Leveraging Artificial Intelligence in Cross-Cultural Mediation: Enhancing Neutrality and Efficiency in Dispute Resolution

1. INTRODUCTION

Mediation and conciliation are few of the many modes of Alternative Dispute Resolution (ADR). Though mediation and conciliation seem similar, there are subtle differences between the two. The process of mediation includes the resolution of disputes where a third-party mediator is engaged, who facilitates negotiation between the disputing parties to help them reach a mutual agreement. In mediation, an unbiased expert deals with both parties separately and collectively, thereby persuading them to reach a mutually agreeable outcome. These experts are trained in the art of persuasion, conflict analysis, conflict management, and conflict resolution, and when accredited by the institution, they are called Mediators. Mediation is a quick remedy for dispute resolution compared to formal legal procedures. The matters for which mediation is sought are mostly general in nature and span a range of domains (Sucharitkul, 2001).

However, in contrast, conciliation is mostly utilized when there is a specific legal dispute instead of a general dispute. The role of the conciliator is also different from that of a mediator. The conciliator plays an active and more intrusive role in dispute resolution. A conciliator may offer solutions and propose settlements for parties to agree upon. In other words, conciliation is a method of dispute resolution where a compromise can be made, and parties are free to appoint more than one person as conciliator (Sucharitkul, 2001). If at any stage of the conciliation the conciliator believes that a resolution is possible, they can propose a settlement, and if accepted by the parties, it will become a binding and legally enforceable “settlement agreement”.

In today’s era of global interconnectivity, disputes have obtained a transborder nature instead of being confined within national borders or similar cultural frameworks. As transnational, diplomatic, and interpersonal relations evolve, so does the nature of cross-cultural disputes. Cross-cultural disputes are primarily conflicts that might involve parties having divergent cultural, legal, and linguistic backgrounds. The resolution of such disputes is a tricky process as cultural sensitivity becomes equally important. This has led to the evolution of cross-cultural mediation, a specialized form of Alternative Dispute Resolution (ADR) which acknowledges and bridges cultural differences for promoting mutual understanding and lasting settlements.

The advent of Artificial Intelligence (AI) has prompted a reevaluation of traditional ADR frameworks. Mediation, especially in cases plagued by cultural disputes, can be improved with further efficiency and neutrality with AI due to its data processing, pattern recognition, real-time translation, and predictive analytic capabilities. Generative AI innovations such as ChatGPT and other large language models offer tremendous advancements for cross-cultural interaction through interpreting real-time dialogue, tone modulation, role-play, and unbiased proposal crafting (Zeleznikow, 2021). This paper investigates the possibility of AI serving cross-cultural mediation while maintaining needed empathy without hurting the success of the endeavor. For the purpose of coherence, this research is divided into three parts. The first part delves into the cultural aspects of mediation and ADR case studies ethnographically. The second part attends to the issues of integrating AI into social work, analyzing the international advancements, pros, and cons of each developed system. The final part discusses how regulatory frameworks, particularly in developing jurisdictions like Pakistan, can be adapted to responsibly incorporate AI in mediation without undermining the voluntary and human-centric nature of dispute resolution.

Significant development in the field of information technology has resulted in rapid development in the domain of AI technologies. The emergence and deployment of generative AI programs, such as Chat-GPT, have democratized access to AI for ordinary users, marking a historic milestone. This breakthrough enables everyday users to integrate AI into their daily routines and tasks. Consequently, the widespread adoption of generative AI across various fields has sparked discussions about its potential to supplant human tasks and the wisdom of such a course of action. The legal profession is also grappling with this development, as the true implications of generative AI in this domain are still being evaluated (Zeleznikow, 2021). A major query in this front is the extent to which AI can automate adjudicative functions. In this context, adjudicative functions can denote both judicial decision-making processes as well as other forums of legal adjudication such as Alternate Dispute Resolution (ADR).

Literature Review

Gold (2005) highlights that traditional litigation is a product of Western society and is therefore a more prominent method of dispute resolution in Western culture. However, ADR methods like mediation and conciliation are more popular means of dispute resolution in collectivist societies and Eastern cultures. The working of a legal system in a particular state also impacts the reliance on ADR methods. Since legal systems are influenced by cultural dimensions and values, so are the modes of alternative dispute resolution like mediation and conciliation. Gold (2005) asserts that understanding cultural values, divides, and lags is essential for the effective implementation of alternative dispute resolution mechanisms since societies are becoming more inclusive and diverse with each passing day.

