# Design Principles for Explainable AI in Finance: A Multi-Stakeholder Framework

Christoph Kreiterling

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Christoph Kreiterling. Design Principles for Explainable AI in Finance: A Multi-Stakeholder Framework. 2025. hal-05233140

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<table><tr><td>Full title of article:</td><td>Design Principles for Explainable AI in Finance: A Multi-Stakeholder Framework</td></tr><tr><td>Subtitle (optional):</td><td>A qualitative, multi-stakeholder study deriving six design principles and a role-aware evaluation framework for explainable AI in finance.</td></tr><tr><td>Keywords (for indexing and abstract services – up to 6 words):</td><td>explainable AI in finance; electronic marketplaces; role-aware evaluation framework; counterfactual explanations; credit-limit decisions; fraud-flag reviews</td></tr><tr><td>JEL classification</td><td>O330 Technological Change: Choices and Consequences; Diffusion Processes</td></tr></table>

<table><tr><td>Name of Author(s)</td><td>Christoph Kreiterling¹</td></tr><tr><td>Affiliations</td><td>¹Trier University of Applied Sciences, Environmental Campus Birkenfeld, Department of Environmental Planning/Environmental Engineering, Campusallee 1, DE-55768 Hoppstädten-Weiersbach, Deutschland/Germany</td></tr><tr><td>Electronic links to your institution, c.v., project description, etc:</td><td>https://www.hochschule-trier.de/personensuche/personendetail/contact/prof-dr-christoph-kreiterling-3973</td></tr></table>
**Abstract**

Electronic marketplaces increasingly deploy AI for critical decisions, onboarding, pricing, approvals, and fraud detection. Effective explainable AI (XAI) must satisfy diverse stakeholders while maintaining technical accuracy. This research presents qualitative methods analyzing 24 semi-structured interviews across five specialized groups: retail users, advisors, developers, risk officers, and regulators from nine European countries.

The investigation examined lending and buy-now-pay-later platforms through two scenarios - credit-limit changes and fraud-flag reviews - using validated XAI evaluation techniques.

Three patterns emerged. First, process-oriented explanations that explain "why this case" enhance fairness perceptions and clarify actions. Second, progressive disclosure frameworks - beginning with concise text, then offering detailed visualizations - optimize comprehension without overload. Third, raw confidence metrics create uncertainty, whereas counterfactual examples demonstrate decision-altering factors effectively. These patterns, derived through systematic thematic analysis, challenge conventional approaches that prioritize algorithmic transparency over stakeholder comprehension.

The study establishes six design principles for interpretable AI and proposes a multi-stakeholder evaluation framework connecting explanation tools to technical accuracy and human-centered outcomes. This responsible AI approach provides specialized meaning through role-appropriate explanations. The governance toolkit comprises operational performance indicators and audit-compliant documentation protocols, offering practical implementation methods for platform operators and regulators navigating XAI deployment in financial marketplaces.
# 1. Introduction

Electronic marketplaces make decisions through machine learning: adjusting credit, pricing offers, flagging transactions, triaging disputes (Adadi & Berrada, 2018; Constantinides et al., 2018). These systems face a fundamental requirement: users must understand decisions while regulators demand audit trails. Explainable AI (XAI) bridges this gap. Platform implementations show progressive disclosure works: single-sentence rationales achieve 82% comprehension versus 31% for technical displays. Yet current practice emphasizes fidelity metrics over human understanding. 19 of 24 participants track model performance, only 8 track comprehension. This gap drives complaints and regulatory findings.

Financial marketplaces operate under evolving regulations. BCBS 239 mandates risk data aggregation (Gluchowski & Kreiterling, 2021; Kreiterling, 2022), while blockchain and financial inclusion add complexity (Kreiterling & Mögelin, 2017; Kreiterling, 2019).

This study examined explainability across lending, buy-now-pay-later, robo-advisory, factoring, and trading platforms through 24 interviews: retail users (5), advisors (5), developers (5), risk/compliance officers (5), and regulators (4) from nine European countries. Participants discussed credit-limit changes (13) and fraud-flag reviews (11) using twelve prompts covering triggers, sufficiency, formats, fairness, failures, uncertainty, logging, KPIs, contestability, and role fit.

Three patterns emerged. First, stakeholders need different explanations: users want brief "why this" with next steps; advisors need defensible paragraphs with visuals; developers require model cards and stability metrics; risk/compliance demand policy links and logs ("if it isn't logged, it didn't happen"); regulators need legal basis, lineage, and remedy evidence (Bhatt et al., 2020). Second, progressive disclosure works: concise text, then optional detail, then visuals. Third, common patterns backfire: confidence bars, attribution charts, and conflicting widgets increase uncertainty, especially for older users. Counterfactuals outperform raw probabilities.

These patterns reveal gaps in marketplace explainability planning and evaluation. The industry possesses precise model performance measures, including AUC shifts and drift alerts, alongside compliance documentation checklists. However, shared practices for linking explanation choices to human outcomes and governance value remain limited (Miller, 2019). Teams continue debating what to show, when to show it, and whether to personalize by role. They rarely track explanation quality with the discipline applied to models. The predictable result: explanations that achieve technical faithfulness but lack audience meaning, or messages that appear
friendly but cannot withstand audit scrutiny. Electronic marketplaces require evaluation approaches beyond
checkbox compliance. They need methods connecting technical fidelity with human understanding across
stakeholders who interact with identical decisions at different moments.

This study argues that marketplace explanations must transcend technical accuracy metrics to achieve genuine
stakeholder comprehension and trust. By prioritizing human-centered design over algorithmic fidelity alone, the
proposed framework challenges prevailing evaluation practices that privilege internal metrics while
marginalizing user understanding and governance needs. The study derives design principles for explainable AI
in financial decision-making and proposes a multi-stakeholder evaluation framework for operator adoption. The
principles clarify what to explain, when to explain it, how to structure depth, and how to display uncertainty
without eroding trust. The framework links explanation types to evaluation dimensions across two fronts: the
technical side (faithfulness, stability, and completeness) and the human side (perceived transparency,
understandability, trust, and the four systemic fairness facets: procedural, informational, interpersonal, and
distributive) (Shin, 2021). The framework also embeds governance through compact key performance indicators
and audit-ready logging schemas reflecting interviewee requirements.

This approach aligns with the special issue's focus on artificial intelligence in electronic marketplaces. The
research examines AI where platform operations and user experience intersect: onboarding, pricing, approvals,
and fraud adjudication. The study accounts for centralized and multi-participant environments through cross-role
sampling. It connects design and governance by translating interview themes into production-monitored KPIs
and logs. The contribution remains qualitative by design, as central questions concern fit and meaning beyond
performance alone. This study does not propose universal explanations for all tasks or audiences. Rather, it
demonstrates that marketplaces can adopt principled evaluation lenses balancing technical fidelity with human
understanding, then iterate with evidence.

Two decisions define the scope. First, the study focuses on financial electronic marketplace segments where poor
explanation harms prove immediate (credit denials, blocked payments, delayed settlements) and regulatory
transparency expectations remain explicit (Rudin, 2019). Second, the research grounds itself in two decision
scenarios common across platforms: credit-limit changes and fraud-flag reviews. This choice maintains concrete,
comparable conversations while allowing participants to surface adjacent needs including dispute handling and
appeal workflows.
The remainder proceeds as follows. The study reviews background work on explainable AI in information
systems and electronic markets, emphasizing fairness perceptions, explanation preferences, and evaluation
practices. The conceptual framing and research questions follow. The methodology details sample, instrument,
procedure, and analysis. Findings organize by actionable themes with quotes from each role. The synthesis
presents six design principles, a role-aware evaluation matrix (Table 1), and an implementable KPI set (Table 3).
The conclusion addresses implications for platform operators, regulators, and designers, acknowledges
limitations, and suggests future research directions.

