court approaches to reviewing awards, and varying public policy standards in individual states come into play, requiring the involvement of experts from multiple jurisdictions. This means that the "outcome" of a dispute is not merely a question of how the tribunal decides, but also whether and how the award will be recognized and enforced in relevant countries.

Empirical data from investment arbitrations show that some structural variables—for example, the type of claim, legal basis (bilateral investment treaty versus free trade agreement), sector (energy, mining, infrastructure), and essential procedural steps—have a significant influence on the probability of success for the claimant or respondent. A Duke Law Journal study, for instance, found that models using case variables were better able to predict the outcome of an investment arbitration than models based primarily on arbitrator characteristics or the place of proceedings, suggesting that the content of the dispute and the legal framework are more fundamental for prediction than individual arbitrator profiles. At the same time, some characteristics of tribunals and seats of arbitration can influence the likelihood of an award being set aside or the approach to procedural issues, such as evidence or the standard of review, which differs across countries.

Another complication is confidentiality. Many commercial arbitral awards are not public, or only summaries or redacted versions are published. This limits the amount of data available to external predictive models. Some institutions and law firms therefore build internal databases of awards, procedural decisions, and other documents, which serve as a data foundation for their own predictive models; however, these models are not publicly accessible. Platforms like Anywhere.legal can play an important role by allowing clients and partner firms to securely and structurally store dispute outcomes, procedural history, and relevant documents from various jurisdictions, and then train predictive models on this internal corpus. These models are tailored to a specific segment or company and sensitive to international contexts.

From a practical perspective, it is also important to realize that an arbitration "outcome" has multiple dimensions. A party may formally lose a dispute but gain significant procedural advantages (e.g., in terms of costs or precedential effects), or conversely, win a dispute but encounter difficulties in enforcing the award in a particular jurisdiction or face negative tax implications. Predictive analytics should therefore not reduce the outcome to a binary "win/lose," but should work with multiple outputs—the probability of an award being granted to a certain extent, the probability of successful enforcement in key jurisdictions, the probability of an award being set aside at the seat of arbitration, and the impact on reputation or business relations, as well as tax consequences. More complex models can, for example, combine legal and financial prediction with risk management and the science of risk in international law, which emphasizes that in a global environment, it is necessary to systematically work with uncertainty and scenarios, rather than relying on a single expected outcome.

Empirical Attempts to Predict Arbitration Outcomes and Their Limits

Existing empirical studies in arbitration—especially investment arbitration—show that even despite the complications mentioned above, certain patterns of tribunal decision-making can be statistically captured. Analysis of investment awards, for instance, demonstrated that models based on a combination of factual and legal variables (e.g., type of breach, interpretation of fair and equitable treatment clauses, expropriation issues, procedural history) can estimate with a certain degree of accuracy whether a tribunal will award damages to a claimant, and if so, whether the amount will be high or rather limited. It was also found that supplementing information about the tribunal's composition or the place of proceedings, while providing additional information, is not the main source of the model's predictive power, though this does not mean it is entirely irrelevant for assessing the probability of successful award enforcement.

In practice, this means that lawyers, when estimating an arbitration outcome, should not rely solely on the "profile" of a specific arbitrator or institution. Instead, they should focus on a thorough analysis of the merits of the dispute, relevant contractual and international provisions, and factual circumstances. Predictive models can be useful in this regard by helping to identify decisive variables that have historically had the greatest impact on dispute outcomes. This can direct the preparation of evidence and argumentation to areas that have a higher probability of influencing the tribunal, with respect for the procedural rules of the given arbitration.

The limits of these models, however, are fundamental. Firstly, historical data may reflect a certain period of legal development that has since changed, for example, in the area of investment treaties, where new generations of agreements often contain different protection standards and procedural provisions, or in the tax aspects of international transactions. Secondly, in both investment and commercial arbitrations, the complex factual context plays a significant role, which may not be fully captured in the data—for example, political circumstances, reputational pressures, ESG regulations, or sanction regimes, all of which can have a decisive impact on the enforceability of an award in a specific jurisdiction. Thirdly, models trained on one type of dispute and in a certain period may not be reliably transferable to other sectors or geographical areas where different legal principles or regulatory approaches apply.