Sourdin (2018) is of the view that in terms of application of artificial intelligence to the adjudication process, there is an established consensus among academics and legal experts that currently AI is only capable of contributing marginally to the process of litigation and judicial decision-making. He contends that AI can be used to perform clerical tasks and conduct research; however, its potential as a court is significantly limited by several factors. Admittedly, while AI is rational, impartial, and can wield an extensive knowledge base, its inability to interpret laws, apply sentencing disparity, absence of thought process capable of being equal to jurisprudential reasoning by a judicious mind, algorithmic bias, ethical implications such as lack of accountability, and its reliance on syntax rather than semantics (Sourdin, 2018) conclusively deem that generative AI cannot play the role of a judge.

Bagshaw (2015) observes that technological development has led to the evolution of conflicts, and now more conflicts are occurring at the international level, i.e., global conflicts. Therefore, mediation and conciliation are becoming popular means of resolving these global conflicts since states cannot rely on traditional means of dispute resolution in all circumstances. Bagshaw (2015) further suggests that mediators should avoid the imposition of their own norms and values in circumstances where disputing parties belong to different cultural backgrounds. Furthermore, it is crucial to recognize and respect indigenous cultural values for effective resolution of disputes at local as well as international levels. Mediators should be trained in self-reflexivity to manage their personal biases and social, linguistic, political, and cultural influences. This practice enhances the professionalism and impartiality of mediation, particularly in resolving both national and global disputes.

Wenying (2005) has critically examined the role of conciliation in dispute resolution. There are different types of conciliation such as administrative, people’s, and organizational conciliation. These types differ across cultures and countries. For instance, administrative conciliation is done by administrative bodies for dispute resolution within the powers vested by law. People’s conciliation involves communities using a mediation committee. Whereas, institutional conciliation is conducted by specialized conciliation wings established by the administrative agencies which are regulated by the laws and regulations governing arbitrations.

Inman (2013) have assessed the implications of cultural influences on mediation, particularly in times of international crisis. It has been observed that cultural differences might escalate tension between parties and therefore, it is necessary to address and mitigate these differences so as to minimize tension. Mediation has the effect of reducing the duration of conflict and increases cooperation in furtherance of dispute resolution. Cross-cultural mediation is tricky to navigate and conduct, but the key is that parties understand the cultural differences and cooperate together for a workable outcome.

According to Zarefsky (2024) cultural understanding among postgraduate students participating in cross-cultural exchanges may be enhanced by AI tools which offer translation services and cultural perspectives. AI-powered language applications and virtual mediation platforms enable students to improve their communicative skills and function better in cross-cultural situations. Their findings highlight the changing role of AI technologies in facilitating interactions across divided societies. The more educational institutions begin to embrace these tools, the more important it may become to study their impact on students’ intercultural competences development in the context of time.

Research Methodology

This paper examines the impact of cultural divides on mediation and conciliation in domestic and international disputes. To effectively address this research question and critically examine the subject matter, both qualitative as well as comparative methods of research have been deployed. The qualitative method includes the reappraisal of literature available on the subject matter, including but not limited to scholarly articles, judicial precedents, statutory instruments, and other sources of law available. Furthermore, the comparative method involves comparative analysis of relevant literature and data concerning the subject matter. Therefore, the scholarly articles, relevant statutory instruments, and judicial precedents will be relied upon for a detailed analysis of the topic at hand.

Cultural values directly influence the approaches towards dispute resolution and more particularly concerning alternative dispute resolution (ADR) rather than traditional means of dispute resolution, i.e., litigation. There are five (05) fundamental cultural dimensions such as collectivism vs. individualism, low/high-context communication, polychronic/monochronic time orientation, low/high power distance, and low/high uncertainty avoidance. In addition to these dimensions, there is also a significant difference between Western and non-Western cultures in ADR. Western cultures promote individualism, low-context communication, monochronic orientation, and low power distance while non-Western cultures are directly opposite (Gold, 2005).