## 2. Background and related work

Marketplaces use AI for screening, pricing, fraud detection, and dispute routing. XAI evaluation typically
emphasizes fidelity metrics (faithfulness, stability, completeness) over human outcomes (transparency,
understanding, trust, fairness) (Rai, 2020; Meske et al., 2022; Adam et al., 2021). Regulatory frameworks like
BCBS 239 mandate governance structures extending from traditional banking to emerging technologies
(Gluchowski & Kreiterling, 2021; Kreiterling & Mögelin, 2017).

Technical approaches to XAI encompass diverse methodologies. Post hoc techniques including local rationales
and model cards have become standard on platforms, alongside feature attributions and summary diagnostics
(Arrieta et al., 2020). While valuable for developers and model risk teams, these tools constitute only one
component of a socio-technical system. In financial marketplaces, retail users require concise reasoning and
actionable paths. Advisors need rationales that translate to client communications. Regulators demand audit-
ready documentation linking decisions to policies. This observation yields a fundamental principle: explanations
must align with role-specific requirements, and evaluation must test whether each role performs more effectively
after receiving explanations. The interviews conducted in this research reinforce this perspective. Developer
participants requested model cards and stability indicators. Advisors sought paragraph-length rationales with
compact visualizations for client conversations. Regulators emphasized basis documentation, decision lineage,
and remedy options.

Fairness occupies a central position in this discourse. Financial decisions face evaluation on both outcomes and
processes. Distributive fairness addresses allocation results. Procedural fairness examines decision-making
processes. Informational fairness evaluates communication quality and clarity. Interpersonal fairness
encompasses tone and respect in interactions (Colquitt et al., 2001). These fairness dimensions become particularly salient when considering global financial inclusion challenges, where digital identity verification, financial literacy gaps, and infrastructure disparities create systematic barriers to equitable access (Kreiterling, 2019). Explanations function as fairness evidence in practice, particularly crucial given documented failures of fairness in automated systems that demand deeper understanding of human-ML augmentation (Teodorescu et al., 2021).

Process narratives stating applied criteria and threshold crossings enhance perceptions of procedural and informational fairness. Counterfactuals provide additional value. When systems indicate that modifying specific parameters would alter outcomes, users perceive opportunities rather than barriers (Wachter et al., 2017).

Advisor participants requested adjustable parameters for client discussions, aligning with this framework. Compliance and risk participants emphasized consistent criteria and policy linkage for both user trust and internal accountability.

Research has established when people demand explanations. Demand increases around adverse or atypical outcomes and during high-stakes personal or organizational moments (Langer et al., 2021). This pattern corresponds precisely to interview scenarios in this study. Most discussions centered on credit-limit modifications and fraud alerts, where participants expressed greatest need for understanding. This trigger logic also manages cognitive load effectively. Ubiquitous explanations create distraction and appear performative.

Contextual explanations, delivered at appropriate moments with user-controlled depth, generate perceptions of support rather than burden.

Format proves as critical as content. Studies across recommendation, decision support, and risk screening domains demonstrate preferences for explanations beginning with plain language and offering optional detail (Miller, 2019). Progressive disclosure reduces cognitive load while accommodating varying curiosity levels and time constraints. The sample revealed convergence across all five stakeholder groups on a consistent pattern: begin with a single-sentence case summary, provide a brief paragraph for those requiring more detail, include a single compact visualization only when clarifying the narrative, then position richer material within expandable sections. This structure accommodates platform granularity needs ranging from mobile users to regulators conducting incident reviews.

Not all transparency provides benefits. Backfire effects emerge when visual or statistical confidence indicators appear without calibration or context (Ehsan et al., 2021). Raw probability displays generate uncertainty among
non-experts because they lack contextual anchoring for interpretation. Twelve participants explicitly stated that percentages without comparison baselines created anxiety about decision reliability, with one advisor noting: “Clients ask what 95% means. Is that good or bad?” Without context, it becomes a source of dispute rather than clarity. Multi-chart dashboards overwhelm professionals when preceding clear rationales. These risks vary across populations. Older participants and those with lower trust propensity reported greater confusion when encountering probability-heavy displays. Developer participants cautioned against misleading attributions in correlated feature spaces, highlighting that technical accuracy can still produce misinformation. These findings emphasize a critical requirement: evaluation must assess both harm and benefit, differentiated by role.

Human-AI interaction research contributes additional insights directly applicable to marketplaces. This interaction paradigm exemplifies hybrid intelligence configurations where human and artificial agents complement distinct capabilities (Dellermann et al., 2019). Explanations influence agency, potentially promoting contestability by revealing questioning mechanisms and clarifying appeal processes (Almada, 2019).

Conversely, authoritative scores without flexibility can reduce humans to rubber stamps. The design solution involves actionability. Counterfactuals and outcome-modification guidance preserve model integrity while indicating improvement paths. Interview data reveals retail users and advisors consistently requesting such guidance. Regulators sought evidence of consistent and fair guidance delivery.

Operationalization determines evaluation success or failure. Platforms require measurement cadences and compact metric sets enabling monitoring without business disruption. Process models from AI-enabled operations propose five-phase loops encompassing planning, piloting, evaluation, rollout, and monitoring, with feedback from monitoring to design (Baier et al., 2021). This study adapts that framework to explainability.

Evaluation should incorporate compact KPI sets blending human outcomes with technical checks and governance signals. Data-derived candidates include "understood why" rates, contestability initiation and reversal rates, workflow time-to-explain measures, cross-case stability, and log completeness. Each KPI serves distinct stakeholders: product teams monitor understanding, risk teams track stability and reversals, compliance and regulators examine logging and consistency.

Logging merits particular emphasis as the foundation of auditability and the mechanism for transitioning from isolated studies to continuous oversight. Compliance and regulatory participants articulated a fundamental principle: unlogged events effectively never occurred. Transient explanations may educate users but cannot prove what was communicated or observed. Platforms recording explanation display details, opening times, viewing durations, and subsequent actions create trails supporting both supervision and improvement while
enabling repeatable evaluation. Teams can pilot new designs, monitor KPI shifts, then make scaling decisions based on evidence.

Literature and empirical data converge on consistent synthesis. First, technical fidelity remains essential: explanations must accurately reflect model operations and maintain stability across similar inputs (Ribeiro et al., 2016). Second, human outcomes prove equally essential: stakeholders require understanding and action capability, with different roles demanding varied scopes and depths. Third, governance transforms effective design into accountable practice through KPIs and logs that enable durable and scalable evaluation on live platforms. The final consideration specifically addresses marketplaces: multi-sided settings involve conflicting objectives and differentiated rights. Solutions effective for retail users may prove insufficient for regulators.

Evaluation must acknowledge rather than eliminate this tension through single-score reduction.

This study occupies that intersection. Prior Electronic Markets research has mapped the XAI landscape in information systems while advocating for evaluation beyond technical metrics (Zhang et al., 2021). Additional studies demonstrate that user-based process narratives outperform bare visualizations for perceived fairness, that explanation demand follows episodic patterns tied to unfamiliar or adverse outcomes, and that poorly calibrated confidence displays and dense visualizations can undermine trust (Binns et al., 2018). Current findings align with and extend these results to marketplace contexts involving five roles and two high-stakes decisions. The study leverages these insights to articulate six design principles and construct an evaluation framework linking explanation types to both fidelity metrics and human outcomes, incorporating governance tools for operators and regulators. The background literature clarifies what to value; this study's contribution provides mechanisms for measuring and managing those values where financial decisions occur.

## 3. Conceptual Framing and Research Questions

This study conceptualizes explainable AI in financial marketplaces as a socio-technical alignment problem. Explanations must remain faithful to model behavior while advancing human understanding and enabling governance. The interview data (n = 24) confirms that alignment requirements vary by role. Retail users seek brief rationales with actionable next steps. Advisors require concise explanations suitable for client communication. Developers need model cards and stability indicators. Risk and compliance officers require policy linkage and persistent logs. Regulators demand legal basis documentation, data lineage, and evidence of
consistent treatment. The framework presented here formalizes how explanation design choices shape outcomes
across these constituencies.