Moreover, in arbitrations, the degree of diversity of legal cultures and approaches is more pronounced than in national judicial systems. Arbitrators come from different jurisdictions, have different academic backgrounds and experiences, and although they strive to apply uniform international standards, their decision-making style and degree of deference to the state or investor may differ. Predictive models therefore provide a map of the most probable scenarios rather than a "crystal ball," which should always be confronted with qualitative expert judgment, ideally through the coordination of experts from various countries.

Platforms like Anywhere.legal can partially mitigate these limitations by allowing predictive outputs to be combined with expert opinions from arbitration and legal specialists in the relevant field and jurisdiction. A typical practice might involve AI processing internal and public data in the first phase, proposing a basic estimate of success probability and damages scope. This estimate is then supplemented and refined by an expert opinion from a chosen specialist, who can highlight specific nuances of local law, tax regulations, or current case law not captured by the model. This ensures a comprehensive assessment of the situation.

Predictive Analytics, Preliminary Assessments, and the Settlement Potential of Arbitrations

An interesting connection between predictive analytics and arbitrations is represented by the practice of preliminary assessments by the tribunal. Here, arbitrators, based on initial written submissions and evidence, provide parties with an informal, non-binding opinion on the strengths and weaknesses of their cases. The goal is to accelerate the clarification of disputed issues, contribute to rationalizing parties' expectations, and often support settlement. Preliminary assessments usually take place after the first round of main submissions and before evidence taking, allowing the tribunal to share its preliminary view with the parties without prejudicing the final award. This practice is especially valuable in international disputes where consensus is often harder to achieve.

Predictive analytics can play a dual role here. From the parties' perspective, it can provide statistical guidance on how similar disputes have fared elsewhere, thereby helping to better interpret the tribunal's preliminary opinion—for example, whether their situation is within the mainstream of jurisprudence or if it is an exceptional case. From the tribunal's perspective, analytical tools can help identify relevant case law from various countries more quickly, summarize the parties' main arguments, and potentially highlight significant differences from typical patterns, which can prompt deeper argumentation in the final award.

In practice, it often turns out that combining human preliminary assessment with data-backed risk estimation leads to significantly more effective settlement negotiations. Parties are motivated to re-evaluate their positions if they see that both the tribunal and statistical analysis suggest a certain range of probable outcomes, and they can focus on commercial solutions that reflect this framework. Given that most arbitrations still end in settlement before an award, the development of these types of tools is crucial for shortening case duration and reducing costs.

Platforms like Anywhere.legal can support preliminary assessments by allowing parties and the tribunal to share a structured dispute brief, including predictive scenarios, in one secure environment. AI can create a clear summary of claims, defenses, key evidence, and relevant case law, based on which the tribunal prepares its preliminary assessment. Parties thus gain a comprehensive picture of procedural risks and can quickly re-evaluate their willingness to settle. In international disputes, where coordination among multiple legal teams and jurisdictions is challenging, this centralization of information is essential for efficient and coordinated resolution.

Risks, Ethics, and Governance of AI Predictions in Arbitrations

Algorithmic Bias, Transparency, and Confidentiality Protection

The deployment of AI in arbitration raises a host of ethical and legal questions, which are even more complex in an international environment. Primarily, there is the risk of algorithmic bias, where the model reproduces or even amplifies historical inequalities and stereotypes contained in the training data. For example, if in certain types of disputes, state-owned enterprises or large multinational corporations historically won more frequently against smaller entities, the model might begin to assign a higher probability of success to these entities without a legitimate legal reason. Similarly, implicit advantages might arise for parties from certain jurisdictions or with a specific type of legal representation if these factors correlate with historical success, which is particularly sensitive in an arbitration environment with constant arbitrator turnover and differing legal systems.

Another critical aspect is transparency. In the legal environment, it is often necessary to be able to explain the reasons behind a particular decision. However, for complex models, especially deep neural networks, the internal logic of decision-making is difficult to interpret, which is the "black box" problem. This is acceptable when the model is used only as an internal support tool for a legal department, but it becomes a problem if it were to have a direct impact on the procedural rights of parties or be used by a tribunal without adequate explanation. Discussion in academic literature therefore emphasizes the need for "explainable AI" in law, i.e., models that allow at least partially retrospective explanation of which factors had the greatest impact on a prediction, with regard to international standards of due process.