Therefore, it is essential to utilize cultural strategies to minimize the impact of cultural differences on negotiation or conciliation being conducted between the parties. Some of the crucial cultural strategies for the negotiators or conciliators involved in a dispute resolution are:

• Comprehending the nature and types of culture they are dealing with.
• Being cognizant of the difference in cultural dimensions and the attributes of each culture that can influence such negotiations or conciliation.
• Acknowledging the cultural communication methods such as verbal, non-verbal, ethics, norms, etiquettes, etc.
• Focusing on cooperative working engagement through recognizing and respecting the local customs (Gonzalez, 2021).

Different cultures perceive the importance of even punctuality differently. For instance, in the United States, it is considered imperative for a person to be punctual and be on time to meetings. It is considered disrespectful when a person is late. However, the same is not true for Latin America since timekeeping is viewed flexibly in that region (Gonzalez, 2021).

Theoretical Issues Regarding Mediation and Conciliation

There are certain theoretical issues regarding mediation and conciliation, for instance, the diversity of communication theories concerning mediation such as the social exchange theory, role theory, structured mediation, social learning theory, as well as mediation analysis. Whereas, other issues include the assessment of conflict and the application of mediation on such conflict where the primary query is whether this particular conflict/dispute is required to be “managed” or “resolved” or “minimized/mitigated”. Depending on the required outcomes, mediation sometimes operates on the basis of a cooperation-based, win-win, win-lose, or competitive-based framework. Third-party control is another factor which contributes to these theoretical issues as well (Nally, 1995).

There are various models of mediation and conciliation which are deployed after considering the contextual requirements of each dispute/conflict. Mediation and conciliation are problem-solving approaches towards dispute management which can be utilized in almost every situation where the conflict or dispute arises. However, it is crucial to conduct a pre-mediation / pre-conciliation inquiry as to which theoretical framework would work better in a particular dispute resolution after carefully considering the facts, contextual realities, and requirements of the parties to the dispute. Despite the theoretical issues concerning mediation and conciliation, they are still reliable alternative dispute resolution methods in contrast with traditional dispute resolution methods since mediation and conciliation don’t follow a top-down authority model but rather are based on strictly addressing the requirements of the parties to the dispute and implementing effective dispute resolution strategies (Ridley-Duff, 2010).

It is necessary to critically analyze a few case studies concerning cross-cultural interaction so that mediation and conciliation could be used as a tool for minimizing the cultural divide. This way, mediation and conciliation will work as a bridging gap in the dispute resolution whether it be domestic or international. Some of the case studies are given as follows:

The Swiss EGL company finalized a deal with the National Iranian Gas Export Company in March 2008. This was a time when Iran was facing trade sanctions by the United Nations. Nevertheless, Switzerland was still able to successfully conclude the deal without violation of any of the sanctions imposed. This was achieved by careful consideration of cross-cultural factors between Iran and Switzerland. From the Iranian side, the state-owned company was involved, but from the Switzerland side the company was a private one. Yet the government of Switzerland added some of their representatives in the panel for finalizing the deal. Furthermore, Switzerland has established good diplomatic relations with Iran and carefully navigated through their culture. This helped in the negotiation of a crucial trade deal. Therefore, if cultural differences are carefully understood and minimized, any conflict or dispute can be amicably resolved through mediation or conciliation (Bahgat, 2010).

There are two dominant communities in Turkey, namely the Turks and Kurds, which have been in a long-standing conflict over cultural rights and ownership of land. There are cultural differences between Turks and Kurds. The Kurdish culture has a strong sense of identity and desire for cultural autonomy. However, the Turkish culture has elements of nationalism, governmental authority, and historical supremacy. While mediating or conciliating a dispute between these two factions, a cross-cultural study-based approach is required by the mediator or conciliator (Zarefsky, 2024).

These case studies highlight that cross-cultural mediation and conciliation are complex to navigate and it is crucial to understand cultural differences for resolving the dispute amicably (Gonzalez, 2021).

AI's involvement with arbitration and negotiation has greatly advanced as there are tools that can assist mediators in comprehending complicated information, and the technology itself is able to automate tasks and analyze data. To give an instance, AI can be a mediator's helper by giving the mediator statistical information, recognizing trends in the talks, and helping the mediator remain neutral and consistent. On the contrary, the application of AI in cross-national mediation is largely absent. AI can assist in reducing cultural stereotypes in new ways by providing an equal perspective for the participants, making the organization of sittings more effective. Nonetheless, there are issues concerning the use of AI in mediation, especially concerning the need to balance human care with the capacity of AI for analysis (Gonzalez, 2021).

