6.4 Challenges in Algorithmic Fairness, Transparency, and Explainability

Myths of Intelligence: Deep Challenges in Algorithmic Fairness, Transparency, and Explainability

Algorithmic Fairness, Transparency, and Explainability/Interpretability (XAI) stand as three pillars supporting the foundation of the increasingly crucial edifice of Responsible AI. They are not only necessary prerequisites for AI technology to gain public trust, achieve widespread societal acceptance, and integrate into critical domains (like healthcare, finance, justice) but also core elements for meeting the increasingly strict and specific legal, regulatory, and compliance requirements emerging globally.

Particularly in law—a specialized professional field with zero tolerance for inaccuracy, an ultimate pursuit of justice, extraordinary emphasis on procedural fairness, and a requirement that all significant decisions be well-reasoned, understandable, and reviewable—the importance of these three dimensions, fairness, transparency, and explainability, is magnified to an unprecedented degree. They directly relate to whether AI systems can be trustworthily applied by us (lawyers, judges, prosecutors, legal staff, or ordinary citizens) in scenarios that could assist, or even directly influence, the formation of legal judgments, allocation of litigation resources, assessment of commercial risks, or definition of individual rights and obligations.

However, despite being highly desirable goals widely advocated by the tech community, legal profession, and society at large, putting these lofty ideals into actual practice, especially when facing modern machine learning models (particularly large language models, LLMs) with extremely complex internal mechanisms evolved through deep learning on massive datasets, reveals profound, multi-layered, and potentially fundamental challenges in achieving truly meaningful and fully satisfactory fairness, complete system transparency, and deep decision explainability.

These challenges are not merely limited to current technical capabilities (e.g., we cannot fully “see inside” a neural network with trillions of parameters). They delve deeper into the ambiguity and plurality of core concept definitions (e.g., what is “fairness”?), the inherent conflicts and trade-offs between different value objectives (e.g., pursuing one type of fairness might sacrifice accuracy), and even some fundamental philosophical puzzles (e.g., can machines possess “understanding” akin to humans?).

For every legal professional, deeply understanding the nature and complexity of these challenges and their potential impact on legal practice is a crucial prerequisite for prudently evaluating the true capabilities and potential risks of various AI tools, rationally participating in discussions on AI governance and legal rulemaking, and effectively utilizing technology while steadfastly upholding fundamental legal principles and professional ethics in daily work. We must absolutely avoid falling into blind optimism or unrealistic fantasies about AI capabilities, overlooking the deep myths and severe challenges potentially hidden beneath the halo of “intelligence.”

I. Algorithmic Fairness: Pursuing Elusive “Fairness” in a Maze of Plural Values

The core goal of algorithmic fairness sounds simple and intuitive: to ensure that when AI systems make decisions, predictions, assessments, or recommendations (e.g., assessing credit risk, screening job applications, predicting recidivism likelihood, or even providing sentencing references), their outcomes do not result in systemic, unfair discrimination, favoritism, or adverse impact against individuals based on certain legally protected or socially recognized characteristics that should not be grounds for differential treatment (e.g., in many legal and ethical contexts, this includes gender, ethnicity, race, religion, age, disability status, origin, and possibly socioeconomic status, sexual orientation; often termed “protected characteristics” or “sensitive attributes”). In essence, AI should treat individuals involved in human affairs “equally,” “impartially,” and “without bias.”

However, the seemingly self-evident concept of “fairness” immediately reveals its inherent complexity, multi-dimensionality, and potential conflicts between different interpretations once we attempt to translate it from abstract ethical principles into concrete, implementable technical standards that can be quantitatively measured within algorithms. In the real world, “fairness” itself is a contested concept, context-dependent, and influenced by diverse cultural, philosophical, and legal traditions. Mathematizing and embedding it into algorithm design and evaluation proves exceedingly difficult.

• The Dilemma of Defining “Fairness”: Coexistence and Intrinsic Conflict of Multiple Standards:

  • Multiple Faces of Fairness: Decades of research by computer scientists, statisticians, social scientists, and ethicists have proposed dozens, even hundreds, of different mathematical definitions and metrics attempting to capture “algorithmic fairness” from various angles. These numerous definitions reflect society’s own pluralistic understanding and emphasis on different facets of fairness. They generally fall into several major categories:

    • Group Fairness: Currently the most common and easily quantifiable type. Focuses on whether the AI system’s decision outcomes achieve some form of statistical equality, balance, or rate consistency between different social groups defined by protected characteristics (e.g., male vs. female group, majority vs. minority ethnic group). Common group fairness metrics include:
      • Demographic Parity / Statistical Parity: Requires the proportion of individuals receiving a certain outcome (e.g., loan approval, interview recommendation, high-risk flag) to be approximately equal across all protected groups. E.g., the loan approval rate should be roughly the same for male and female applicants. While simple and measurable, its core issue is that it completely ignores potential real differences in base rates relevant to the outcome between groups. To achieve “formal equality” in group proportions, it might sacrifice individual-level accuracy (e.g., potentially denying qualified applicants from an “over-approved” group while approving less qualified ones from an “under-approved” group), which can be deemed unfair or inefficient in many contexts.
      • Equal Opportunity: Attempts to address Demographic Parity’s flaw by focusing on individuals who should rightfully receive the positive outcome (e.g., those who can actually repay the loan, qualified job applicants). It requires the probability of being correctly predicted as positive (True Positive Rate, TPR, also Recall or Sensitivity) to be approximately equal across all protected groups. In other words, all “qualified” individuals should have the same chance of being correctly identified by the model, regardless of their group membership.
      • Equalized Odds: A stricter standard than Equal Opportunity. It requires both Equal Opportunity (equal TPR across groups) and that the probability of being incorrectly predicted as positive (False Positive Rate, FPR) among individuals who should not receive the positive outcome (e.g., those unable to repay, unqualified applicants) also be approximately equal across all protected groups. This means that regardless of whether you are “qualified” or not, your chances of being “misjudged” by the model (either missed or wrongly included) should be equal across groups.
    • Individual Fairness: Shifts focus from group statistics to the individual level. Its core philosophy echoes Aristotle’s “Treating similar individuals similarly.” It demands that for any two individuals who are very similar with respect to all task-relevant features (importantly, excluding the protected sensitive attribute itself), the model should produce highly similar predictions or decisions, regardless of which social group they happen to belong to. While aligning well with intuitive notions of fairness (no differential treatment based on irrelevant factors), it faces significant practical challenges:
      • How to objectively and fairly define and measure the “similarity” between individuals based on “task-relevant features”? This requires a reliable metric, whose design itself could be biased.
      • How to accurately determine which features are truly “task-relevant” and which are irrelevant or should be excluded?
      • How to ensure the features used to measure similarity are themselves free from historical bias? Achieving individual fairness usually requires deep domain understanding and potentially more complex algorithm designs.

  • Core Dilemma: Incompatibility & Trade-offs among Fairness Metrics: A crucial conclusion, repeatedly proven theoretically and practically, is that unless under extremely rare, often unrealistic ideal conditions (e.g., base rates for all relevant metrics are identical across all groups), these different, plausible mathematical definitions of fairness are often mutually exclusive and cannot be fully satisfied simultaneously! For example, a model strictly satisfying Demographic Parity will almost certainly violate Equal Opportunity or Equalized Odds (unless base rates happen to be equal); a model satisfying Equalized Odds might fail to meet Demographic Parity. This means that in any specific AI application context where we aim for algorithmic fairness, we are almost always forced to make choices and trade-offs between different fairness dimensions. We must decide: in this particular scenario, which type(s) of fairness do we deem most important? To what extent are we willing to sacrifice other fairness dimensions, or even overall model prediction accuracy, to enhance this chosen fairness? This choice itself is by no means a purely technical decision that can be made independently by algorithms or engineers. It is fundamentally a profound value judgment problem, requiring difficult, responsible balancing and decision-making based on specific legal requirements (e.g., definitions of different types of discrimination in anti-discrimination laws), core ethical principles (e.g., prioritizing equal opportunity vs. equal outcome?), prevailing societal values, and careful assessment of the real-world social consequences of different choices.

• Subtlety of Bias Sources & Complexity of Detection: (Sources discussed in Section 6.1, complexity emphasized here)

  • The origins of algorithmic bias are extremely diverse and often intertwined, potentially lurking in any stage of the AI lifecycle:
    • Data Level: Societal biases inherited from historical data, selection bias from data collection methods, measurement bias in how data was recorded, annotation bias from human labelers’ subjectivity, data imbalance or missing data issues, etc.
    • Algorithm/Model Level: Algorithm design choices, model optimization objectives (e.g., accuracy vs. fairness trade-off), feature engineering decisions (e.g., including proxy variables), model architecture selection, etc.
    • Deployment & Interaction Level: Context shifts between training and deployment, user interaction feedback (if used for retraining), human interpretation and use of model outputs, etc.
  • Challenges in Bias Detection:
    • Availability & Legality of Sensitive Attributes: Directly assessing fairness across groups requires access to individuals’ sensitive attribute information. However, laws in many regions (including China’s PIPL, EU’s GDPR) strictly restrict or prohibit collecting and processing such sensitive personal data without clear legal basis or explicit consent, posing legal and practical barriers to direct fairness measurement.
    • Choosing Appropriate Fairness Metrics: As discussed, multiple fairness definitions exist. Selecting the most relevant and meaningful metric(s) based on the specific context’s legal and ethical requirements demands expert judgment.
    • Identifying Indirect Discrimination & Proxy Variables: Complex “black box” models might not use protected attributes (like race) directly but could achieve discriminatory outcomes by learning correlations with seemingly neutral but highly correlated features (“Proxy Variables”) (e.g., zip code, school attended, certain consumption habits). Detecting and quantifying this more subtle, harder-to-perceive form of Indirect Discrimination / Disparate Impact / Proxy Discrimination is particularly challenging.