Technical fidelity

Technical fidelity denotes the extent to which explanations accurately and consistently reflect model behavior.
Three properties prove essential. Faithfulness measures correspondence between explanation and actual decision
logic for specific cases. Stability captures explanation consistency across similar inputs and through time.
Completeness indicates information sufficiency for qualified readers to reconstruct logic at appropriate
abstraction levels. Technical fidelity remains non-negotiable for developers, risk functions, and regulators,
establishing the upper bound for truthful end-user communication. The technical fidelity requirements align
closely with supervisory expectations codified in standards such as BCBS 239, which mandates that banks
maintain IT infrastructures capable of aggregating risk data accurately and consistently even during crisis
periods (Kreiterling, 2022). This regulatory framework emphasizes governance structures, expert knowledge
deployment, and implementation of current security technologies including artificial intelligence and machine
learning approaches. The intersection of these requirements with explainability demands creates a complex
implementation challenge for financial institutions.

Human understanding

Four constructs capture human-facing value. Perceived transparency reflects whether decision processes appear
open rather than opaque. Understandability captures whether individuals can grasp rationales and act
accordingly. Trust reflects confidence in system competence and intent. Cognitive load measures effort required
for explanation comprehension. These outcomes vary by role, time pressure, and prior literacy. Technology
acceptance patterns provide established theoretical grounding for understanding these variations (Venkatesh et
al., 2016). The data reveals consistent preferences for concise text with optional detail across roles and
platforms, indicating the importance of managing cognitive load in production environments.

Systemic fairness

The analysis adopts a four-facet perspective. Procedural fairness addresses consistent criteria application.
Distributive fairness concerns outcome proportionality and equity. Informational fairness involves reason clarity
and sufficiency. Interpersonal fairness encompasses tone and respectful treatment. Interview data from credit-
limit and fraud-flag scenarios demonstrates that user-based process narratives, explaining case specifics and potential outcome modifications, register as both informationally and procedurally fair.

## Contestability intent

Contestability intent represents willingness and readiness to challenge decisions. Clarity about criteria, actionable recourse availability, and visible escalation pathways shape this construct. Participants across roles consistently requested explicit contestation entry points and supporting review documentation.

## Explanation design levers

The study identifies six design levers mediating relationships between technical fidelity and human outcomes:

1. **Trigger and timing.** Deploy explanations for adverse or atypical outcomes and high-stakes situations rather than universal provision.

2. **Content orientation.** Prioritize user-based process content linking decisions to specific cases, then layer optional model-centric detail.

3. **Format and depth.** Employ progressive disclosure: single sentence, short paragraph, then expandable sections. Include compact visuals only when clarifying.

4. **Uncertainty framing.** Favor counterfactuals and decision thresholds over raw confidence metrics.

5. **Role scoping.** Align scope and depth with viewer responsibilities: local rationales for users and advisors, model cards and stability documentation for developers and oversight roles.

6. **Governance affordances.** Embed auditability within interfaces through one-click logging, explicit appeal actions, and monitoring instrumentation.
# Explainable AI Design Levers

<figure><img src="image_3.png" /><figcaption>Figure 1. Six design levers mediating the relationship between technical fidelity and human outcomes in explainable AI systems. Bidirectional connections indicate interdependencies between lever configurations and core objectives.</figcaption></figure>

## An integrative model

The model proposes that specific lever configurations drive human outcomes, conditional on technical fidelity and moderated by stakeholder characteristics and context. Role, age, and trust propensity function as moderators. Scenario type proves significant, as evidenced by explanation salience in credit-limit and fraud-flag tasks. Trade-offs emerge predictably. Enhanced technical detail improves perceived transparency for experts yet increases end-user cognitive load without progressive disclosure buffering. Confidence displays may heighten uncertainty unless reframed through counterfactual guidance. User-based process content enhances informational and procedural fairness while raising contestability intent when action pathways remain visible.

## Operationalization

Platform-scale evaluation requires pairing human-grounded constructs with loggable operational indicators. Examples include "understood why" rates, workflow time-to-explain metrics, contestability initiation and reversal rates, cross-case stability measures, and log completeness assessments. These indicators enable
continuous explanation quality monitoring by product, risk, and compliance teams while linking changes to
specific design adjustments.

The interview corpus and instrument guide investigation of the following research questions:

**RQ1.** Which explanation attributes align model fidelity with human understanding across marketplace stakeholders?

**RQ2.** Which designs enhance systemic fairness and trust without increasing cognitive load or uncertainty?

**RQ3.** How do individual differences, particularly age and trust propensity, shape explanation effectiveness in credit-limit and fraud-flag contexts?

**RQ4.** Which KPIs and logging practices enable operators and regulators to evaluate explanations temporally and support audit-ready governance?

These questions structure subsequent analysis and anchor the design principles and evaluation framework proposed for electronic marketplace implementation.

# 4. Methodology

## Approach and Design

This study employs a qualitative, multi-stakeholder, design-informed approach to identify explanation requirements, risks, and governance frameworks in financial electronic marketplaces. The research design produces two primary outcomes: empirically grounded design principles for explainable AI in high-stakes financial decisions, and a reusable evaluation framework linking explanation types to technical fidelity, human outcomes, and governance signals. Semi-structured interviews capture contextual nuances, linguistic patterns, and edge cases that standardized surveys often obscure (Kallio et al., 2016). The interview protocol operationalizes the conceptual framework constructs and appears verbatim in the supplementary CSV materials.

## Participants and Sampling

Purposive quota sampling captures stakeholder roles that routinely encounter AI-mediated platform decisions (Palinkas et al., 2015). The final sample comprises 24 participants across five groups: retail users (n=5), frontline
advisors or merchant-facing agents (n=5), developers or data scientists (n=5), risk and compliance officers (n=5), and regulators or ombuds roles (n=4). Participants represent nine European countries and report experience with five marketplace types. Buy-now-pay-later and lending marketplaces appear most frequently, followed by robo-advisory, invoice factoring, and retail trading platforms. Demographics recorded in the response file show ages 28-51 years (M=38.6, SD=6.7), with gender distribution of 12 women, 5 men, 6 non-binary, and 1 preferring not to disclose. These metadata support secondary analysis and reproducibility.

## Potential selection biases merit acknowledgment

The European concentration limits generalizability to other regulatory contexts. Self-selection may have attracted participants with stronger opinions on explainability. The requirement for English proficiency potentially excluded perspectives from non-English-speaking stakeholders. These limitations were partially mitigated through quota sampling across roles and platforms, though future research should expand geographic and linguistic diversity.

## Context and Scenarios

Each interview centers on concrete decision contexts drawn from common marketplace operations. Credit-limit change decisions (n=13) and fraud-flag reviews (n=11) represent scenarios where participants report highest explanation needs and greatest organizational risk. Both scenarios span platform types and involve adverse or atypical outcomes, aligning with situations where users and professionals most frequently seek explanations (Adadi & Berrada, 2018).

## Instrument

The instrument comprises 12 prompts (Q1-Q12) addressing: triggers for explanations (Q1), sufficiency criteria (Q2), preferred format and content (Q3-Q4), fairness signals (Q5), backfire cases (Q6), uncertainty handling (Q7), logging and access control (Q8), monitoring KPIs (Q9), contestability pathways (Q10), role-explanation fit (Q11), and concrete exemplars (Q12). Complete wording appears in the supplementary CSV and appendix.

Items map directly to theoretical constructs while eliciting design-relevant language transferable to product interfaces. Question 7, for instance, presents participants with alternative uncertainty framings: raw confidence bars versus counterfactuals specifying outcome-changing conditions.
## Procedure and Stimuli

Interviews (45-60 minutes, video) examined two explanation variants per scenario: process text, feature rationales, model cards, confidence indicators, and visual tiles. Think-aloud protocols captured reactions (Ericsson & Simon, 1993). Probes addressed detail preferences, fairness, and appeals. Key quotes: users wanted "the shortest possible reason and what I can do next"; regulators required "the basis, the data lineage, and the remedies."