Maintaining neutrality is vital for the process of mediation. However, in cross-cultural mediation, bias from the culture of the mediator or the culture of the parties can permeate the mediation. AI applications can help mediators to remain neutral by identifying communication patterns and bias, or by providing neutral information about the cultures involved that might be useful for resolving the dispute. For instance, AI may provide instant analysis of language and cultural barriers as possible sources of miscommunication impairment (Shamir, 2016). This will enable AI to remove these barriers. The aim closely is to achieve a situation free from cross-cultural elements for the parties truly to meet for settlement, able to put their cultural differences behind.

Mediators using tools that employ artificial intelligence, like natural language processing, have the ability to assess tone and sentiment and gain insight into emotion-related factors that may be culturally influenced. Moreover, AI can attempt to mediate discussions in a balanced way by suggesting positions that are in keeping with the cultural inclinations of each party without necessarily siding with anyone. In a scenario where a mediator tends to unconsciously side with the cultural characteristics of one of the parties, AI modifies the recommendations it makes to the parties so that they are regarded as fair and in the context of the dispute that has been analyzed but without the aid of the mediator (Sourdin, 2018).

One of the major advantages of AI in ADR is its ability to streamline processes. Cross-cultural conflicts can be protracted because of the added intricacy; however, there are several implementations of AI that enable mediators to make the processes more efficient. AI can assist with the scheduling of sessions, distribution of documents, and translation, which will ensure that mediators can dedicate their time to the key issues at the heart of the conflict. AI systems can be utilized to research previously resolved similar cases and give the mediators insight into the possible outcomes as well as recommend settlements that have previously been successful. This is especially helpful in cross-border situations in which one is not familiar with the relevant norms and practices (Lodder, 2012).

Language barriers are one of the obstacles to effective cross-cultural mediation. The use of AI tools for translation can solve the language barrier by offering accurate context-based translations instantly. Furthermore, AI can provide contextual information and such knowledge facilitates the understanding of the cultural relevance of any conflict among the parties, thereby improving the communication between the parties. AI technologies can replicate a variety of negotiation situations through the manipulation of the factors, enabling the mediators to estimate how some decisions or strategies may be viewed by each side. This simulation, for instance, can be effective in multiparty and cross-cultural negotiations where the terrain is more intricate (Susskind, 2019).

Conversely, there is no doubt that AI has the ability to enhance cross-border mediation, but its application in ADR raises a number of ethical issues alongside the blurring of boundaries. The foremost issue is the opaqueness of AI systems, specifically when it comes down to how algorithms work to come to a particular ‘conclusion’ or endorse a certain course of action. This ‘black box’ predicament can negatively affect the faith of the parties to the mediation in the process when decisions are made regarding specific cultural concerns. In addition, AI systems are biased to the extent that their algorithms remain biased or that their training data is biased (Bagshaw, 2015). If the information employed for the construction of AI models reflects existing cultural bias, then there is a possibility that these biases may be reinforced. To mitigate this, technologists and legal practitioners need to guarantee that AI instruments are trained on comprehensive datasets comprising all ethnic groups and monitored regularly for fairness. Another challenge lies in the human element of mediation. Mediation often requires empathy, active listening, and the ability to understand the unspoken nuances of communication—qualities that AI, at least in its current form, cannot replicate. Over-reliance on AI could lead to a diminished role for human mediators, potentially eroding the relational aspects that make mediation a successful form of dispute resolution (Cortes, 2010).

A Comparative Analysis of ADR and Judicial Decision Making to Understand Application of AI

ADR methods are perceived as a legal innovation which offers litigants a course other than that provided by a costly, lengthy, and difficult formal system of adjudication (Mackie, 2013). The term itself was first used by Professor Frank Sander in the Pound Conference in 1976. Later on, while explaining this novel concept, he proceeded to list the following as the goals of ADR (Sander, 1985):

• to relieve court congestion, as well as undue cost and delay;
• to enhance community involvement in the dispute resolution process;
• to facilitate access to justice;
• to provide more "effective" dispute resolution.

As far as definitions are concerned, while ADR defies any strict definition, at best it can be defined as “a set of approaches and techniques aimed at resolving disputes in a non-confrontational way” (Shamir, 2016). ADR methods exist in a spectrum of dispute resolution mechanisms which range from inter-party negotiation to arbitration and adjudication at the other end. Arbitration, conciliation, facilitation, negotiation, and mediation are some prominently used ADR mechanisms.