• Technical Challenges in Bias Mitigation & The “Whack-a-Mole” Problem:

  • Academia and industry have developed various techniques aiming to Mitigate (note: usually mitigate, not eliminate) algorithmic bias, broadly categorized into three types:
    • Pre-processing Techniques: Intervene on the original training data before model training. Methods include resampling (Oversampling minorities / Undersampling majorities) to balance group representation, data augmentation to generate synthetic data for underrepresented groups, or more complex techniques to modify data representations (e.g., learning embeddings that “disentangle” or remove sensitive attribute information). Challenges: Modifying original data might introduce new unknown biases, or damage data integrity and information content, potentially harming model generalization and overall accuracy across all groups.
    • In-processing Techniques: Intervene during the model training process, incorporating fairness considerations into the learning objective. Methods include adding fairness-related regularization terms or constraints to the model’s loss function, forcing it to balance accuracy with fairness metrics; or designing algorithm architectures with inherent fairness guarantees (e.g., Adversarial Debiasing networks). Challenges: These methods often require explicit trade-offs between overall model Accuracy and the specific Fairness metric being pursued (the “Fairness-Accuracy Trade-off”). Improving fairness for one group or aspect might necessitate sacrificing some overall performance, and the impact of this trade-off might also be uneven across groups. Setting the right balance is difficult.
    • Post-processing Techniques: Adjust the model’s output predictions or decision thresholds after the model is trained, to make the final decisions statistically satisfy a predefined group fairness metric. E.g., setting different admission cutoffs, credit score thresholds, or risk standards for different protected groups to achieve balanced outcome rates. Challenges: While simpler to implement, this approach doesn’t fix the underlying bias within the model itself and merely provides a “superficial fix” (criticized as “Whitewashing” or “Fairness Gerrymandering”). It might directly violate the individual fairness principle (“treating similar individuals similarly,” as individuals with the same score might receive different outcomes based on group), and could raise new legal or ethical controversies (e.g., does it constitute reverse discrimination?).
  • No “Silver Bullet,” Need for Holistic Governance: It must be recognized that currently no single bias mitigation technique is a “panacea” or “silver bullet” that perfectly solves all bias problems in all scenarios. Choosing which method(s) to use (often a combination is needed) requires very careful, case-by-case assessment and selection based on the specific application context, data characteristics, acceptable accuracy-fairness trade-offs, relevant legal requirements, and the core fairness goals to be achieved. Technical solutions often need to be combined with non-technical measures (improving data collection, strengthening human review, establishing grievance mechanisms, enhancing organizational culture) for holistic governance.

• Special Requirements & High Sensitivity of Fairness in Legal Scenarios:

  • Distinguishing Legitimate vs. Illegitimate Differential Treatment: Law itself does not always prohibit all forms of differential treatment. It often explicitly requires or permits differential treatment based on legitimate, justifiable factors (e.g., special protection for minors based on age; differential regulation for companies based on risk level; considering legitimate risk factors in insurance pricing). AI systems need the capability to distinguish between “legally required/permitted differentiation” and “unlawful discrimination,” accurately applying the former while strictly avoiding the latter. This requires deeper integration of legal rules into model design or application logic.
  • Differences Between Legal Standards of Discrimination & Statistical Fairness Metrics: Note that the legal definition and proof process for “discrimination” (especially indirect discrimination or disparate impact) are not necessarily identical to, and may even conflict with, various statistically defined “fairness metrics.” An AI system satisfying one or more statistical fairness metrics might still be found legally discriminatory in court. Conversely, a system not perfectly meeting a statistical metric might not necessarily constitute illegal discrimination (e.g., if the disparity is proven to result from business necessity). Therefore, simply satisfying a statistical fairness metric cannot be equated with “compliance with anti-discrimination law.” Legal compliance assessment requires deeper legal analysis.
  • Extreme Sensitivity to Fairness in Judicial Contexts: In scenarios directly impacting fundamental individual rights, liberty, or significant property interests (e.g., AI assisting bail decisions, sentencing recommendations, evidence assessment, witness credibility evaluation), public and legal system expectations for fairness are highest and most sensitive. In these domains, any perceptible, systemic algorithmic bias, even if statistically minor, could severely undermine public trust in the judicial process and outcomes, with far greater negative repercussions than in commercial applications. Therefore, applying AI in these high-risk judicial contexts demands the absolute strictest standards for fairness assurance.

Fairness: A Complex Socio-technical Problem Requiring Ongoing Dialogue & Reflection

Achieving truly meaningful algorithmic fairness is far more than just a technical problem solvable by better algorithms or cleaner data. It is fundamentally a deeply intertwined, extremely complex socio-technical challenge involving technical possibilities, legal regulatory boundaries, conflicting ethical principles, and societal value judgments. It has no easy answers or universal solutions. It demands continuous, cross-disciplinary, open, and honest dialogue and collaboration among computer scientists, data scientists, legal scholars, ethicists, sociologists, economists, policymakers, and representatives from communities directly affected by algorithmic decisions. We need, in each specific application context, to deeply analyze the meaning of different fairness definitions, their potential positive and negative societal consequences, and the complex trade-offs they entail with other important values (individual rights, accuracy, efficiency, privacy). And ultimately, based on thorough deliberation and democratic processes, make responsible choices aligned with core societal values and the spirit of the rule of law.

