Ammar Zafar - University or Liverpool
Artificial Intelligence Influence in Family Law: Balancing Technological Advancements with Ethical Considerations
Abstract
This essay examines the role of Artificial Intelligence (AI) in family law, highlighting its efficiency-enhancing benefits and the challenges it poses, particularly ethical concerns and potential biases. It reviews AI’s applications in legal processes, such as rule-based and case-based reasoning, and tools. The article emphasizes the need for balancing technological advancement with human judgment and ethical oversight in legal practices, advocating for a responsible approach to AI integration in the legal sector.
Introduction
Artificial Intelligence has become integral to various sectors, including law, transcending its traditional presence in technologies like GPS and social media platforms. In the legal field, AI’s adoption has markedly enhanced procedural efficiency, case management, and accessibility. This sector now utilizes a comprehensive array of AI tools, facilitating tasks ranging from will drafting to sophisticated analyses in child custody cases. Such advancements are critical for the legal industry to remain relevant in an automation-driven environment, streamlining complex legal processes.
However, AI’s application in law is not without challenges. A significant concern is the potential for bias, as AI algorithms reflect the data, they are fed. Biases, particularly related to race and gender, may infiltrate AI decisions in sensitive areas like child custody and bail. These biases often originate during data processing and collection and can be exacerbated by human involvement in AI programming, where unintentional reinforcement of biases may occur. Thus, while AI presents opportunities for efficiency and innovation in the legal domain, it also demands careful consideration of the ethical implications of its use.
What does AI mean?
Understanding the concept of Artificial Intelligence is pivotal before delving into its legal implications. AI, despite its widespread impact, lacks a standardized definition. It is imperative to acknowledge both the current state and future potential of AI for humanity. AI, at its core, involves automating tasks traditionally reliant on human intellect. It encompasses a variety of computational methods designed to enhance machine capabilities in complex intelligence tasks, such as pattern recognition, computer vision, and language processing. The definition and perception of AI are dynamic, evolving with technological advancements—a phenomenon known as the "AI effect" or "odd paradox."
In the legal realm, AI has been increasingly implemented in roles traditionally held by humans, such as decision-making and deep learning applications, notably in AI-based legal software. Technologies like COMPAS, a predictive tool used by adjudicators, and e-discovery systems, comprehensive database platforms for legal information management, exemplify advancements in legal software. AI’s application in legal software employs programming and engineering approaches to enhance the efficiency of legal processes, including data examination, investigation, recognition, gathering, and preservation. These AI-based systems transform labour-intensive e-discovery programs into efficient processes, enabling legal professionals to focus on more complex tasks.
Legal AI systems often utilize rule-based architectures, where algorithms dictate decision-making processes based on predefined criteria. For example, these systems can automate preliminary alimony recommendations in family legal proceedings, streamlining settlement negotiations. To illustrate, "If a marriage lasted N years and one spouse earns significantly more than the other, then X amount of alimony is recommended." For instance, the codes might state: "If the marriage lasted over 20 years and one spouse’s income is twice the other, then suggest alimony equal to 40% of the higher earner’s income for half the duration of the marriage."
In contrast, the case-based reasoning (CBR) model in AI deviates from rule-based systems. CBR analyses previous cases stored in databases, using historical data to make predictions and decisions, thus facilitating more nuanced and contextually relevant legal judgments.
The transformative potential of advanced AI technologies in the legal sector is undeniable. These technologies, equipped with case laws, legislation, precedents, and legal argument analysis, can significantly enhance the accessibility and efficiency of justice. They are poised to provide accurate legal advice on issues like child custody and divorce and expedite processes such as filing electronic orders. While this may seem futuristic, current advancements in AI research suggest that these innovations could be realized within the next 15 years.