A specific problem in arbitrations, and especially in cross-border cases, is confidentiality protection. Data used to train and run predictive models may contain sensitive information about business strategies, proprietary technologies, personal data, or regulatory incidents. Once this data is used for training, there is a risk that sensitive information may be "imprinted" into the model and indirectly reconstructed from its outputs, especially with generative models. International practice and data protection regulations (including GDPR and its equivalents in other jurisdictions) therefore emphasize robust anonymization, pseudonymization, contractual restrictions on data use, cross-border data transfer, and careful management of access to models.

A platform like Anywhere.legal can address these risks by providing a clearly defined framework for working with data: separating training data from specific cases, utilizing private instances of models that do not send data to third parties and are hosted in secure data centers complying with stringent standards, and implementing control mechanisms that prevent unintended information leaks. At the same time, it can structure the process so that AI outputs are always labeled as a draft or supporting analysis, not a binding legal conclusion, and that they undergo human review by a responsible lawyer or arbitrator. In an environment where various jurisdictions and courts are already defining the boundaries for AI use in legal practice, such a governance framework is crucial for maintaining trustworthiness, especially in international disputes.

De Facto Regulation of AI by Courts and the International Dimension

An interesting phenomenon of recent years is that while the regulatory debate on AI often takes place at the level of legislation and soft law, courts and administrative bodies are already de facto regulating AI through their decision-making. Analysis of the "AI on Trial" database shows that hundreds of cases worldwide concern the deployment of AI in various contexts—from biometric surveillance to automated immigration decision-making to the use of AI in the legal sector—and that courts are creating de facto rules in these matters for what is compatible with the right to due process, privacy protection, or intellectual property.

In the context of arbitrations, this de facto regulation is particularly important because courts at the seat of arbitration have the power to review awards for fundamental procedural guarantees and public policy. If, for example, a tribunal were to rely on a predictive model in its decision-making that was fundamentally flawed, non-transparent, or in violation of legal data protection requirements in a given jurisdiction, it could lead to the invalidity or setting aside of the award. For this reason, most institutions, including the AAA with its AI Arbitrator, emphasize that AI serves only as a supporting tool and that the final decision and justification are always the result of human judgment.

The international dimension of the problem lies in the fact that different jurisdictions may have different approaches to the use of AI in legal practice, personal data protection, and procedural guarantees. This is especially relevant for platforms like Anywhere.legal, which work with cases across borders and must ensure that the way AI is used in case preparation is compatible with the requirements of the seat of arbitration, the law of the states where the award is enforced or where the parties are located, as well as the tax and regulatory rules of the relevant countries. In practice, this means the need to continuously monitor the development of case law in key jurisdictions, analyze decisions concerning AI in law, and implement best practices in data protection and procedural fairness.

Impact on Negotiating Balance, Access to Justice, and Settlement Rates

Predictive analytics in arbitrations has the potential to significantly influence the negotiating balance between parties. A party that possesses a quality data foundation, well-thought-out predictive models, and the ability to quickly structure a dispute can have a significant advantage in settlement negotiations, deciding on case duration, or choosing procedural steps. Some commentators suggest that AI will increasingly influence settlement rates because it will allow for a realistic estimate of the "expected value" of a dispute, thereby reducing the scope for illusions and so-called optimism bias on both sides.

At the same time, there is a risk that advanced AI tools will initially be more accessible to large corporations and institutions, which could deepen inequality in access to justice. If one party works with detailed predictive analysis and the other relies only on limited experience, its negotiating position may be significantly weaker, even if it has a strong legal case. For this reason, it is important that predictive analytics becomes more accessible over time, even for smaller entities, for example, through platforms with transparent and predictable pricing models that allow AI to be used even without massive investments in their own data teams.

On the other hand, it can be argued that properly designed AI tools can contribute to increasing access to justice by making the initial phase of case analysis cheaper and faster, a phase that is otherwise costly and time-consuming. If a lawyer or business obtains a relatively robust estimate of procedural risks in an international context within hours or days, they can make a more qualified decision on whether to initiate a dispute, seek a settlement, or choose another path. In combination with an online platform where an expert from another jurisdiction can be quickly involved and a structured brief shared, predictive analytics can paradoxically lower the barriers to entry into international arbitrations.