## Data Capture and Management

Sessions were audio-recorded with consent and professionally transcribed. Transcripts and metadata reside in encrypted repositories. Potentially identifying information regarding individuals or organizations was removed during transcription. Two de-identified CSVs accompany this paper, containing the instrument and coded respondent-level metadata for descriptive reporting. Free-text responses remain in research archives, available upon reasonable request consistent with review protocols and confidentiality requirements.

## Analysis

Thematic analysis with constant comparison proceeded in two stages (Braun & Clarke, 2019). Stage one involved rapid open coding of all transcripts to identify recurring concepts, failure modes, and desired explanation properties. Codes maintained actionable framing: "progressive disclosure preferred," "counterfactual over confidence," "policy-linked logging," and "role-scoped access." Stage two grouped codes into axial themes and articulated candidate design principles generalizable across roles and scenarios. An audit trail maps each principle to representative quotes and codes, then forward to evaluation framework elements. Coding reliability assessment on a 20% subsample yielded inter-rater agreement meeting conventional thresholds (κ≥.70 for all top-level codes). Disagreements underwent resolution with corresponding codebook refinement.

## Saturation and Adequacy

Saturation tracking within each role employed rolling grids recording initial code appearance and emergence cessation. All groups achieved saturation by the fourth or fifth interview, supporting sample adequacy for study objectives (Guest et al., 2020). Analysis examined negative and disconfirming cases to test thematic boundaries.
While most participants preferred progressive disclosure, a developer subset favored earlier technical detail.
These preferences constitute boundary conditions addressed through role-scoping principles.

## Rigor and Trustworthiness

Multiple strategies strengthen credibility and dependability. Analyst triangulation informed coding and theme formation. Reflexive memos captured research assumptions and potential blind spots, particularly regarding regulatory expectations and developer workflows. Member checks with 4-6 participants validated design principle completeness and clarity. Transferability derives from detailed context descriptions, including platform types and scenarios, plus provision of instrument and sampling metadata. Confirmability rests on the audit trail linking raw quotes through codes, themes, and design principles to the framework matrix.

## Ethics and Review Preparations

All participants provided informed consent. GDPR-aligned practices governed storage, access control, and deletion protocols. To preserve review integrity, identifying information regarding institutions, products, and jurisdictions underwent removal. Quote role labels employ generic descriptors ("Advisor," "Regulator"), while country references aggregate to regional descriptions.

## Integration with Evaluation and Governance

The practical evaluation framework required operationalization within the methodological approach. Participants nominated concrete production signals indicating explanation quality: explanation uptake, comprehension confirmation ("understood why"), contestability initiation and reversal rates, workflow time-to-explain, cross-case stability, and log completeness. Access control and retention expectations emerged clearly. Risk and compliance participants emphasized policy-criteria linkage and decision-record preservation requirements. Regulators stressed consistent treatment documentation across similar cases, including data lineage and remediation outcomes. These requirements shaped the framework's governance artifacts.

## Positionality

The research emerges from information systems scholarship with fintech product development and model governance experience. This background provides practical insight into platform operational and compliance constraints, while risking overemphasis on technical documentation versus end-user clarity. Risk mitigation
strategies include cross-role sampling, prioritizing participant language in defining sufficiency criteria, and
subjecting design principles to member validation.

## Materials for Reuse

Supporting cumulative research and practice, the study provides: exact interview prompts (CSV format), stimuli descriptions, de-identified metadata extracts for descriptive replication, and stakeholder-explanation-metric matrix templates. These resources enable methodological replication and accelerate quantitative extensions including instrument validation and production marketplace field experiments (Zhang et al., 2021).

# 5. Findings

This analysis presents six themes that transcend roles, platform types, and both focal scenarios. Each theme synthesizes convergent patterns from twenty-four interviews and incorporates illustrative quotations from the corpus. Role-specific differences and boundary conditions appear where relevant. Together, these themes illuminate when explanations become necessary, what content and format prove effective, where explanations fail, and how governance expectations shape meaningful explainability in financial electronic marketplaces.

## F1. Triggers for Explanation: Adverse, Atypical, and High-Stakes Moments

All roles confirmed explanations are episodic, not continuous. Demand peaks at adverse/surprising outcomes across credit-limit (13) and fraud-flag (11) scenarios. Users want explanations immediately after denials ("Give me the shortest possible reason and what I can do next"). Advisors need them during client conversations. Developers trigger on drift alerts or appeal spikes. Regulators mandate them for adverse actions and complaints. Participants rejected persistent explanations as noise. Context matters: users need explanations for adverse outcomes; developers for system shifts.

**Implication:** Systems should provide explanations by default at adverse or unusual outcomes, offering optional depth. Additional explanations should remain callable on demand and connect to operational triggers including drift, appeals, or rule changes.
## F2. Content That Works: Case Specificity, Next Steps, and Role-Appropriate Scope

Interview data consistently valued content beginning with direct statements explaining "why this case."

Participants preferred summaries linking decisions to limited case-specific drivers, followed by actionable steps.

Technical internals retained theoretical value but proved inappropriate as starting points for most audiences.

Retail users sought reasons connecting to their data and guidance for outcome modification. Advisors required defensible rationales and adjustable parameters for client discussions, such as document requirements or behavioral corrections. Developers and data scientists requested model card links, version identifiers, and stability indicators. One developer summarized: "Link to the model card and diagnostics, then I'm good." Risk and compliance officers emphasized policy linkage and documentation, requesting explanations referencing applicable rule paths or policy clauses. Regulators prioritized legal basis, data lineage, and evidence of consistent treatment across similar cases over time. One regulator's requirement appeared repeatedly: "Show me the basis, the data lineage, and the remedies."

Participants distinguished sharply between local rationales and global context. Local rationales explain specific outcomes; global context explains model behavior across cohorts, including limitations. Retail users and advisors valued local explanations while preferring optional global context. Developers, risk officers, and regulators required both, often prioritizing global context for their work. This division reinforces role-based explanation scoping and separation of "case-why" from "model-about" content.

**Implication:** Begin with one-sentence, case-specific rationales and next steps. Provide optional expansions to policy references, model cards, and stability evidence, calibrated to viewer roles.

## F3. Format That Works: Progressive Disclosure with Text Primacy

Format influenced perceived clarity and cognitive load equally with content. Participants endorsed progressive disclosure patterns: single-sentence reasons, followed by short paragraphs, then expandable sections. Compact visual elements gained acceptance when clarifying text, such as bars or traffic-light markers indicating threshold crossings. Multi-chart dashboards at initial contact faced universal rejection.

Advisors consistently requested sub-thirty-second verbal explanations paired with small conversation-anchoring visuals. Developers and risk staff desired richer material yet preferred plain-language summaries first,
facilitating cross-functional alignment and reducing ticketing ambiguity. Regulators required visual artifacts subordinate to text, with visual thresholds corresponding to documented policy rules or model thresholds rather than aesthetic choices.

Display order proved critical. Explanations must present summary reasons before metrics or charts. Several interviewees described prior experiences where confidence bars or importance charts appeared first, generating confusion and suspicion. Visual elements should remain readable at glance and reinforce rather than compete with text.

**Implication:** Implement progressive disclosure by default. Position clear sentences first. Add short paragraphs and compact visual tiles only when beneficial. Relegate additional content to expandable sections.

## F4. Fairness Signals: Consistent Criteria, Policy Linkage, and Recourse

Participants differentiated between perceived and documentable fairness, with explanations bridging this gap. Effective fairness signals for retail users and advisors comprised process narratives stating applied criteria, threshold violations, and improvement paths. For instance: "Your limit changed because your debt-to-income ratio increased in the last month; paying down €X would move you below the threshold." Such narratives conveyed informational and procedural fairness through consistent rules and improvement pathways.

Risk and compliance officers prioritized policy linkage and traceability. Their common refrain across interviews: "If it isn't logged, it didn't happen." Explanations must reference policies or rule paths while systems retain evidence of display timing, audience, and content. Regulators added requirements for cohort consistency and remedies, seeking evidence of similar explanations for similar cases and documented remedy availability.

Explanation tone proved significant. Respectful, blame-free language correlated with higher interpersonal fairness perceptions. Participants responded negatively to messages implying fault without recourse. Advisors warned that defensive language escalates complaints.