Traditional approach
The emergence of AI in legal practices represents a pivotal transition from the traditional, labour-intensive approach of lawyering. Historically, legal work entailed extensive manual research, drafting, and physical document exchange. This paradigm shifted in the late 2000s with technological advancements that introduced efficient intake processes, remote document accessibility, and digital filing systems. These innovations have seamlessly integrated into legal practices, enhancing rather than replacing the human-centric aspect of the profession. Technologies such as remote video conferencing, particularly crucial in sensitive cases like domestic violence, facilitate Alternative Dispute Resolution (ADR) participation while maintaining safety.
AI’s efficiency gains notwithstanding, the human element remains crucial, especially in family law, where individuals often engage with little legal support. Digital legal resources provide accessible guidance in complex legal matters, but the intricacy of online legal information can be overwhelming for laypersons. The comprehension of detailed court forms and case laws remains a challenge, as seen in various states in the U.S. and the EU, where access to domestic dispute resolution without legal representation is constrained and Online Dispute Resolution (ODR) options are limited.
This context underscores scenarios where traditional face-to-face dispute resolution is preferable to ODR. Conventional methods emphasize relationship-centred counselling, fostering a broad relational perspective among parties, enhancing interpersonal skills, and fostering mutual understanding. Research suggests that voluntary settlements in family disputes reduce emotional and financial burdens, yielding agreements that align with the unique needs and values of the families involved and potentially decreasing future conflicts.
Traditional family mediation frequently leads to greater client satisfaction with the legal process. It restores control to the parties, enabling equitable negotiations devoid of facilitator biases. Agreements reached through human-mediated dispute resolution are often more detailed and specific, resulting in better compliance due to their tailored nature. These methods also encourage constructive communication through emotional challenges, offering insights into each party’s motivations, an aspect often lacking in ODR. For example, in traditional mediation for post-divorce asset distribution, emotional dynamics present both challenges and opportunities for meaningful breakthroughs. A competent mediator navigates these emotionally charged situations, fostering empathy, and enhancing the potential for a favourable outcome.
AI Lawyers: Future in the Making
The integration of AI into legal practices signifies a pivotal evolution, comprising two distinct phases. The initial phase, a moderate-innovative stage, witnesses the enhancement of traditional legal practices with technological tools, aiding courts and solicitors in more effective case management. The second, more advanced phase involves the incorporation of machine learning, natural language processing, and automated document analysis, streamlining the handling of extensive databases, pattern recognition, and language interpretation to aid in decision-making and predictions.
A key application of AI in legal proceedings, especially in the pre-trial and sentencing stages, involves the use of sophisticated algorithms. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) program exemplifies this, analysing defendants’ data against historical records to make reasoned decisions. COMPAS primarily assesses the likelihood of future criminal behaviour, showcasing a transformative approach in judicial processes. The potential of COMPAS extends beyond its current criminal justice applications, with possibilities in family law, particularly in divorce and child custody cases. By analysing comprehensive data such as age, employment history, behavioural patterns, and past relationships, COMPAS could significantly aid in custody decisions, providing an analytical approach to evaluating potential conflict risks and ensuring child safety.
However, the deployment of COMPAS raises significant ethical concerns. Investigations by Pro Publica revealed a racial bias in the COMPAS algorithm, disproportionately affecting African American defendants by incorrectly categorizing them as high-risk at a higher rate than their white counterparts. This discrepancy highlights a systemic bias, leading to harsher judicial outcomes for African Americans. The opacity surrounding COMPAS’s operational methodology, particularly the lack of public information about its data collection and decision making algorithms, raises critical questions about accountability and ethical AI use in the legal system.
In addition to COMPAS, other software like Wevorce, SmartSettle, coParenter, DivorceBot, Online Family Wizard, Lex Machina, and Modria show promise in family law. Modria, with a history in online dispute resolution, employs data analysis to identify common ground and propose solutions through deductive reasoning. Split-up, another advanced AI tool by J. Zelenznikow and A. Stranieri, focuses on post-divorce marital asset assessment, combining rule-based reasoning and neural networks. It offers explanatory outcomes and employs the Toulmin theory of argumentation, allowing parties to object and revisit settlement phases, thereby ensuring fair and comprehensive decision-making.