However, for these benefits to outweigh the risks, it is essential that platforms like Anywhere.legal provide transparent information on how their models are trained, what data is used, and how outputs should be interpreted. In practice, this means clear disclaimers that it is a supporting analysis, not legal advice or a binding prediction, and an emphasis that the final strategic decision must be made by a human expert with regard to international legal and tax contexts. From an ethical perspective, it is also appropriate for parties to consider whether and to what extent they should inform the tribunal that they have used AI predictive tools, for example, within a contractual agreement on technology use or legal profession ethical codes.

Key Risks and the Role of Anywhere.legal in Their Management

Given the range of risks mentioned, it is useful to summarize the typical combinations of risk and impact that appear in practice and how a platform like Anywhere.legal can systematically address them. The following table presents several common scenarios and suggests what role structured case management and AI assistance can play in cross-border cases.

In practice, it turns out that the combination of technological solution and process management is essential. AI alone, without clear case management and a governance framework, can actually increase risks. In contrast, a platform that connects structured document work, predictive analytics, and coordination across teams and jurisdictions enables conscious risk management. For this reason, the combination of a centralized environment, quality technological background, and access to an international network of legal and tax experts is crucial for the effective use of predictive analytics in arbitration.

MicroFAQ: Common Questions on AI Risks in Arbitration

At this stage, it is useful to briefly answer several practical questions that often arise when considering AI in arbitrations.

1. Does using AI in case preparation itself jeopardize the enforceability of an award?
   The answer depends on the specific jurisdiction and the nature of use, but generally, if AI serves as an internal support tool for parties or the tribunal, and the final decision is the result of human judgment, there is no reason to question enforceability. A problem would arise if the human element were merely formal and the decision were factually made by a non-transparent model without possibility of explanation, which could contradict due process requirements in various countries.

2. Does a party need to inform the tribunal that AI was used in case preparation?
   There is no universal obligation to disclose such use yet; however, some ethical codes and professional standards recommend a degree of transparency, especially if AI were to be used directly in evidence gathering or analysis. A reasonable compromise might be an internal policy that defines what types of tasks AI may be entrusted with and what level of human control is necessary, considering the specifics of international arbitrations.

3. What about data protection and using cloud AI services in sensitive international disputes?
   The answer here is quite clear: without robust contractual agreements, technical security, and guarantees that data will not be used to train public models, using open cloud services is highly risky, especially for cross-border data transfer. The solution is to use platforms that provide a private instance of models, the possibility of local deployment, or other forms of separating client data from the public environment, all in compliance with relevant international standards and local data protection legislation.

Anywhere.legal as an Integration Layer for Predictive Analytics in Arbitration

Structuring Briefs, Documents, and Data for Prediction

For meaningful use of predictive analytics in arbitrations, data quality is crucial. In practice, this means that the factual situation, claims, defenses, procedural history, contractual framework, and relevant legal and tax issues must be captured in a structured manner. Many legal teams, however, still work with unstructured email threads, scattered files, and unformalized notes, which significantly complicates both internal analysis and the use of AI, especially when data and experts are located in different countries. Platforms like Anywhere.legal solve this problem by providing a unified environment where a case can be established, its parameters defined, and all relevant documents uploaded, which are then automatically processed using AI, with regard to international linguistic and legal standards.

Generative and analytical models can automatically extract key data from uploaded contracts, submissions, correspondence, and other documents—for example, the value of the dispute, governing law, agreed arbitration clause, identification of parties, a timeline of events, and disputed points. This information is then stored in a structured format suitable for predictive models. Simultaneously, further analyses can be run on this structured brief, such as estimating the probability of success based on historical data, scenarios for costs and timelines, or analyzing the suitability of different forums and their impact on the tax and regulatory aspects of the case.

Crucially, this process takes place in an environment that respects confidentiality and allows for precise access management. Given that predictive analysis can raise concerns if based on opaque data processing in external systems, it is advantageous if AI can be operated within a platform that ensures data does not leave the client's secure storage, is not used for training public models, and the entire process is auditable, with guarantees of compliance with international data protection standards. In this sense, Anywhere.legal represents not just a tool for convenient work, but a robust infrastructural layer that creates conditions for the safe and systematic use of predictive analytics in international and cross-border disputes.