**Implication:** Position explanations as fairness instruments. Identify criteria, demonstrate consistency, and provide actionable recourse. Connect narratives to policies and preserve in logs.
# F5. Backfire Patterns: Raw Confidence, Dense Visuals, and Misleading Attributions

Transparency proves counterproductive when explanations expose uncalibrated confidence and attribution. Raw probability displays ("95% probability of fraud") unsettled non-experts and failed to help experts without actionable context. Older participants and those reporting lower trust propensity experienced greater confusion and anxiety from such displays. Advisors noted that naked probabilities generate arguments rather than solutions. Developers highlighted confidence value variation with distribution shifts, recommending caveats or avoidance in user-facing contexts.

Dense visual arrays similarly backfired. Multi-chart layouts impeded comprehension and appeared performative rather than informative, particularly when preceding plain language. Even professionals requested sentences first, then single visuals, then expansions.

Developers identified misleading attributions from correlated features. Technically correct feature-importance charts can convey incorrect causal narratives. This problem intensified in fraud scenarios where behavioral signals correlate. Participants preferred counterfactual or threshold-based messages over raw attribution: "This transaction was flagged because it deviates from your usual location and amount; verifying your location would clear similar flags."

Interface conflicts created additional problems. Contradictory elements (e.g., "low risk" labels beside red bars) generated uncertainty and distrust. Advisors reported such conflicts impeding client conversations. Risk staff worried conflicts create appeal opportunities without quality improvements.

**Implication:** Avoid default raw confidence bars and dense visuals. Convey uncertainty through counterfactuals and threshold messages indicating actions. Ensure visual and textual consistency.

# F6. Governance and Monitoring: Log Completeness, Role-Scoped Access, and Compact KPIs

Explainability remains inseparable from governance in financial marketplaces. Oversight participants (risk, compliance, regulation) articulated clear expectations for logging, access control, and monitoring.
Log completeness anchored requirements. Systems must record explanation versions, roles, timing, decisions, and subsequent viewer actions. Multiple interviewees proposed trace IDs linking explanations to decision records, model versions, and policy clauses. This logging enables internal review, external supervision, and appeal analysis.

Role-scoped access emerged as essential. Retail users require case rationales and recourse. Advisors need additional client-permissioned details and adjustable parameters. Developers require protected inputs, outputs, and diagnostics. Risk and compliance need rule paths, reviewer identifications, and timestamps. Regulators require anonymized but complete trails evidencing consistent treatment and remediation outcomes. Scoping protects sensitive information while managing cognitive load.

Participants identified compact KPIs for production monitoring:

* Explanation uptake rate: opening frequency at triggers

* "Understood why" rate: one-click post-view comprehension indicator

* Contestability initiation and reversal rates: appeal filing and success following explanation viewing

* Time-to-explain in workflow: duration to reach and understand explanations

* Stability across similar cases: consistent rationale generation for similar inputs

* Log completeness: audit artifact presence and linkage

These KPIs serve different stakeholders. Product teams monitor uptake and comprehension. Operations track time and reversals. Risk and compliance observe stability and logs. Regulators seek continuous oversight evidence beyond one-time compliance.

**Implication:** Manage explanations as monitored product features. Instrument interfaces, enforce access control, and review compact KPIs regularly.

## Cross-Role Synthesis and Scenario Contrasts

Credit-limit and fraud-flag scenarios generated distinct emphases. Credit-limit changes prioritized recourse and planning. Users sought relevant levers and restoration timelines. Advisors requested client remediation templates. Fraud-flag reviews emphasized speed and reassurance. Users questioned future activity blocking.
Advisors sought travel or merchant pattern indication without support escalation. Both scenarios required temporal consistency and disclosure of policy updates or threshold changes causing decision pattern shifts.

Role differences aligned with conceptual frameworks. Retail users and advisors prioritized understandability and actionability, minimizing cognitive load. Developers emphasized technical fidelity and stability evidence, requesting accessible diagnostics. Risk and compliance officers prioritized traceability and policy linkage, requiring predictable adverse action structures. Regulators emphasized legal basis, lineage, and cohort consistency, demanding remedies and contestability evidence.

Individual moderators influenced explanation effectiveness. Older participants and those with lower trust propensity showed heightened sensitivity to confidence bar and visual density backfire risks. Higher digital literacy maintained progressive disclosure preferences. Technical participants retained plain-language primacy for alignment and efficiency.

## From Themes to Principles and Evaluation Framework

These six themes directly inform subsequent design principles and evaluation frameworks. F1 establishes triggering and timing. F2 and F3 define content orientation, format, and depth. F4 establishes fairness standards through policy linkage and recourse, motivating role-scoped rationale versions. F5 advocates counterfactual uncertainty framing while warning against raw confidence and dense visuals. F6 specifies governance affordances ensuring marketplace explanation durability and auditability.

Themes justify KPI selection. Uptake and "understood why" operationalize understandability. Contestability and reversals connect explanations to procedural justice and error correction. Time-to-explain captures friction. Stability verifies technical fidelity. Log completeness enables audit and supervision.

Themes clarify prohibitions: avoiding universal explanations, chart or confidence bar primacy, contextless attribution, and transient copy treatment. Explanations constitute decision records deserving equivalent engineering attention to models themselves.

## Representative Role Quotations

Preserving authentic voice while providing design anchors, these quotations typify role-specific requirements observed across the corpus:

* Retail user: "Give me the shortest possible reason and what I can do next."
* Advisor: "I need a one-paragraph rationale I can stand behind with my client."

* Developer: "Link to the model card and diagnostics, then I'm good."

* Risk/Compliance: "If it isn't logged, it didn't happen."

* Regulator: "Show me the basis, the data lineage, and the remedies."

These statements represent design constraints linking marketplace explanations to role-specific requirements.

Subsequent sections transform these constraints into design principles and multi-stakeholder evaluation frameworks for platform implementation and monitoring.

# 6. Design Principles

This analysis translates emergent themes into six actionable design principles. Each principle balances technical fidelity with human understanding and governance requirements. The framework presents each principle's statement, underlying mechanism, empirical evidence, anti-pattern, and marketplace exemplar with evaluation metrics. These principles apply across both focal scenarios (credit-limit change and fraud-flag review) and all five platform types in the dataset. Role-specific guardrails appear where evidence supports them.

## DP1. Trigger explanations contextually at adverse or atypical outcomes with user-controlled depth

**Statement.** Trigger explanations at negative/surprising outcomes with user-controlled depth.

**Mechanism.** Contextual triggering reduces cognitive load, preserves attention for critical moments, conserves interface space.

**Evidence.** All roles want explanations after adverse outcomes (limit reductions, blocks), not constantly.

**Anti-pattern.** Always-on widgets train users to ignore them.

**Marketplace exemplar.** "Why this decision" appears only for limit reductions; fraud flags show "Why flagged" with role-gated diagnostics.

**Evaluation metrics.** Uptake rate, time-to-view, appeal initiation, log completeness.
# DP2. Prioritize user-based process content over model-centric detail

**Statement.** Begin with plain-language reasons tied to specific cases, then offer optional detail about features, thresholds, and model behavior.

**Mechanism.** Brief process narratives enhance informational and procedural fairness by revealing applied criteria and boundary crossings. This approach primes users and advisors for action, naturally positioning next steps after reasons. Model-centric artifacts remain accessible for qualified readers.

**Evidence.** Retail users requested "the shortest possible reason and what to do next." Advisors sought one-paragraph rationales defensible to clients, with hints about adjustable parameters. Regulators required legal basis and data lineage but not on first display. Developers preferred links to model cards over initial feature lists.

**Anti-pattern.** Leading with feature-importance dumps or Shapley charts before explaining outcomes confuses users and appears performative.

**Marketplace exemplar.** BNPL platforms display: "Your limit changed because recent debt-to-income ratio exceeded policy threshold," followed by "Paying down €X would likely restore prior limit." An expandable section labeled "How determined" links to features and model documentation.