Emerging technologies are enhancing the capabilities of family law practitioners, providing greater flexibility in handling cases, including those involving domestic violence or clients with attendance constraints. These advancements in wealth distribution and dispute resolution mark a new era in family law administration, fostering empathy, trust, and rapport between attorneys and clients in virtual environments, thereby enhancing the efficacy and client-centred approach of legal service.
Balancing: Advancement and Concerns
The conversation over the application of AI to the legal domain includes viewpoints from proponents and opponents of AI. Advocates emphasise the substantial advantages artificial intelligence (AI) offers to customers and attorneys. These benefits include increased productivity, AI’s exceptional ability to handle large amounts of data, and easier access to legal services.
On the other hand, despite these significant benefits, there are important worries that AI might potentially come with significant drawbacks. These include possible biases in the methods used to make judgements, a lack of openness in the process used to make decisions, and problems with accountability. Such risks jeopardise the integrity and fairness of legal results by raising the possibility of injustices. This fair analysis of artificial intelligence in the legal field emphasises the necessity of managing both the technology’s possible advantages and disadvantages with caution.
The Reliability of AI: Dependent on the Quality and Bias of Its Data
In academic circles of law, the effectiveness of AI is inextricably tied to the quality of the data upon which it is built. AI’s dependability is dependent on the data utilised in its construction, much as the strength of a chain is based on its weakest link. Inadequate or subpar training data, programming faults, and defects in the creation of algorithms can all contribute to inconsistencies in AI-generated outputs, thereby compromising their effectiveness for client specific goals. A more concerning element of these flaws is their tendency to create feedback loops inside the algorithm, maintaining existing biases and perhaps resulting in accidental discriminating effects. This scenario has the potential to have serious consequences for both clients and legal practitioners.
The phenomenon of algorithmic bias emerges when prejudiced outcomes are produced due to flawed programming and biased or incomplete data. This issue poses a novel challenge to judicial fairness, necessitating vigilance from both legal practitioners and scholars. In machine learning software, biases can amplify as they become more ingrained in the system’s predictive patterns. These patterns, utilized by AI to identify relevant case features and match them with outcomes in similar cases, offer multiple avenues for bias infiltration. The primary pathways include biased or incomplete datasets used for training algorithms and the intrinsic design of the algorithm itself. For instance, historical data populating these systems’ knowledge bases can be fragmentary, yielding datasets that inadequately represent the nuances of individual disputes. This inadequacy stems from the fact that a substantial portion of litigation is resolved outside of court, abandoned, or dismissed. Relying solely on data from final judgments to the exclusion of mediated settlements and non-litigated agreements skews predictions away from accurately representing the most probable or ’typical’ outcomes.
Furthermore, case-based systems that use historical data to forecast outcomes may unintentionally perpetuate biases inherent in that data. For example, in AI-mediated custody fights, a male client may be at a disadvantage since previous data favours moms. In an era of rapid social transition, dependence on historical data raises worries about these systems’ ability to adapt to shifting cultural norms and judicial viewpoints. While the effect of prejudice on court precedent may have waned over time, depending only on historical data for contemporary case judgements might result in biased outcomes if the data reflects patterns of prior discrimination rather than the merits of the present case.
Unravelling the Riddle of Accountability
When it comes to judicial review and legal responsibility, the incapacity of AI programmes to explain the reasoning behind their judgements raises serious issues in the field of academic research. This opacity, often known as the "black box" problem, refers to the fully independent data processing methods of artificial intelligence that are yet unknown, even to the system creators. This lack of openness foreshadows several possible outcomes.
Future case results may unintentionally be impacted by legal professionals who base their case tactics on AI-generated forecasts. This impact is brought about by the feedback loop that is formed when decisions are made based on AI predictions and actions produce new data that is then reintegrated into the system to shape case outcomes and future forecasts. Because this process is self-reinforcing, biases or flaws in the original algorithm may remain unchecked and get stronger over time.