Practical Scenario: An International Contractual Dispute Heading for Arbitration

To clarify how predictive analytics combined with a platform can work in a specific case, it's useful to imagine a typical situation. A company based in one state entered into a long-term supply contract with a supplier from another jurisdiction, including an arbitration clause in favor of institutional arbitration in a third country. During performance, delays occur, disputes arise over delivery quality, and payments are subsequently suspended. The parties exchange several sharp letters, the matter escalates, and it is clear that the dispute could escalate into arbitration.

In practice, the internal legal team or external counsel often starts working "on the fly" with emails, contract versions, and notes, and it takes weeks to create a consistent timeline, analysis of contractual provisions, and an initial estimate of procedural risks. However, if the case is initiated on Anywhere.legal, all available documents—contracts, purchase orders, delivery notes, emails, meeting minutes—can be uploaded within the first few days, and AI can generate a structured summary that identifies key contract provisions, disputed points, and relevant milestones. This is done with consideration for any differing language versions of the documents.

Based on such a structured brief, a predictive model can then be run. This model uses a combination of internal data on previous similar disputes (e.g., in a given sector or institution) and publicly available data to estimate the probability of success in arbitration, the expected time to award, the probable extent of damages, and cost scenarios. The result is not a "verdict," but a data-backed scenario that can be presented to management: what is the expected value of the dispute, what are the extreme scenarios, and what are the alternatives in the form of settlement or other arrangements, including related tax and regulatory impacts in various jurisdictions.

At the same time, AI can analyze the arbitration clause and highlight potential procedural problems, such as ambiguity regarding the number of arbitrators, language of proceedings, atypical cost allocation, or uncertainty regarding the governing law. The legal team can invite a local expert within the platform from the seat of arbitration country or the supplier's jurisdiction, who can comment on typical court approaches in that country to award recognition, regulatory obstacles that might affect award enforcement, or cultural or tax factors that might play a role in settlement negotiations. Coordinating multiple experts from different countries is thus a significant added value.

This integrated approach—combining AI structuring, predictive analytics, and collaboration with an international network of experts—enables a company not only to better estimate the outcome of potential arbitration but also to make more qualified decisions on whether to escalate the dispute or attempt a settlement under certain conditions, with regard to complex cross-border impacts. It also creates a documented record of how the strategy was chosen, which is important for internal governance and potential management liability, as well as for ensuring compliance with tax and regulatory requirements.

Predictive Analytics in Legal Operations and Performance Measurement

From a legal operations perspective, predictive analytics in arbitrations is a tool not only for individual cases but also for strategic management of an international company's entire dispute portfolio and legal risks. Modern legal departments increasingly work with KPIs such as total legal costs relative to turnover, average cost per dispute by type, average time to dispute resolution, dispute success rate, or internal client satisfaction. AI can help identify major "time and cost sinks" in these areas—for example, overly long information gathering phases in various jurisdictions, inefficient coordination with external law firms across countries, or excessive document production in arbitrations.

Studies on the efficiency of legal services show that a significant portion of lost billable hours and inefficiency arises from activities that are highly automatable, such as research, drafting initial submissions, proofreading, and billing oversight. In arbitrations, this is compounded by the time spent on case management—managing deadlines, coordinating with arbitrators and the institution, creating joint case files—and on internal reporting, often in different languages and time zones. A platform like Anywhere.legal can significantly streamline these processes by integrating automatic activity tracking, time sheet generation, AI support for drafting submissions, and centralizing communication and documentation, even on an international scale.

Predictive analytics then allows these operational data to be transformed into strategic management. For example, it can show that arbitrations in a certain forum or jurisdiction have significantly higher external legal service costs without a corresponding increase in success rates. This can lead to a review of contractual policies and preferences for arbitral institutions and seats of arbitration. Similarly, analysis might reveal that in a certain type of dispute, settlements are concluded only after extensive evidence production, even though predictive models would have indicated a low chance of a better outcome than a settlement in an earlier phase. Based on these insights, a legal department can establish internal "decision gates" where, before proceeding with arbitration beyond a set budget, a predictive analysis and settlement review are mandatory, also including recommendations from tax and regulatory experts.