However, while the potential of AI in the judicial decision-making process is limited, the same cannot be said for the prospective role of AI in the process of ADR. This is because there is a fundamental difference between the judicial decision-making process and the decision-making process in ADR. While both are devoid of any strict definitions, this paper can utilize existing descriptions of the two to portray their difference. Judicial decision making is a process which envisions a judge as the adjudicator of specific, concrete disputes, who disposes of the problems within the latter by elaborating and applying a legal regime to facts, which he finds on the basis of evidence and argument presented to him in an adversary process (Weiler, 1968).

On the other hand, ADR decision-making is defined to include the rational gathering, analyzing, and considering of information, and the making and communicating of a decision (Sourdin, 2018). By this standard, ADR decision making consists of stages including gathering of information, determination of rights and interests, and finally the determinative stage. Thus, while judicial decision making is a complex thought process involving appraisal of evidence, application of a judicious mind, interpretation and application of laws, ethical evaluations of laws, and considerations of the after-effects of judgments on future disputes, the decision-making process in ADR is relatively simpler, consisting of analysis of provided information, the strict application of law, and the rendering of a decision that will only affect the parties involved. The ethical challenges in ADR by AI can also be mitigated by the oversight of the judiciary which is responsible for the actual execution of the decision passed (Zarefsky, 2024).

Resultantly, AI can increasingly be utilized to aid decision-making in matters which have simple outcomes and options, a trait of disputes in ADR, as well as complex issues like environmental and medical concerns (Sourdin, 2018). Over the past five decades, AI has evolved to perform tasks requiring human intelligence, including legal analysis. Its adaptable nature allows integration with existing adjudicatory or non-adjudicatory processes. Sophisticated AI systems in the legal field use advanced branching technology to create detailed decision trees for resolving disputes (Hall, 2005). These systems emulate human intelligence by asking users questions about the dispute to gather accurate information. Then, they apply the law to this information to form conclusions based on specific rules for different situations. Finally, the computer can take actions based on the information provided. This process may help in giving preliminary decisions (Hall, 2005).

Alternative Dispute Resolution (ADR) methods have gained prominence in recent times due to their cost- and time-effective nature. Among them, mediation, conciliation, and arbitration are widely utilized owing to their benefits and ease of implementation. Therefore, these methods are now being prioritized as means of dispute resolution rather than relying on traditional methods of dispute resolution. However, these methods are still susceptible to certain flaws, and technology can play a constructive role to further enhance the effectiveness and efficiency of these methods of dispute resolution (Bagshaw, 2015).

Artificial Intelligence (AI) means a set of computer systems or programs that are competent to perform tasks which require human intelligence. Therefore, it would be appropriate to say that AI can simulate human intelligence and can perform similar functions such as reasoning, learning, analysis, problem-solving, language comprehension, and perception. AI is being currently deployed and used in various sectors such as robotics, health, education, etc. AI can also be used as an assisting tool for increasing the effectiveness of ADR methods. It can be utilized for automation of certain ADR processes which might include, but are not limited to, case management, document analysis, as well as decision-making. AI-based programs are capable of analyzing huge data clusters and can therefore analyze tons of legal documents and precedents to assist the mediators, conciliators, or other ADR practitioners in providing valuable insights regarding a particular dispute (Aderemi, 2023).

The integration of AI in the field of ADR has led to the evolution of predictive analysis. It means that AI algorithms are capable of analyzing past legal cases, national and international disputes to predict future dispute resolutions. This would help in comprehending the potential outcomes of a future dispute and would assist the mediators or conciliators or arbitrators involved in such dispute resolution process. Therefore, predictive analysis can drastically reduce the time and resources spent on dispute resolution methods by providing reliable predictions based on previous case data (Alessa, 2022).

AI-driven frameworks are being utilized for facilitation of negotiations. These mechanisms can execute legal documents, draft settlement agreements, and even recommend optimal ADR strategies which could be deployed depending upon the particular context of the dispute at hand. Through automating certain elements of the dispute resolution process, AI will assist mediators, negotiators, or arbitrators to focus more on the critical aspects of the dispute, thereby enhancing the dispute resolution process. AI can also enhance the case management system regarding ADR methods. AI can assist the ADR practitioners in highlighting the critical elements of the case/dispute at hand, identifying potential issues, and even recommending practical steps for resolution of such issues (Poole, 2024).