In this challenging process, legal professionals, with their deep understanding of fundamental legal principles (equal protection, non-discrimination), procedural justice values, and rights remedy mechanisms, play an indispensable key role. We need to proactively understand the capabilities and risks of emerging technologies, but more importantly, firmly apply legal frameworks and thinking to scrutinize, regulate, and guide the direction of technology application, ensuring it serves, rather than deviates from, the core goals of the rule of law.

II. Transparency: Seeking Necessary “Visibility” Amidst the Heavy Fog of the “Black Box”

Transparency, in the context of AI (especially complex machine learning models), generally refers to the extent to which an AI system’s internal working mechanisms, information about the training data used, the process leading to specific decisions, and information regarding its performance, risks, and limitations can be Seen (Visible), Understood (Understandable), and Accessed (Accessible) by relevant stakeholders (including developers, users, regulators, affected individuals, and the public).

Transparency is widely considered fundamental for building public trust in AI technology, a prerequisite for enabling effective accountability, key for comprehensive risk management and identifying potential issues, and a necessary condition for implementing robust, effective legal regulation and governance frameworks. Without a certain degree of transparency, AI systems remain mysterious “black boxes,” making it difficult for us to fully trust their outputs or hold them accountable and improve them when they err.

However, in practice, especially when dealing with modern AI models (like deep neural networks and LLMs) whose internal structures are extremely complex with potentially trillions of parameters evolved through self-learning on massive data, achieving complete, absolute transparency faces not only enormous, potentially fundamental technical hurdles but also creates numerous dilemmas regarding trade secret protection, system security, and even effective information communication.

• Different Dimensions & Levels of Transparency: Transparency is not a monolithic, black-and-white concept but a multi-dimensional, multi-layered construct. We can understand its specific requirements from different stages of the AI lifecycle and different information levels:

  • Data Transparency: Concerns whether sufficient information about the data used to train and (if applicable) evaluate and run the AI model is available. E.g.:
    • What are the main sources of training data? (Public web data? Specific book corpora? Internal business data? User-generated content?)
    • What were the collection methods and time frame?
    • What main features or variables does the data contain? What is its scale, coverage, and representativeness?
    • What preprocessing, cleaning, filtering, or annotation steps were applied? What were the standards and methods?
    • What known potential biases, limitations, or quality issues exist in the data? (Real-world Consideration: For commercial companies, high-quality, carefully curated training datasets are often core competitive assets and trade secrets. Demanding full disclosure is usually unrealistic. Thus, data transparency often involves providing necessary metadata about data characteristics and limitations, balanced with protecting commercial secrets.)
  • Algorithmic/Model Transparency: Concerns whether the AI model’s internal structure and working principles are knowable. E.g.:
    • What is the specific algorithm type and architecture used? (Transformer-based LLM? CNN? GBDT?)
    • What are the key hyperparameter settings? (Number of layers, nodes, learning rate, etc., though less meaningful to non-experts).
    • Are the model’s specific parameter weights (potentially trillions for deep learning models) public?
    • Is the model’s source code available for inspection and audit (for open-source models)? (Real-world Consideration: For closed-source commercial models (like GPT-4, Claude 3), these internal details are usually strictly confidential. For open-source models (like Llama 3, Qwen), while code and (sometimes) weights are public, this doesn’t automatically equate to full understandability of their extremely complex internal operations, even for experts.)
  • Design & Development Process Transparency: Concerns whether information about the key design decisions made during model development, the chosen optimization goals (e.g., prioritizing accuracy vs. fairness?), ethical principles considered and followed, risk assessments conducted and mitigation measures taken, and trade-offs made between different value objectives is adequately documented and potentially disclosable within appropriate limits (e.g., to regulators, auditors, research community). This helps understand the rationale and considerations behind the model’s design.
  • Decision Transparency / Explainability: Concerns, for a specific prediction, recommendation, or decision made by the AI system, how it arrived at that particular result from the input. What was the main basis for this derivation? Which input features had a key influence? What (approximable) logical rules or judgment patterns did it follow internally? (This is closely related to Explainability (XAI), discussed in the next section, and is arguably the most crucial manifestation of transparency at the level of specific decision application.)
  • Governance, Deployment & Performance Transparency: Concerns whether the overall governance framework surrounding the AI model, its actual deployment environment, application scope limitations, user policies, and related risk management measures are clearly visible. Also, are the model’s key performance evaluation results (accuracy on standard test sets, robustness test results, fairness metrics, known failure modes or limitations) made appropriately public or available to users, regulators, or affected parties?