Anywhere.legal, in this context, provides not only technology but also a data infrastructure that allows these metrics to be continuously collected, analyzed, and visualized from various international cases. This makes predictive analytics a common part of legal risk management, and it is not uncommon for internal dispute outcome estimates to be significantly refined after just a few years of systematic data collection. This has a direct impact on budgets, reserves, and investment decisions, even in international comparisons.

MicroFAQ: How Anywhere.legal Fits into Existing Processes?

In this section, it is useful to briefly explain how a platform like Anywhere.legal can function alongside existing systems and processes in a legal department.

1. Does implementing such a platform require replacing existing DMS or billing systems?
   In practice, this is usually not necessary; Anywhere.legal can be seen as an overlay layer that focuses on cross-border cases, arbitrations, and disputes of higher complexity, where it brings the greatest value in the form of AI structuring, predictive analytics, and international expert coordination across jurisdictions. Documents can remain in the main DMS, while metadata and case management are managed in Anywhere.legal, often with the possibility of API integration.

2. Will AI tools within the platform replace the work of lawyers and in-house counsel?
   Practical experience shows that AI in this area functions as a "force multiplier," not a replacement—it accelerates initial analysis, draft preparation, and case file structuring. However, interpreting outputs, making strategic decisions (including tax and regulatory assessments), and communicating with the opposing party and the tribunal remain in human hands. The advantage is that lawyers can dedicate more time to higher-value activities and complex legal considerations, while routine tasks are partially automated.

3. What about pricing? Predictive analytics can sometimes seem like "black magic" that will be very expensive.
   In practice, thanks to shared infrastructure and the gradual building of data sets, a transparent and predictable pricing model can be achieved, for example, in the form of a subscription or case-based fee, which is well defensible to management, also considering budgets for international disputes. Anywhere.legal typically helps estimate the scope of work and costs of a dispute in the initial phase, which is crucial for financial planning.

Future of AI in Arbitrations: Trends Towards 2030

Evolution of the International Disputes Market and the Role of Technologies

Analyses of global dispute trends suggest that by the middle of the next decade, the increase in complex cross-border disputes will continue, especially in regulated industries, ESG, the digital economy, and geopolitically sensitive sectors. At the same time, the importance of arbitrations is expected to grow due to the need for a neutral forum for dispute resolution between parties from different legal and political systems, with increasing emphasis on award enforcement in various jurisdictions. Statistics from international institutions show an increase in the number of cases, geographical diversification of parties and arbitration seats, and growing pressure for efficiency and predictability of proceedings.

Technology plays an increasingly significant role in this context. Digitalization of case management, remote hearings, electronic filings, and online platforms for file management have become standard, with the pandemic and subsequent development of remote work accelerating this trend and necessitating efficient international coordination. AI tools are now building on this infrastructure, focusing on text processing—automatic summarization, searching extensive (often multilingual) case materials, drafting submissions—and on predictive analytics, which we analyzed in detail above.

In arbitrations, institutions are expected to increasingly offer integrated platforms for digital case management, which will be at least partially connected with analytical tools, whether proprietary or from third parties. The AAA AI Arbitrator example shows that institutions are ready to experiment with hybrid models of AI-assisted decision-making if it brings measurable time and cost savings while maintaining key procedural guarantees crucial for international award recognition. Similar projects are expected to emerge in other institutions, especially for smaller document-only disputes and in sectors with a large number of similar cases, where the impacts on various jurisdictions can be clearly defined.

Generative AI, Legal NLP, and the Quality of Reasoning

The development of generative AI and specialized legal NLP models fundamentally changes how legal document text can be processed. Modern models can summarize extensive submissions with relatively high accuracy, identify key legal issues, find relevant case law, and generate draft reasons or procedural decisions. In combination with domain-specific training on legal texts and arbitral awards, a fairly accurate capture of reasoning style and argumentative structure can be achieved, which can save tribunals a significant amount of time in drafting awards, also considering citations of legal sources from different countries.