Furthermore, a new domain of Online Dispute Resolution (ODR) is being explored with reference to AI as it has the potential to contribute more in a virtual environment where mediation or arbitration is being carried out. The primary objective of incorporating AI in ADR is to improve the working capacity and outcomes of the ADR methods. For instance, the incorporation of AI can lead to enhanced access to justice as well as reduced cost of operation (Aderemi, 2023). In light of its rapidly developing abilities, AI can serve as a viable alternate for human arbitrators in simple disputes, provided that it is carefully monitored by human arbitrators and is subjected to close scrutiny by the judiciary under the existing legal framework, which is examined in the last part of this paper. The incorporation of AI in the field of ADR requires that the arbitrators, mediators, conciliators, and legal practitioners understand and engage with the AI technologies. Therefore, legal practitioners and ADR experts should acquaint themselves with the use of AI technology so that they could better navigate a rapidly changing digital world (Kulmuhametov, 2023).

The future of AI has great potential when it comes to its deployment in the field of ADR. AI technologies are becoming more and more refined and better with each passing day. Further developments with enhanced machine learning capabilities are likely to revolutionize the ADR sector as AI might be directly deployed as an alternative to a human mediator or conciliator. However, there are certain ethical considerations regarding the use of AI in ADR methods which need to be addressed. For instance, it has been reported that AI algorithms are biased and they work according to the data set that is fed to them. If the information provided to AI algorithms is flawed, then the entire processing would be faulty and it would lead to unfair dispute resolutions when deployed. Therefore, there is a dire need to introduce stringent regulatory frameworks regarding AI systems to ensure transparency in decisions made by AI and to build trust in the system of ADR (Wu, 2023).

As ADR refers to dispute resolution outside court, therefore it is important for any arbitrator that the predictability of the legal proceedings will be effectively communicated to the client: what are the chances of their success if their arbitration fails and what cost will be incurred in litigation. This is something for which the accuracy of the response of any arbitrator depends on his ability to analyze the trends vis-à-vis outcome of similarly placed cases in a similar jurisdiction (Alsamhan, 2023). AI can process large amounts of data of previous and present cases and their subsequent outcomes, which a human arbitrator lacks even otherwise; if a human arbitrator endeavors, then it will consume the time of the arbitrator or he has to be an experienced one which makes provisions of his services expensive and limits access to justice in consequence. Secondly, in arbitration a lot of data is needed to be processed that is comprised of bundles in some cases. For a human arbitrator, it is a work of days but for AI it could take a few minutes to seconds to appraise trends and patterns in raw figures to reach meaningful insights and weaknesses. This can not only save the time of business enterprises but also ensure that each and every document is perused and nothing is overlooked in the process (Zeleznikow, 2021).

Thirdly, AI can revolutionize the case management system by automatizing case filing, scheduling hearings, and record keeping in a manner that is traceable with few clicks at the comfort of their sofas. The language models such as the likes of Chat-GPT can provide easy access to the parties regarding the proceedings of their cases and any inquiries on their fingertips. This way accessibility of the information and case proceedings can be ensured. Fourthly, the impartiality of the arbitrator is the major concern of the parties. This can be mitigated by implying AI in the field. As the AI system is a non-human entity, it is free from any affiliation, inducement, and incentives which is a cause of concern for human arbitrators (Srivastava, 2021). Biasness of the AI system can be curtailed by feeding in diverse and large amounts of data in the system, and the regulatory frameworks can also ensure compliance with this fundamental principle of justice.

Lastly, evidence collection requires a lot of effort from both the arbitrator and parties as it requires their physical presence and appraisal of the same. However, this can be delegated in the hands of AI (Barnett, 2018), and also the progress in AI which is now empowered to analyze sentiments and tone of the input data which makes it more relevant in appreciating the demeanor of the witnesses and communication in the form of emails and other platforms (Taherdoost, 2023).