• Severe Real-World Challenges in Pursuing Transparency:

  • Natural Barriers of Intellectual Property & Trade Secrets: AI models (especially advanced foundation models developed by large tech companies with huge investment) and their unique, curated training datasets constitute core IP and trade secrets. Demanding their full disclosure is often commercially infeasible and faces strong resistance. Finding the delicate balance between promoting transparency for public interest and protecting legitimate incentives for innovation and IP rights is a central challenge for global AI governance and legislation.
  • Security Risks & Concerns about Misuse: Excessive transparency, such as fully disclosing detailed architecture, all parameter weights, or complete training data, could make models more vulnerable to various attacks and misuse. E.g., attackers might more easily find vulnerabilities to design effective Adversarial Attacks; malicious users might more easily copy or steal models, conduct Model Inversion to extract sensitive training data, or even use public information to generate harmful or illegal content (e.g., fine-tuning for disinformation). Thus, pursuing transparency must carefully consider potential security risks and involve prudent trade-offs.
  • The “Visible but Incomprehensible” Dilemma due to Extreme Complexity: Even for fully open-source models where code, architecture, and weights are accessible, it doesn’t guarantee anyone can truly “understand” how they work. For deep neural networks with trillions of parameters and extremely complex structures, the internal information processing is highly parallel and non-linear. Attempting to fully comprehend how a specific decision arises from the interaction of these billions of parameters is nearly impossible even for top AI experts (let alone ordinary users or domain professionals). In such cases, merely achieving information “Visibility” (Transparency) does not necessarily lead to the genuine “Understandability” or “Controllability” desired by users or society. We might see the blueprint of the “black box” but still not grasp its mechanics.
  • Information Overload & Barriers to Effective Communication: Providing appropriate, meaningful transparency information to different audiences is an art. Bombarding non-expert users with excessive, overly technical information filled with jargon (e.g., showing neural activation maps or complex mathematical formulas) might not help them understand the AI system at all, but instead cause information overload, leading to more confusion, anxiety, or even resistance. This defeats the purpose of effective communication and trust-building. Transparency practices need to provide different levels and forms of information, tailored to the background knowledge, specific needs, and concerns of the recipient, making it easy to understand and use.

• Strong Demand for & Inherent Tension with Transparency in Legal Scenarios:

  • Due Process Requires Accountability: In law, the Due Process principle is fundamental. It generally requires transparency in administrative and judicial decision-making processes, allowing parties to understand the main factual basis and legal reasoning behind decisions affecting their rights, enabling effective challenge, rebuttal, appeal, or review. If a key legal judgment (e.g., evidence admissibility assessment, damage calculation, sentencing reference) heavily relies on output from an unexplainable “black box” AI system, the legitimacy, reasonableness, and acceptability of that decision process and outcome are fundamentally questioned. This creates deep inherent tension with the legal system’s demand for Accountability.
  • Mandatory Requirements from Emerging AI Regulations: As AI use spreads, governments and legislatures increasingly recognize the need for regulation. More laws are imposing explicit, mandatory transparency requirements on AI (especially systems deemed “high-risk”). For example, the EU AI Act sets forth various transparency obligations for high-risk AI systems, including providing detailed technical documentation, clear instructions for use, capability for event logging for traceability, informing users they are interacting with an AI system, etc., to enable effective market surveillance and risk assessment by regulators. China’s related regulations (like the one on generative AI) also raise requirements for algorithm transparency, labeling, etc. Legal service organizations using or developing AI systems must ensure compliance with these growing regulatory demands.
  • Potential Conflicts with Discovery Rules in Litigation: In lawsuits arising from AI system decisions (e.g., alleging discrimination by hiring algorithms, liability in autonomous vehicle accidents, loan denial based on AI credit scores), the adversely affected party is likely to demand, during Discovery, disclosure of deep technical information related to the AI system—algorithm details, training data info, model parameters, testing records, etc. This will almost inevitably lead to fierce disputes, with the party possessing the AI system asserting trade secret, IP protection, or technical infeasibility defenses. Courts in the future will face the extremely difficult task of balancing the litigant’s right to access necessary evidence for remedy against the AI developer’s legitimate interest in protecting commercial innovation secrets, possibly requiring development of new rules.
  • Lawyer’s Fiduciary Duty & Communication Responsibility to Client: Lawyers owe fiduciary duties to clients, including Duty of Loyalty and Duty of Communication/Candor. When lawyers substantially use AI tools in handling client matters in a way that could significantly affect case outcome or service quality, are they obligated to transparently inform the client about AI’s role, the specific technology relied upon, and its potential risks and limitations? This is an evolving ethical discussion. The prevailing responsible view suggests that disclosure is necessary, at least when AI use significantly impacts strategy, outcome expectations, or fees, or when the client explicitly asks. Communication should be honest and adequate, ensuring the client understands AI’s role and limits.