The fact that AI algorithms, not human judges, determine the variables impacting case decisions is at the heart of this problem. Based on patterns they find; these algorithms make predictions that may contain discriminating or harmful aspects that are overlooked and left incorrect. Once these prejudices are ingrained in the algorithm, they will probably be used consistently, strengthening any discrimination. For example, future projections will consider an AI program’s discovery of a link between the genders of the parties involved and custody judgements made by the court. The issue is made worse, and the program’s biased correlation is strengthened if legal professionals act based on these forecasts.
The intrinsic opacity of AI decision-making in legal settings presents important questions about the need for more responsible and transparent AI systems in judicial procedures as well as the persistence of biases.
Limitations of AI-Generated Agreements in Addressing Individual Conflicts and Interests:
In the legal discourse on the integration of AI in dispute resolution, particularly in family law, it is evident that AI-generated agreements may overlook the unique conflicts and interests intrinsic to individual parties. Certain elements of dispute resolution are fundamentally human and resist replication by even the most sophisticated AI systems. For instance, personal values, pivotal in family law disputes, often elude digital quantification and codification.
Child custody decisions, for example, are predicated on a multitude of best-interest factors, which vary by state. These include the children’s needs, the parents’ mental and financial capacities, and the family’s domestic history. These factors can become even more complex when issues like domestic violence, psychological problems, and co-parenting challenges are involved. While AI proves beneficial in resolving more straightforward matters such as the equitable division of property and assets, it struggles with issues like child custody due to their highly individualized nature. This limitation stems partly from AI’s inability to fully represent the diverse nuances of each unique dispute.
Alternative dispute resolution’s appeal lies in its capacity to consider individual interests and needs, unconstrained by strict legal precedents or public policy considerations. Facilitators in these scenarios have significant discretion to apply precedent and law in a manner that addresses the parties’ unique needs and interests while maintaining subjective fairness. Currently, constructing an AI tool capable of adequately assessing the fairness of a proposed judgment is unfeasible, aside from its ability to compare judgments to identify outliers or discriminatory patterns.
Research suggests that the combination of AI systems and human expertise surpasses the efficacy of AI used in isolation. A balanced approach involves a practitioner review of all AI-based recommendations or decisions. Ideally, legal practitioners should employ AI programs as a supplement to their judgment, following an independent evaluation of the parties’ conflicts and interests. This approach, referred to as a "human in the loop" (HITL) system, aims to mitigate biases in the data, ensuring compliance with public policy and facilitating a form of quasi-judicial review. HITL systems are prevalent in the medical field, assisting in classifying skin lesions for cancer risk. Here, an automated system preliminarily identifies areas of concern, which are then verified by a human provider. For AI-based legal programs, HITL involvement is recommended both in the design process and in periodic back-end audits to identify and rectify biased data. The designated HITL individual should comprehend the AI program’s decision-making process and underlying factors and possess the authority to assign legal or fiscal liability in cases of transparency violations. Without practitioner oversight, AI in judicial decision-making risks becoming an opaque process, complicating, or even precluding effective judicial review.
Outlook
In the context of legal academia, the integration of Artificial Intelligence (AI) programs in the judicial system, particularly in aiding judges and facilitators, offers a wider array of remedies and a more robust foundation for assisting parties. However, this advancement introduces new risks and concerns, especially for those not directly involved in technological development. While incorporating new technology into legal practices is crucial, the potential for AI to perpetuate systemic discrimination and challenge the ethical boundaries governing human practitioners is a primary concern. It is essential for judges, facilitators, and parties involved in family law to critically assess whether AI and data analytics tools reduce bias, enhance the likelihood of equitable outcomes, or inadvertently introduce biases through reliance on outdated data and established legal precedents. Despite AI’s remarkable contributions to legal service accessibility, it is not universally suitable for all types of disputes. And when comes to AI-driven online dispute resolution, it may overlook the unique, human centric elements crucial to the success of Alternative Dispute Resolution (ADR) in certain scenarios. AI systems tend to prioritize efficiency by utilizing historical data and information to identify statistically optimal solutions. This approach can neglect the personal conflicts and specific interests of the involved parties, potentially undermining the core objective of ADR — to achieve resolutions tailored to the distinct needs of all parties. Furthermore, while these systems have the potential to counteract judicial bias, they can also introduce biases that Human-in-the-Loop (HITL) processes usually avoid. Therefore, vigilant oversight is necessary to ensure that the application of AI in legal contexts does more good than harm.