The word “arbitrator” has not been defined in the statute; nevertheless, it has been defined in case laws such as in the case of Mrs Yaseen versus Messers Beach Developers (2003) YLR 1109 where the court decided that a non-natural person cannot act as arbitrator. However, in certain cases the court has held, as in the case of Federation of Pakistan through D.G National Training Bureau versus James Construction Company (2018) PLD Islamabad 1, that the technicality of procedure should not impede the way of natural justice in arbitration proceedings. Placing these two dicta in juxtaposition and in the absence of any statutory definition, there is room for the implementation of AI in the field by a human arbitrator. However, the courts, despite the doctrine of least intervention as developed in the case of Karachi Dock Labour Board versus Quality Builders Limited (2016), retain substantial powers in their hands in the form of Sections 15 and 30 of the Act, through which it can remit, modify, or even set aside the award in the circumstances where there is any obvious error in the award or misconduct or non-reading of evidence or any legal infirmity.

The interdependence that exists between nations and people means disputes are likely to be common within both national and global settings. However, cultural boundaries could exist which may make it difficult for the conciliators or mediators in the dispute resolution process. In addition, using AI in ADR, especially in cross-cultural mediation, the issue becomes more complex. However, there are some suggestions and recommendations that can be made to assist mediators and conciliators in effectively bridging the cultural gaps and resolving disputes while utilizing AI technologies for the purpose of their practice:

Mediators and conciliators should receive formal training regarding cultural competence to understand the differences in various cultures involved in the conflict. These training regimens should involve the enhancement of competencies such as cross-cultural communication or cultural sensitivity.

When designing AI systems for mediation, try to include people with a wider geographical presence to ensure that the algorithms are culturally relevant. The training of the AI should utilize information from multiple cultural perspectives to enhance its capability to provide information without prejudice.

Recommend AI developers to integrate information regarding dispute resolution strategies, communication styles, and negotiation methods in different societies. With such integration, AI would be able to provide suggestions about different cultures during mediation processes. AI is simply an aid to the human mediator. The mediator is still in charge of making the most vital and managerial decisions and processes and simply employs AI advice as additional measures rather than orders.

Make misinformation negotiation models that allow the use of AI to help in the analysis and provide suggestions in the course of mediation while still allowing the negotiators to maintain their control over the emotional and relationship aspects of mediation. It makes certain that AI’s data-oriented decisions are paired with human ability to feel and reason. Any AI system that is used in the course of mediation ought to be clear and have a clear reasoning on the decisions made. The mediators and the parties in the dispute ought to know the reasons behind the recommendations or conclusions made through AI.

Create AI navigation tools that facilitate understanding that could display how decisions are made, what data sources they used, and what the basis for their recommendations was. Parties should also be provided with sufficient insight as to how AI models are built and what parameters are available in the model that the mediator uses. AI-based tools for language translation must not excel only in language translation of vocabulary but should incorporate the interpretation that relates to certain cultural phenomena and the social context in which they are used. This would eliminate the chances of miscommunication because of language barriers and enhance communication between culturally different people.

Other cultures need specialized idioms and even tone, which are subtle but important inputs to be referenced by the translation algorithms that need to be continually revised. Create principles of ethics and policy frameworks that regulate and govern the mediation process with the use of AI. Such policies should include issues of data protection, reduction of bias, transparency issues, as well as the place of AI in the decision-making process.

The attention of governments and legal institutions should turn to the development of standards for the use of AI in ADR, while ensuring equal treatment and banning discrimination. For example, the International Bar Association (IBA) may take measures aimed at the promotion of cross-jurisdictional ethics of AI in ADR. Resolving bias in AI systems is crucial in achieving mediation fairness, given the culturally pluralistic nature of cases. Regular systemic reviews of AI technologies are necessary in order to identify and eliminate bias in data and algorithms, and ensure diversity in social empowerment.

Embrace the practice of fairness auditing of AI systems and examine their impact on culturally diverse data sets. Form cross-sectional teams that will include mediators and lawyers, data scientists, and cultural anthropologists in order to assess whether recommendations by AI systems are fair and do not show undue cultural or racial tendencies. Mediators are often given specific training on how to manage AI tools while dealing with cultural disputes. This will assist them in comprehending the advantages and disadvantages of AI technology in mediations, particularly how to utilize AI insights and human emotions simultaneously.

Develop any AI-centric CPD or certification courses for mediators engaging in cross-cultural mediation. Moreover, such programs should be focused on ethical issues related to AI and its interpretations. Collaboration between legal institutions, mediation centers, and AI developers is necessary in running cross-cultural mediation AI-assisted pilot tests. This will illuminate cases when AI is beneficial and when it provides no value.