• The Principle of “Appropriate” & “Meaningful” Transparency in Practice: Given that achieving complete, absolute transparency is neither realistic (technically/commercially) nor always beneficial (security risks/info overload), the more mainstream and pragmatic approach in AI governance and practice is to pursue “Appropriate” and “Meaningful” transparency. This means dynamically providing different levels and forms of transparency information tailored to the specific risk level of the AI application, its potential impact scope, and the specific needs and comprehension capabilities of the information recipient, genuinely aimed at achieving trust, accountability, risk management, or compliance goals. E.g.:

  • Provide detailed technical documentation, risk assessment reports, compliance proofs to Regulators (as required).
  • Provide clear, understandable user manuals, feature descriptions, explicit warnings about capability limits, application scope, potential risks (esp. hallucination, bias, outdated knowledge) to End Users (lawyers, judges).
  • Provide meaningful explanations (even if simplified or partial) about the main factors considered, basic judgment logic, and avenues for seeking explanation, review, or appeal to Individuals significantly adversely affected by AI decisions (loan applicants denied, job candidates rejected).
  • Encourage developers of Open Source Models to provide open source code, detailed architecture descriptions, metadata about training data, research papers, performance evaluation reports as much as possible, to facilitate community oversight, understanding, and improvement.

III. Explainability (XAI): How Long and How Useful is the Key Attempting to Unlock the “Black Box”?

Explainability / Interpretability (XAI), though often closely linked and sometimes used interchangeably with transparency, focuses more specifically and deeply on the question: To what extent can humans (especially those using or affected by AI) Understand Why and How an AI model makes a particular decision, prediction, or recommendation? It’s not just about whether the system’s internals are “visible,” but whether its decision logic is “knowable and comprehensible.”

Explainability is considered a key pathway to addressing the AI “black box” problem. It is crucial for debugging and improving model performance, detecting and correcting potential biases, building user (especially professional user) trust in AI systems, ensuring fairness and reasonableness of system decisions, enabling effective legal accountability and regulation, and promoting effective communication in human-AI collaboration.

However, providing satisfactory, genuinely meaningful, and technically reliable explanations for modern AI models (especially deep neural networks and LLMs) whose internal mechanisms are extremely complex and learned from vast high-dimensional data, is widely recognized as one of the most difficult, core, and far-from-solved frontier challenges in AI today. The “keys” we currently possess seem neither long enough nor good enough to fully unlock this “black box.”

• Different Types & Goals of Explainability:

  • Global Interpretability: Aims to help us understand how the model makes predictions or decisions as a whole. Focuses on the model’s overall behavior patterns and internal mechanisms. E.g.:
    • Which input features are generally considered most important by the model for prediction? How are features ranked in importance?
    • Has the model learned any human-understandable, general decision rules or logical patterns internally? (E.g., by fitting a simplified surrogate model like a decision tree).
    • How sensitive is the model overall to changes in input features? Global interpretability helps gauge the model’s overall reliability, robustness, and potential biases.
  • Local Interpretability: Aims to explain why the model made a specific prediction or decision for a particular, individual input instance. Focuses on case-level Attribution. E.g.:
    • Why was this specific email classified as spam? (Perhaps due to certain keywords, low sender reputation).
    • Why was this particular applicant’s loan denied? (Perhaps due to key metrics like credit score, debt-to-income ratio failing to meet thresholds).
    • Why was this clause in this specific contract flagged as high-risk by the AI tool? (Perhaps it matched a risk rule, or its text features closely resembled known risky clause patterns). Local explainability is generally considered more important and directly relevant in scenarios requiring accountability for individual decisions, providing personalized justifications, or allowing individuals to appeal or question decisions (e.g., credit scoring, insurance pricing, medical diagnosis assistance, hiring screening, and most legal judgment or decision support scenarios).

• Inherent Challenges & Limitations of Current Explainable AI (XAI) Methods: (Principles mentioned in Sections 2.8 & 6.1, focusing here on depth of challenge)