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Bibliography
• Anthony Davis, The Future of Law Firms (and Lawyers) in the Age of Artificial Intelligence
• Bhavik N. Patel et al., Human–machine Partnership with Artificial Intelligence for Chest Radiograph Diagnosis, 2:111 NPJ DIGIT. MED. 1 (2019),
• Gheorghe Tecuci, “Artificial Intelligence,” Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 2 (2012): 168–80.
• Manheim, Karl M. and Kaplan, Lyric, Artificial Intelligence: Risks to Privacy and Democracy (October 25, 2018). 21 Yale Journal of Law and Technology
• Nello Cristianini, “On the Current Paradigm in Artificial Intelligence,” AI Communications 27, no. 1 (January 1, 2014): 37–43,
• Vaccaro, Michelle Anna. 2019. Algorithms in Human Decision-Making: A Case Study with the COMPAS Risk Assessment Software.
• Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, 2nd ed. (Natick, MA: A. K. Peters, Ltd., 2004).
• Patrick Huston & Lourdes Fuentes-Slater, The Legal Risks of Bias in Artificial Intelligence, LAW360 (2020),
• Julia Dressel and Hany Farid, “The Accuracy, Fairness, and Limits of Predicting Recidivism,” Science Advances 4, no. 1 (January 1, 2018)
• McPeak, Agnieszka, Disruptive Technology, and the Ethical Lawyer (April 19, 2019). University of Toledo Law Review, Vol. 50, 2019, Available at SSRN:
• Randolph Kahn, Law’s Great Leap Forward: How Law Found a Way to Keep Pace with Disruptive Technological Change, BUS. LAW TODAY (Nov. 20, 2016),
• Susan L. Brooks, Online Dispute Resolution and Divorce: A Commentary, 21 DISP. RESOL. MAG., Jan. 11, 2015, at 18; Kristen M. Blankley, Online Resources and Family Cases: Access to Justice in Implementation of a Plan, 88 FORDHAM L. REV. 2121, 2141 (2020)
• Linda S. Smith & Eric Frazer, Child Custody Innovations for Family Lawyers: The Future is Now, 51 FAM. L.Q. 193, 197 (2017)
• Peter K. Yu, Artificial Intelligence, the Law-Machine Interface, and Fair Use Automation, 72 ALA. L. REV. 187 (2020).
• John Zeleznikow & Andrew Stranieri, Split Up: An Intelligent Decision Support System Which Provides Advice Upon Property Division Following Divorce, 6 INT’L J. L
• National Science and Technology Council: Committee on Technology, Preparing for the Future of Artificial Intelligence. Washington.
• Rejmaniak, Rafał. "Bias in Artificial Intelligence Systems" Białostockie Studia Prawnicze, vol.26, no.3, 2021, pp.25-42.
• Leavy, Susan & O’Sullivan, Barry & Siapera, Eugenia,” Data, Power and Bias in Artificial Intelligence”, (2020).
• S. Wachter et al., “Transparent, explainable, and accountable AI for robotics,” Science Robotics 2, no. 6 (May 31, 2017),
• Wensdai Brooks, Artificial Bias: The Ethical Concerns of AI-Driven Dispute Resolution in Family Matters, 2022 J. Disp. Resol. (2022)
• Završnik, A. (2021). Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of Criminology, 18(5), 623-642.