At the same time, generative AI opens new possibilities in multilingual dispute processing. In cross-border arbitrations, documents often arrive in several languages, and arbitrators or lawyers must rely on translations. Specialized models are capable not only of translating but also of preserving legal meaning and terminology, identifying inconsistencies between language versions, and highlighting differences that may have legal impact. This is crucial especially in situations where different language versions of a contract have different legal weight, or where the interpretation of an arbitration clause depends on subtle linguistic nuances, or when tax and regulatory terms in various languages need to be considered.

For predictive analytics, this means that models will be able to process a wider range of documents across languages and legal systems, thereby providing more robust grounds for estimating dispute outcomes. However, the importance of governance also increases, because generative models can hallucinate, meaning they can create plausible-sounding but factually incorrect information, which can be a critical problem in a legal context, especially when it comes to sensitive legal or tax conclusions. Platforms like Anywhere.legal must therefore implement control mechanisms that allow distinguishing "hard data" from generative suggestions, and ensuring that key legal and factual conclusions are always verified by a human expert, ideally considering jurisdictional specifics and legal traditions.

Automated Frameworks and Smart Contracts in Simpler Disputes

Alongside complex arbitrations, fully or partially automated frameworks based on smart contracts are also developing, especially in the area of low-value or technically standardized disputes, which often occur in cross-border trade. These systems typically work with predefined rules encoded in a smart contract, and AI serves to interpret input data and apply the rules. The result can be an automated decision that is enforceable directly within the given platform (e.g., for blockchain projects) or serves as groundwork for traditional arbitration or court proceedings.

Research shows that in these contexts, very high agreement between AI decisions and human experts can be achieved, and dispute resolution time can be dramatically shortened. From a predictive analytics perspective, it is interesting that here, there is a certain convergence between "prediction" and "decision-making": the model not only estimates how the dispute would turn out but directly generates a decision based on encoded rules. Nevertheless, in more complex commercial and investment arbitrations, where there is room for interpretation, discretionary judgment, and consideration of broader political or economic contexts and international law, the human decision-maker will remain irreplaceable.

For Anywhere.legal and similar platforms, this opens up possibilities for integration scenarios. Simpler disputes—for example, minor supplier disagreements within a supply chain with various suppliers from different countries—will be resolved semi-automatically through smart contracts and AI, while more complex disputes will be escalated to full arbitration using predictive analytics as support for party and tribunal decision-making. An important task for legal teams will be to design the contractual architecture so that it is clearly defined which types of disputes fall into which regime, and so that the possibility of reviewing automated decisions is maintained where it makes sense, with regard to international legal norms and regulatory requirements.

Conclusion: How to Systematically Utilize Predictive Analytics in Arbitrations

AI-powered predictive analytics already significantly influences how businesses and legal teams approach litigation, and it is increasingly penetrating arbitration, especially in an international context. In the arbitration environment, its use is more complex due to confidentiality, limited data availability, and its international dimension. Nevertheless, robust methodological foundations and practical tools exist that allow for a relatively reliable estimation of probable dispute outcomes, cost and duration scenarios, including specific tax and regulatory impacts. The key is to understand these tools as a supplement, not a replacement, for legal judgment: they provide a statistical map of risks and opportunities, based on which parties can make more informed decisions about whether to initiate a dispute, what strategy to choose, and when to seek settlement, all within the context of the global legal and business landscape.

In practice, it often turns out that the greatest benefit of predictive analytics lies in forcing the team to systematically structure the case, identify disputed points, clarify procedural and financial implications, and realistically set management expectations, with regard to the complex cross-border dimension. Without quality structured input data from various sources and jurisdictions, any prediction is more an illusion of accuracy than real help. This is where platforms like Anywhere.legal come into play, combining AI for document processing, case management, and international coordination of an expert network. They enable capturing the case brief, centralizing documents, using AI for faster comprehension of content and context, preparing initial drafts of analyses and submissions, and simultaneously engaging local partners from legal and tax experts where local law or practice knowledge needs to be supplemented.

At the same time, risks associated with algorithmic bias, model non-transparency, and data confidentiality protection in various jurisdictions must be kept in mind. Without a robust governance framework—clear labeling of AI outputs, human review, security measures, and compliance with local and international regulations—deploying AI in arbitrations can cause more harm than good. A good practice example is hybrid models like the AAA AI Arbitrator, which combine the speed and analytical capacity of AI with final decision-making in the hands of a human arbitrator, achieving measurable time and cost savings without sacrificing procedural fairness and international enforceability.