  • Fundamental, Seemingly Irreconcilable Fidelity-vs-Comprehensibility Trade-off: A core, almost principled dilemma in XAI. On one hand, if we seek explanations with High Fidelity that accurately reflect the true, extremely complex, highly non-linear information processing and interactions within a complex AI model (like a DNN with billions of parameters), the explanation itself will almost inevitably be very complex, abstract, full of mathematical details, and difficult for even top non-AI experts to intuitively and fully understand (Low Comprehensibility), let alone average users or legal professionals. On the other hand, if we simplify, approximate, or abstract the explanation to make it easier for humans (especially domain experts or affected individuals) to comprehend (e.g., using a simple linear model, a set of decision rules, or importance scores for a few key features to approximate the black box model’s behavior for a specific input or local region), then the fidelity of this simplified explanation (i.e., how accurately it represents the original model’s complex process) may be significantly compromised. It might completely overlook crucial non-linear interactions or hidden logical layers, thus being seriously misleading. Finding an appropriate, meaningful balance between “explaining accurately enough to reflect reality” and “explaining simply enough for human understanding and use”—two often conflicting goals—is a fundamental challenge for all XAI methods.
  • Instability & Sensitivity of Explanations: Numerous studies show that explanations generated by many popular XAI methods (especially post-hoc methods like LIME, SHAP) can be very unstable or fragile. E.g., making tiny, almost imperceptible changes to the input data (Adversarial Perturbations) might sometimes cause the model’s final prediction to remain unchanged, but the corresponding explanation (e.g., which features are deemed most important) to change drastically and completely. This instability casts deep doubt on the reliability, robustness, and whether these explanations truly reveal the key drivers of the model’s decisions. If the explanation itself is so easily manipulated or altered, how much can we trust it?
  • Lack of Ground Truth for Evaluating Explanations: Evaluating whether an explanation method is “good” is inherently difficult because we usually don’t know precisely what the “true” complete internal “thinking” process of an extremely complex AI model is (it likely doesn’t “think” in a human-like symbolic, logical way at all). Thus, we lack an objective, absolute “Ground Truth” standard to judge if an XAI explanation truly “correctly” reflects the model’s internal mechanism, or which explanation is “better” than another. Current evaluation often relies on indirect, sometimes subjective Proxy Metrics, e.g.: Does the explanation align with domain expert intuition? Does it increase user trust in predictions? Does it help debug model errors faster? Does it help users perform a specific task better? These metrics themselves have limitations and may not be universally applicable.
  • Potential for Malicious Manipulation (Adversarial Explanations): Just as “Adversarial Attacks” can trick models into wrong predictions, there’s also a risk of “Adversarial Explanations.” Attackers might target the explanation mechanism itself, not the prediction. They could craft specific inputs such that the model makes an incorrect or even harmful prediction, but the accompanying explanation looks perfectly reasonable, trustworthy, even “fair” or “harmless.” Such manipulated explanations could effectively mask the model’s errors, biases, or malicious intent, making it harder for users or regulators to detect problems, posing severe security or ethical risks.
  • Diverse Needs of Different Audiences Hard to Satisfy Uniformly: Different stakeholder groups have vastly different needs regarding the type, depth, and format of AI explanations:
    • AI Developers/Researchers: Need highly technical, fine-grained, deep internal explanations for understanding behavior, debugging, improving performance, or theoretical innovation.
    • Domain Expert Users (e.g., doctors using AI diagnostics, lawyers using AI contract review): Need explanations that integrate with their professional knowledge, help validate model judgments, understand limitations, and ultimately make better informed professional decisions. They don’t necessarily need deep math but need to know what key info was used and roughly what logic was followed.
    • Regulators/Auditors: Need documentation and evidence demonstrating the decision process complies with relevant laws (anti-discrimination, data protection), assessing potential risks, and ensuring accountability mechanisms are in place.
    • Average Individuals Directly Affected (e.g., loan applicant denied, job candidate screened out, party impacted by adverse judgment reference): Need simple, intuitive, non-technical explanations that help them understand “Why me?”, “How can I improve?”, or “How can I appeal?”. Clearly, no single current explanation method or technique can simultaneously satisfy all these diverse, multi-level, sometimes conflicting needs.

• Specific Challenges for Explaining Large Language Models (LLMs): Achieving meaningful explainability for currently dominant LLMs is particularly difficult due to:

  • Extreme Scale: Modern LLMs have hundreds of billions or even trillions of parameters. Tracking the complete computational path for a specific output (like generating a sentence) or understanding the specific role and complex interactions of these trillions of parameters is computationally and cognitively almost impossible. The complexity far exceeds that of the human brain.
  • Opacity of “Emergent” Abilities: Many surprising complex capabilities exhibited by LLMs—like multi-step logical reasoning, in-context learning, displaying world knowledge and common sense, even generating “creativity”—seem to “Emerge” spontaneously when model scale crosses certain thresholds. The underlying, specific neural computation mechanisms and principles for these emergent abilities are currently not fully understood even by the scientific community. Providing convincing explanations for abilities whose origins are not fully grasped is naturally extremely difficult.
  • Limitations of Attention Weights as Explanations: While Attention Weights (especially from Self-Attention and Cross-Attention in Transformers) provide valuable clues about which parts of the input text or prior context the model “focused on” when generating a specific output token (which words got higher attention scores), this should absolutely not be taken as a complete or sole causal explanation for the final decision or generation. High attention might not be the sufficient or necessary cause; low-attention parts might still significantly influence the outcome through complex indirect paths. Over-interpreting attention maps can be misleading.
  • Interpretive Value vs. Potential Misleading Nature of Chain-of-Thought (CoT): As discussed, CoT prompting guides LLMs to output their intermediate “thinking steps.” This undoubtedly significantly improves performance on complex tasks and greatly enhances a certain degree of transparency and understandability of the output process, making it easier for users to check the logical flow. However, we must be very cautious in recognizing that this generated “chain of thought” is more likely a reasoning process the model “role-plays” or “simulates” based on learned patterns to better fulfill the task (generating a CoT-formatted, seemingly logical answer). It does not necessarily (and likely does not) fully and faithfully reflect the actual, potentially very different information processing logic occurring within its complex neural network computations. Overly trusting this “chain of thought” as the model’s “true thinking” can be misleading. Nevertheless, CoT remains one of the most important and practical means currently available for enhancing LLM transparency, debuggability, reliability, and human-AI collaboration efficiency, provided we maintain a clear understanding of the nature of the explanation it provides.