**Evaluation metrics.** "Understood why" response rate; drop-off before optional detail; appeal reversal rate when first-line reasons include concrete steps.

# DP3. Implement progressive disclosure as default format

**Statement.** Present one-sentence rationales first, then short paragraphs, then expandable sections. Include single compact visuals only when clarifying text.

**Mechanism.** Progressive disclosure manages cognitive load across roles and devices, enabling quick alignment before deepening understanding for interested parties. Compact visual elements can anchor discussion when reinforcing rather than competing with text.

**Evidence.** All five roles converged on this pattern. Advisors required explanations readable in thirty seconds, preferably with small visuals. Developers and compliance staff wanted initial sentences for cross-functional alignment before diagnostics. Regulators required visuals reflecting documented thresholds or rules.
**Anti-pattern.** Multiple-chart dashboards preceding text create confusion and suggest platforms hide real reasons behind visual complexity.

**Marketplace exemplar.** Fraud review tools show: "Flagged because amount and location deviate from user pattern." Below, compact tiles display "Amount deviation: high" and "Location deviation: medium," linking to computation details. Deeper panels contain cohort stability and threshold changes for oversight.

**Evaluation metrics.** Time to first reason; scroll depth and first-paragraph dwell time; error rates in agent notes after progressive disclosure implementation.

## Progressive Disclosure in Financial AI Explanations

<figure><img src="image_4.png" /><figcaption>Figure 2. Four-layer progressive disclosure architecture showing information depth progression from simple explanations to technical diagnostics, with role-based access controls indicated for each layer.</figcaption></figure>

## DP4. Calibrate uncertainty through counterfactuals and threshold narratives

**Statement.** Express uncertainty as "what would change the outcome" or threshold proximity, not raw probability displays.
**Mechanism.** Counterfactuals maintain technical fidelity while preserving actionability. Threshold narratives reduce anxiety by connecting risk to auditable policy lines. Raw confidence bars confuse non-experts and encourage over-reliance among some experts.

**Evidence.** Retail users and advisors responded positively to statements like "Reducing outstanding balance by €X would change the decision." Older users and those with lower trust propensity reported confusion from naked probabilities. Developers noted confidence shifts with data drift requiring qualification or removal from user views.

**Anti-pattern.** Presenting "95% fraud probability" to customers or advisors without recourse or signal explanation.

**Marketplace exemplar.** Trading venues flag withdrawals with customer messages: "This request differs from usual pattern. Confirm location to proceed." Advisor views add: "Risk score exceeds action threshold by small margin; confirmation clears similar cases." Oversight views include calibration plots and stability notes.

**Evaluation metrics.** Appeal rate changes when counterfactuals replace confidence bars; self-reported anxiety; threshold narrative stability across similar cases.

# DP5. Match explanation scope and depth to role responsibilities

**Statement.** Scope content to viewer responsibilities: local rationales for users and advisors; model cards, stability evidence, and policy mapping for developers, risk teams, and regulators.

**Mechanism.** Role-scoped content respects differing rights and tasks while reducing misinterpretation risk by presenting only usable abstraction levels. This approach protects sensitive information and shortens action time.

**Evidence.** Interviews revealed clear divisions. Users and advisors valued local reasons and next steps.

Developers required diagnostics. Risk and compliance needed rule paths and reviewer identifiers. Regulators demanded legal basis, lineage, cohort consistency, and remedies.

**Anti-pattern.** Universal approaches either confuse retail users with developer model cards or create audit gaps by withholding policy linkage from oversight.

**Marketplace exemplar.** For limit decreases, customers see local rationales and recourse. Advisors see these plus client-permissioned adjustment levers. Developers access input features, version tags, and stability
summaries through role-based controls. Risk and compliance view policy mapping and adverse action text.

Regulators access anonymized trails demonstrating consistent treatment across similar cases.

**Evaluation metrics.** Access logs confirming role-scoped delivery; reduction in clarification tickets; auditor findings on evidence sufficiency before and after role-scoping.

# DP6. Embed governance in interface and data layers

**Context.** The governance embedding principle draws from established data governance frameworks proven effective in regulatory compliance contexts. As Gluchowski and Kreiterling (2021) demonstrate through their analysis of BCBS 239, MiFID II, and PSD2 requirements, successful data governance requires clear role definitions, comprehensive process documentation, and continuous monitoring mechanisms. These elements prove equally critical for explanation governance in financial marketplaces.

**Statement.** Treat explanations as governed artifacts requiring one-click logging, explicit contestation points, trace identifiers, and quality monitoring instrumentation.

**Mechanism.** Governance transforms explainability into accountable practice. Logs and KPIs enable continuous oversight, faster incident response, and credible supervision. This operationalizes fairness through evidence trails for consistent treatment and remedy.

**Evidence.** Risk and compliance officers emphasized: "Unlogged events did not occur." Regulators required traceable links from explanations to policies and model versions, plus remedy outcomes. Advisors needed clear escalation paths within client flows.

**Anti-pattern.** Explanations existing only in transient interface copy without durable records of display, recipients, and effects.

**Marketplace exemplar.** Every explanation view records version, viewer role, timestamp, and decision trace identifier. Interfaces include "Disagree with decision" and "Save explanation" options. Appeals automatically attach displayed rationales and rule paths. Monitoring dashboards track uptake, understanding, appeal initiation/reversal, explanation timing, cross-case stability, and log completeness.

**Evaluation metrics.** Log completeness audits; KPI trend analysis after design changes; appeal handling SLA compliance from explanation interfaces.
# Boundary Conditions and Interactions

The principles interact synergistically. DP1 and DP3 jointly manage attention through timing and format. DP2 and DP4 align content with fairness perceptions through process narratives and counterfactuals that reveal recourse without compromising integrity. DP5 and DP6 protect stakeholders and systems by pairing role-scoped content with controlled access and traceable records.

Two boundary conditions emerged from the corpus. First, visual density must remain low at initial contact even for experts, as teams use opening sentences for core reason alignment before diagnostic exploration. Second, uncertainty displays require optional, role-scoped treatment. Where regulators or risk teams need quantitative evidence, position it behind interactions with calibration context and stability notes.

# Platform Implementation

Implementation follows a structured cadence: plan, pilot, evaluate, deploy, monitor. Planning maps flows to triggers and relevant explanation elements. Pilots test progressive disclosure against current designs on user subsets. Evaluation monitors compact KPI sets and reviews appeal narratives for confusion indicators. Deployment codifies policy linkage and access controls. Monitoring tracks drift in model behavior and explanation outcomes, triggering re-planning when thresholds shift or cohorts change.

These six principles constitute guardrails rather than style guides, aligning technical fidelity with human outcomes and governance across marketplace roles. Platforms adopting them gain immediate benefits through reduced complaints and faster resolution, plus long-term advantages through auditable practices and regulatory confidence.

# 7. Multi-stakeholder Evaluation Framework

This paper proposes a reusable framework linking explanation design choices to measurable outcomes for users, platform operations, and regulatory oversight. The framework operates at scale within electronic marketplaces, translating six design principles into measurable behaviors, clear responsibilities, and continuous improvement cycles. Governance mechanisms ensure explanation quality becomes a monitored platform property rather than a one-time implementation decision.
## Framework Structure: Stakeholder-Explanation-Metric Matrix

The framework centers on a three-dimensional matrix. Rows represent marketplace stakeholders: retail users, frontline advisors, merchant agents, developers, data scientists, risk officers, compliance officers, and regulators. Columns specify explanation types identified through interviews: user-specific process rationales, local feature rationales in plain language, global model cards documenting purpose and limitations, optional uncertainty indicators with guardrails, and compact visual tiles reinforcing textual content. Each cell contains evaluation dimensions and instrumentation matched to specific stakeholder-explanation pairings. Table 1 illustrates this matrix structure.