For businesses and legal teams considering the systematic use of predictive analytics in arbitrations, several practical recommendations emerge. It is advisable to start by structuring data and cases, implementing unified case management that allows for the collection and analysis of dispute outcomes (including financial and tax aspects), and gradually testing predictive tools first as internal support for early case assessment and settlement negotiations. Furthermore, it is important to establish internal policies that define how AI is used in case preparation, how data is protected, and how outputs are communicated to management. Finally, in an international environment, it is essential to involve local experts who can supplement data models with the specifics of given jurisdictions and prevent errors in choosing a forum or seat of arbitration, as well as in assessing tax and regulatory impacts.

Anywhere.legal, in this context, positions itself not as an isolated "AI tool," but as a procedural and technological platform that allows all the elements mentioned above to be combined into one functional ecosystem. Experience shows that where briefs, documents, predictive outputs, and communication among experts across countries are concentrated in one environment, it is significantly easier not only to estimate the outcome of a specific arbitration but also to learn from past cases and continuously improve processes, contractual policies, and risk management. At a time when international disputes are becoming increasingly complex and expensive, this systematic, data-driven, and coordinated approach can determine whether arbitration will be a fast and relatively predictable dispute resolution tool for a business, or a source of long-term uncertainty and costs.

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FAQ

1. How accurate is predictive analytics in arbitration disputes today?
   The accuracy of predictive models depends on the type of dispute, data quality, and specific methodology. In court disputes, some specific areas and jurisdictions report accuracies around 80–90%. In arbitrations, the situation is more complex due to confidentiality and data fragmentation, but even here, for certain segments and well-structured data, useful estimates can be achieved. It is important to view these models as a supporting tool, not a replacement for legal judgment.

2. Can AI decide arbitrations alone without a human arbitrator?
   In simple, low-value disputes based on smart contracts, a high degree of automation is being experimented with, and in some cases, it works very effectively. However, in complex commercial and investment arbitrations, the prevailing view is that AI can, at most, assist in analysis and drafting, while the final decision and reasoning must remain in the hands of a human tribunal, which considers all legal, ethical, and international contexts.

3. What specific advantages does predictive analytics bring to company management?
   Predictive analytics enables management to better estimate the expected value of a dispute, cost scenarios, and the duration of proceedings. This is crucial for financial planning, provisioning, and investor relations, especially in cross-border situations. It also helps set realistic expectations regarding the outcome of a dispute and timely consider settlement if data-backed scenarios indicate that continuing the proceedings is unlikely to yield a significantly better outcome, also considering tax and regulatory impacts.

4. How to start using AI in arbitrations if a company is not yet using any predictive analytics?
   The first step is usually to implement unified case management and structured data collection on disputes—value, type, outcome, duration, and costs, with regard to relevant jurisdictions. Subsequently, it makes sense to test AI tools for document structuring and early case assessment in selected cases and gradually build an internal data foundation for predictive models. A platform like Anywhere.legal can significantly accelerate this process because it combines document workflow, AI tools, and access to an international network of experts in one environment.

5. Is using AI in arbitration preparation compatible with confidentiality and GDPR?
   Yes, but only if certain conditions are met: data is processed in a secure environment, it is not used for training public models, anonymization or pseudonymization is ensured where necessary, and clear contractual and technical guarantees exist regarding cross-border data transfer and processing. In practice, this means using platforms that respect these requirements and having an internal policy that defines how AI can be used in sensitive cases with regard to international regulatory frameworks.

6. What role does Anywhere.legal play in estimating arbitration outcomes?
   Anywhere.legal does not serve as an "arbitrator," but as an integration platform for complex cross-border cases: it allows for initiating a case, uploading and structuring documents (including multilingual ones), using AI for analysis and predictive scenarios, and simultaneously engaging local experts from relevant jurisdictions, including tax specialists. This enables companies and legal teams to estimate arbitration outcomes more systematically, quickly, and with less risk of errors caused by chaotic information gathering or uncritical reliance on partial intuition, with regard to the global context.