• Law’s Unique and Higher Demand for “Meaningful Explanation”:

  • Legal decision-making typically requires more than just knowing “which factors are correlated with the outcome” (e.g., XAI might tell you ‘credit score’ and ‘income’ were most important for a loan decision). It demands a deeper understanding of “how these identified relevant factors were combined with specific, relevant legal rules or principles, through an acceptable legal reasoning process, to ultimately derive this specific legal conclusion or decision.” In other words, the legal field often needs not just Feature Attribution but Substantive Reasoning consistent with legal thinking. Many current mainstream XAI methods (especially those focusing on ranking input feature importance like LIME, SHAP) mostly fail to provide this depth of rule-based, logic-based explanation recognizable by legal professionals.
  • Especially for AI systems potentially used to directly assist judicial decisions or sentencing recommendations, if their suggested outputs are adopted or heavily referenced by judges in judgments, they must be able to provide sufficiently clear, logically rigorous explanations supported by facts and law, suitable for inclusion in the reasoning part of the judgment and capable of withstanding appellate scrutiny. This places an extremely high, perhaps currently unattainable, demand on AI system explainability. This is a key reason why AI application in core judicial decision-making remains highly limited and controversial.

Aim for “Sufficient Explanation,” Not “Complete Explanation,” Tailored to Context

Given that achieving complete, mechanistic causal explanations for extremely complex AI models (especially LLMs) is nearly impossible with current technology, our practical goal might need adjustment: instead of hoping for a perfect “complete explanation” revealing everything, aim for providing “Sufficient,” “Meaningful,” and “Audience-appropriate” explanations based on the specific application context, potential risk level, and the core purpose the explanation needs to serve (e.g., for debugging? building trust? meeting compliance? providing grounds for appeal?). That is, provide enough information to meet the most critical needs for accountability, debugging, trust-building, or compliance in that scenario, even if the explanation is a simplification, approximation, or localized view of the model’s complex internal workings.

We need to dynamically and differentially determine the required depth, form, and rigor of explanation based on the AI application’s risk level and potential impact. For low-risk, low-impact applications (e.g., AI assisting internal document summarization), a lower degree of explainability might suffice. For high-risk applications with significant potential impact on individual rights or public interest (e.g., AI assisting credit scoring, hiring screening, medical diagnosis, judicial decision reference), higher levels and stricter forms of explainability assurance must be demanded. In the legal domain, given the seriousness of the work and consequences, the requirement for explainability should generally be set at a high level.

Conclusion: Navigating the Fog with Prudence, Seeking Balance and Progress Amidst Challenges

Algorithmic Fairness, Transparency, and Explainability—these three pillars form the core ethical bedrock and key technical challenges for building trustworthy, responsible AI systems acceptable to society and the legal system. In the legal field, with its paramount pursuit of justice, procedural fairness, and reasoned justification, the importance of these three pillars is elevated to an unprecedented height.

However, through the in-depth discussion in this section, we must clearly recognize that the path towards these lofty ideal goals is not smooth but fraught with profound, multi-dimensional, potentially even fundamental challenges and myths. The very definition of “Fairness” is mired in conflicting plural values and difficult trade-offs. “Transparency” faces natural boundaries when confronting trade secrets, security risks, and human cognitive limits. And “Explainability” encounters current technological bottlenecks and deep theoretical difficulties in attempting to unlock the powerful yet mysterious “black box” of deep learning.

As legal professionals, we need to deeply understand the complexity of these challenges, the inherent contradictions, and the true capability boundaries and inherent limitations of current AI technology. We must absolutely not blindly trust any hype or false promises about AI achieving “perfect fairness, absolute transparency, and complete explainability.” When evaluating, selecting, and using any AI tool, we must conduct rigorous, independent, critical scrutiny and assessment of its actual performance and known limitations regarding fairness assurance, transparency level, and explainability capability.

Ultimately, resolving these profound challenges likely cannot rely solely on technological breakthroughs and evolution. It requires more concerted efforts in designing sound governance frameworks, formulating clear legal rules, establishing unified industry standards, fostering continuous cross-disciplinary dialogue and collaboration, and most importantly—in every specific application scenario, steadfastly upholding human prudent judgment, value balancing, and final ethical responsibility. Through these efforts, we strive to bridge the gap between ideals and reality, seeking dynamic balance and reasonable compromises among conflicting objectives, thereby ensuring that this technology of infinite potential truly, sustainably serves, rather than harms or distorts, the core values of the rule of law we cherish.

Navigating the fog of AI requires wisdom, courage, and above all, an unwavering commitment to guarding the spirit of law and human values. The next chapter will further explore how to construct effective AI governance frameworks to better address these challenges in practice.