Support the responsible use of AI in cultural analysis and mediation scenarios by establishing research focused on its deployment efficacy. This includes, for instance, quantitative and narrative studies built around the effects of AI on mediation’s outcomes, neutrality, and efficiency. Reframe AI into a neutral facilitator of mediation services to avoid suspicions of bias or undue favors. This can enhance confidence on the parties especially in cases where neutrality is vital.

At the commencement of mediation, treat and demonstrate the AI as a neutral support to both sides while addressing its purpose and its application. The mediators, on the other hand, should explain that AI is not intended to determine the processes of the dispute but rather to increase the fairness and transparency of its resolution. Create AI systems capable of handling pan-cultural multi-party disputes. Such tools should assist in modifying the optimum end to suit the satisfaction of all the parties involved in the dispute and to tailor the suggestions for resolution.

Create AI systems that are capable of interpreting cultural characteristics in the context of multi-party negotiations and assist mediators with real-time analysis of how various cultural aspects may affect negotiation outcomes. AI can also recommend culturally acceptable solutions for all the participants.

As mediators and conciliators, it is important to remain flexible in terms of the mediation tactics that they use during a conflict and to also take into consideration factors such as cultural values, norms, and communication preferences of the disputing parties. Hence why some cultures require more flexibility during a conflict.

In the same sense, these stakeholders should foster such behavior in order to maintain credibility with the disputants while facilitators should be neutral and give respect to the cultural institution of all the parties involved. Principled negotiations build trust and prevent the stakeholders from biased behaviors, disregarding key aspects for peacebuilding efforts. The interactions must be free and diverse such that every person and their culture and beliefs are welcomed to engage in it. Active engagement and understanding are very important to respect other people's beliefs.

People recruited for mediation and conciliation could be from various cultures as such teams possess a better understanding of decentering the cross-culture related issues. These multicultural groups can help to speed up the conflict resolution process. The legal on the one hand and the cultural on the other hand approaches should be sought in terms of the behavior of mediator and conciliator. It is important to know how cultural factors structure in order to know how mediation and legal provisions can be integrated.

Allowance should be made for the use of technology in virtual given all four methods. This encourages involvement from people in underserved locations since virtual channels can reach beyond borders. Accessibility of such technologies entails that they should be human-centered and responsive to the cultural considerations of the users.

Let us start by saying there is a need for post-war reconciliation in order to understand the historical injustices, trauma, and societal fractures caused by grievances. Then, let us diachronically speak about the need for cross-cutting approaches and inclusiveness for social integration. The stakeholders should be empowered to mediate and reconcile continuously; their cultures and concepts are ever-evolving. Development of feedback and evaluation mechanisms should also be instituted to help mediators and conciliators alter their perception and approach towards the dispute change process with a view of minimizing the cultural gap.

The mediation and reconciliation as means of overcoming national and international disputes are of paramount significance and understanding clearly forging cultural interdependence. Video conferencing is designed to ease human interaction through promotion of cultural unity and understanding. Utilizing cultural inclusion, cultural competence, flexibility, and sensitivity, differences can be bridged and constructive engagement can be fostered among stakeholders of different cultures. Integrating AI in cross-cultural mediation presents great promise for increasing neutrality and efficiency. AI can help limit the amount of misunderstanding and cultural bias during mediation by providing mediators with objective evidence, an interpreter, and knowledge on social customs. In any case, dealing with the bias, the lack of transparency, and, more importantly, the undue dependence on technology are among the ethical issues AI brings. In order for AI to be efficiently integrated into cross-cultural mediation, it is vital to integrate AI technologies while respecting the necessity of human components in the practice. In line with what was mentioned above, the recommendations in this paper emphasize the importance of cultural considerations, neutrality, and flexibility in the affected parties’ mediation and conciliation. People from the community should be adapted so that they mingle seamlessly with the deployed strategies. Technology can also positively be applied in making the dispute resolution process more efficient and painless. The culture of Social Conciliation and Mediation is growing and encouraged as it facilitates the process of dispute resolution among people from different cultures. Finally, the disputing parties can now cooperate in solving, giving room to sustainable solutions by accepting and appreciating each other’s cultural expectations, boundaries, and identities.

Conflict of Interest

The author has no financial or non-financial conflict of interest in the subject matter or materials discussed in this manuscript.

Data Availability Statement

Data availability is not applicable as no new data was created.

Funding Details

No funding has been received for this study.

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