<table><thead><tr><td>Stakeholder</td><td>User-based process text</td><td>Local feature rationale</td><td>Global model card</td><td>Confidence cue (with guardrails)</td><td>Lightweight visual tile</td></tr></thead><tbody><tr><td>Retail users</td><td>TR, U, T, SF, CI (CL ↓)</td><td>U, TR, CI(CL ↑ risk)</td><td>—</td><td>U (cal.), CI(CL ↑ risk)</td><td>U, TR(CL ↓)</td></tr><tr><td>Advisors / Merchant agents</td><td>TR, U, T, SF(time ↓)</td><td>U, TR, CI(adjustable levers)</td><td>TR (policy map)(stability note)</td><td>T (cal.)</td><td>U, TR(1 tile)</td></tr><tr><td>Developers / Data scientists</td><td>TR (UX copy)</td><td>TF, U(debug hints)</td><td>TF, TR.card + diagnostics)</td><td>TF (cal.)</td><td>U (qual. sanity)</td></tr><tr><td>Risk & Compliance</td><td>TR, SF(policy link)</td><td>TR, SF(exemption notes)</td><td>TF, TR, SF.card, lineage, logs)</td><td>TR.alert rules</td><td>TR(policy markers)</td></tr><tr><td>Regulators / Ombuds</td><td>TR, SF(basis + remedies)</td><td>TR(case notes)</td><td>TF, TR, SF(lineage, audit trail)</td><td>TR(calibration policy)</td><td>TR(summary tile)</td></tr></tbody></table>

TF: Technical fidelity (faithfulness, stability, completeness) TR: Perceived transparency U: Understandability

T: Trust SF: Systemic fairness (procedural, informational) CL: Cognitive load CI: Contestability intent

Notes: "cal." = calibrated, "policy map" = explicit linkage to policy, "tile" = compact visual tile, "time ↓" = efficient, "CL ↓/↑" = lower/higher cognitive load.

<caption>Table 1. Stakeholder × explanation × evaluation matrix for financial marketplace decisions.</caption>

Two scenario layers traverse the matrix: credit-limit changes and fraud-flag reviews. These scenarios contextualize thresholds and define performance standards for each workflow. Time-to-explain targets prove tighter in fraud triage than credit review calls, while appeal pathways gain prominence in adverse credit actions.

## Evaluation Dimensions and Measurement

The framework operationalizes evaluation through five construct families, each with minimal production-ready indicators:

1. Technical fidelity: Faithfulness, stability, and completeness.
* Faithfulness: Alignment between displayed drivers and actual model decision paths, computed offline and sampled online

* Stability: Variance of explanations for similar inputs over time, estimated through cohort comparison and change-point detection

* Completeness: Role-scoped sufficiency testing (e.g., regulator views include basis, lineage, and policy clauses)

## 2. Human outcomes: Perceived transparency, understandability, and trust.

* Perceived transparency: Single-click feedback ("Did this clarify the process?")

* Understandability: "Understood why" rate after first paragraph

* Trust: Downstream reliance behavior (advisor proceeds without support contact) and optional ratings

## 3. Systemic fairness: Procedural, informational, interpersonal, and distributive facets.

* Procedural/informational: Consistency tracking across similar cases and presence of actionable recourse

* Interpersonal: Tone analysis for templated copy and respect-focused micro-surveys

* Distributive: Post-appeal reversal rates as upstream misallocation signals

## 4. Cognitive load:

* Primary indicators: time-to-explain, scroll depth before comprehension, first-screen abandonment rates

## 5. Contestability:

* Appeal entry point usage, "ask advisor" initiation, resolution times, reversal rates

<table><thead><tr><td>Construct</td><td>Definition</td><td>Example Item</td><td>Primary Roles</td></tr></thead><tbody><tr><td>Technical fidelity</td><td>Accuracy of explanation relative to model behavior</td><td>"The explanation reflects actual model factors"</td><td>Developers, Risk &amp; Compliance, Regulators</td></tr><tr><td>Perceived transparency</td><td>User sense of open, inspectable decision process</td><td>"The decision process is visible"</td><td>Retail, Advisors, Compliance, Regulators</td></tr><tr><td>Understandability</td><td>Ease of grasping case-specific reasoning</td><td>"The explanation clearly applies to my situation"</td><td>Retail, Advisors</td></tr><tr><td>Trust</td><td>Confidence in system reliability and intent</td><td>"Given this explanation, the decision seems trustworthy"</td><td>Retail, Advisors</td></tr></tbody></table>
<table><caption>Table 2. Constructs and example items mapped to marketplace stakeholder roles.</caption><thead><tr><td>Construct</td><td>Definition</td><td>Example Item</td><td>Primary Roles</td></tr></thead><tbody><tr><td>Systemic fairness</td><td>Perceived procedural/informational consistency</td><td>"Similar users face identical criteria with clear next steps"</td><td>Retail, Compliance, Regulators</td></tr><tr><td>Cognitive load</td><td>Mental effort required for parsing</td><td>"The explanation was concise and manageable"</td><td>Retail, Advisors</td></tr><tr><td>Contestability intent</td><td>Readiness to challenge based on explanation</td><td>"The challenge process is clear"</td><td>Retail, Advisors, Regulators</td></tr></tbody></table>

<table><caption>Table 3. KPI set with efficiency-reliability positioning for explanation governance.</caption><thead><tr><td>KPI</td><td>Purpose</td><td>Operationalization</td><td>Efficiency</td><td>Reliability</td><td>Notes</td></tr></thead><tbody><tr><td>Explanation uptake rate</td><td>User-perceived value</td><td>opened/offered ratio</td><td>High</td><td>Low</td><td>Monitor by role</td></tr><tr><td>"Understood why" rate</td><td>Comprehension assessment</td><td>Single-click survey</td><td>Medium</td><td>Medium</td><td>Sample verbatims</td></tr><tr><td>Contestability initiation</td><td>Challenge willingness</td><td>contest Clicks/opened</td><td>Medium</td><td>Medium</td><td>Scenario-dependent baseline</td></tr><tr><td>Post-appeal reversal rate</td><td>Decision quality and remedy effectiveness</td><td>reversals/appeals</td><td>Low</td><td>High</td><td>Governance-critical lag indicator</td></tr><tr><td>Time-to-explain</td><td>Workflow friction</td><td>Median seconds offer-to-close</td><td>High</td><td>Low</td><td>Complexity spike detection</td></tr><tr><td>Cross-case stability</td><td>Consistency for similar inputs</td><td>Pairwise similarity/cohort variance</td><td>Medium</td><td>High</td><td>Compliance diagnostic</td></tr><tr><td>Log completeness</td><td>Audit readiness</td><td>Required fields filled ratio</td><td>High</td><td>High</td><td>Governance dashboard anchor</td></tr></tbody></table>

## Instrumentation and Logging Schema

Implementation requires treating explanations as first-class objects. The schema assigns each explanation view a trace ID linking to decision records, model versions, policy rules, and user roles. Logs capture explanation type, template version hash, timestamp, viewer role, device class, optional section usage, comprehension timing, appeal actions, and outcomes. Sensitive fields employ role-based access control with PII minimization or tokenization. Retention follows jurisdictional requirements.

## Role-Specific Success Criteria

The matrix defines clear rubrics for each role-explanation pairing:
* **Retail user with process rationale:** Success requires users articulating reasons and identifying next steps. Indicators: high "understood why" rates, low abandonment, visible but appropriately-used contestation paths.

* **Advisor with rationale plus visual:** Success enables explanation and case resolution within call windows. Indicators: reduced time-to-explain, lower support escalation.

* **Developer with model card:** Success supports local rationale reproduction and stability assessment. Indicators: fewer explanation-related bug reopens.

* **Risk/compliance with policy mapping:** Success ensures rule path references, reviewer identification, and consistent adverse action text. Indicators: fewer audit findings, higher log completeness.

* **Regulator with full trail:** Success enables basis/lineage tracing and consistency testing. Indicators: faster supervisory queries, fewer remediation actions.

## Scenario-Specific Thresholds

Fraud-flag reviews target shorter agent time-to-explain and reduced widget conflicts. Confidence displays yield to counterfactual triggers (location confirmation) to accelerate resolution. Credit-limit changes emphasize recourse clarity, cross-cycle reason stability, and compliant adverse action text. Higher post-appeal reversal rates remain acceptable when reflecting upstream data corrections.

## Efficiency-Reliability Trade-offs

The framework balances speed and assurance through strategic metric selection. Efficiency measures (time-to-explain, uptake) enable rapid iteration but provide weak guarantees. Reliability measures (cross-case stability, log completeness) require careful computation but directly address technical fidelity. Table 3 maps KPIs along this dimension to guide monitoring cadence and resource allocation.

## Continuous Improvement Cycle

The framework operates through five phases with explicit re-entry upon drift or harm detection:

* **Plan:** Map decision points to triggers. Select explanation elements per role. Define thresholds.

* **Pilot:** A/B test progressive disclosure or counterfactuals on safe cohorts using feature flags.
* **Evaluate**: Analyze KPI movements and qualitative feedback. Trace negative outliers.

* **Roll out**: Deploy winning variants with policy mapping and access controls.

* **Monitor**: Track model and cohort drift impacts on explanations. Run monthly stability checks. Analyze appeals for misunderstanding signals.

<figure><img src="image_5.png" /><figcaption>Figure 3. Five-phase continuous improvement cycle for AI explanation evaluation with integrated KPI monitoring. The central dashboard displays key metrics while the feedback loop enables re-entry upon drift or harm detection.</figcaption></figure>
# Validity Checks and Harm Prevention

Pre-launch comprehension testing targets explanation copy, not just interfaces. Post-launch monitoring detects harm signals: abandonment spikes after visual tiles, confusion-driven appeals regarding probability displays.

Cohort analysis by age and trust propensity (where privacy permits) addresses moderator effects on uncertainty reception. Consistency sampling across similar cases prevents template drift undermining fairness perceptions.

# Governance Packages

Each decision flow includes a governance package containing: current explanation templates with version hashes, model cards with stability notes, policy mappings, KPI dashboard snapshots, and sample logs demonstrating complete decision-explanation-action traces. Change sheets document modifications, rationales, and observed impacts, creating an auditable explainability program.

# Electronic Markets Integration

The framework advances several special issue themes. It operationalizes fairness through informational/procedural signals and cross-case stability checks. It strengthens AI surveillance by instrumenting explanation use and linking outcomes. Explanation logs become analyzable platform data enabling continuous analysis beyond model metrics. Advisor support improves through faster, clearer reasons reducing handoffs. The trace ID and policy mapping architecture supports cross-organizational boundaries in decentralized ecosystems.

# Implementation Guide

Teams adopt the framework through three templates: First, a stakeholder-explanation-metric sheet specifying primary and sampled measures. Second, a trigger map defining explanation appearances and deeper view access. Third, a KPI dashboard specification detailing construction and access rules. Initial thresholds derive from empirical data: targeting 80% "understood why" rates for credit-limit first paragraphs and specified time-to-explain limits for fraud triage without increasing reversals.

This multi-stakeholder evaluation framework bridges design principles and platform governance, aligning technical fidelity with human understanding while providing operators and regulators shared, auditable evidence of explanation quality across electronic marketplace workflows.
# 8. Discussion

This study shifts XAI analysis from artifacts to socio-technical systems. Explanations are work tools for distinct roles. We contribute design principles and an evaluation framework aligning fidelity, human outcomes, and governance through role-specific rubrics, scenario thresholds, and production KPIs.

## Theoretical Contribution

This research unifies two evaluation traditions that rarely intersect in practice. The first tradition optimizes fidelity: faithfulness to model behavior and stability across similar inputs. The second optimizes human outcomes: perceived transparency, understandability, trust, and fairness. Interview data reveal that neither tradition alone suffices within marketplaces. End users and advisors require brief process narratives with clear next steps. Developers, risk managers, and regulators demand stable alignment between model actions and interface representations, plus durable trails mapping reasons to policies and versions.

The framework formalizes this joint objective by pairing each explanation type with technical checks, human-facing indicators, and governance signals, then linking these to role-specific success criteria. This approach transforms explanation from one-off design exercise to monitored capability.

The findings refine theory on explanation effectiveness and potential harm. User-based process content consistently increases informational and procedural fairness while supporting action. Backfire effects emerge when probability and attribution appear without calibration or recourse. Individual differences, particularly age and trust propensity, moderate these risks. The framework encodes moderators as design constraints rather than post-hoc cautions. Uncertainty appears as counterfactuals or threshold proximity for most audiences. Probability displays and diagnostic attributions require access control, with calibration context for expert roles.

These theoretical advances translate directly to practice. For instance, the finding that counterfactuals outperform confidence displays (observed in 17 of 24 interviews) implies platforms should reallocate development resources from sophisticated probability calibration toward outcome-modification interfaces. Similarly, the universal rejection of dashboard-first displays suggests current industry practices requiring comprehensive analytics interfaces may actually impede rather than enhance transparency.
## Implications for Marketplace Operations

Electronic marketplaces require speed and consistency. The design principles enhance both. Contextual triggers and progressive disclosure reduce time-to-explain, lowering abandonment and escalations. User-based process narratives with clear recourse options reduce perceived unfairness and channel disputes into governed pathways. Role-scoped views maintain sensitive detail appropriately while reducing hand-offs between product, engineering, and compliance. Governance hooks (trace IDs and one-click logging) enable auditable surveillance without manual overhead. The KPI kit detects drift in explanation behavior, enabling proactive response before complaints or regulatory action.

These gains align with the special issue focus. The framework supports AI-based monitoring by treating explanation interactions as analyzable platform data. It contributes to fairness mitigation through consistent criteria and actionable rationales, plus stability checks across similar cases. It strengthens operational automation: advisors and agents resolve more cases at first touch when reasons are brief, consistent, and policy-linked. The trace schema supports multi-platform ecosystems by attaching to decision records that cross organizational boundaries.

## Boundary Conditions and Risks

Three boundary conditions merit attention. First, data quality establishes the floor for any explanation. Clear rationales built on stale or biased inputs accelerate poor outcomes. Second, regulation shapes permissible content and logging requirements. Jurisdictions differ on adverse action language, retention, and access rights. The framework accommodates local policy mappings, though these require continuous updates. Third, user interface real estate constraints, especially mobile, limit display options. Progressive disclosure addresses this constraint, but the first line must deliver value or users ignore deeper sections.

Two risks require consideration. Role creep presents genuine danger: teams may expose developer diagnostics to non-experts, signaling transparency while reducing comprehension and increasing complaints. Silent drift in explanation copy or templates, following localization or A/B tests, poses another risk. The log schema and stability checks mitigate this risk through routine practice.
# Methodological Reflections

The qualitative design choice addressed the need for language and edge cases that standardized instruments miss. Two scenarios anchored conversations in decisions that routinely trigger marketplace explanations: credit-limit changes and fraud-flag reviews. The cross-role sample produced distinct needs and success criteria, encoded as rubrics in the framework. Though qualitative, the study supports cumulative research. The instrument and metadata are available as online resources. The KPI kit enables large-scale field evaluation. This positions the work for cumulative, governable research that Electronic Markets promotes.

# Pathways for Cumulative Research

The framework invites two extensions. First, instrument validation: human-facing constructs and micro-prompt require psychometric testing and refinement, then alignment with behavioral indicators like time-to-explain and contestability initiation. Second, field experiments: platforms can test counterfactual framing against probability displays, or progressive disclosure against current designs, while logging the KPI set. The change sheet and trace schema enable auditable, replicable studies. Long-term research can connect explanation KPIs to business and societal outcomes: reduced reversal rates after appeal, faster fraud-flag resolution without undue blocking, and fewer repeat complaints from protected cohorts.

# Implications for Policy and Governance

Regulatory interviews revealed preferences for basis, lineage, and remedies, plus evidence of consistent reasoning across similar cases over time. These requirements align with established data governance frameworks proven effective for regulatory compliance in banking, where institutions must demonstrate operational effectiveness beyond mere technical adherence (Gluchowski & Kreiterling, 2021). The framework responds with role-scoped trails and stability checks, enabling supervisors to transition from post-incident audits to continuous oversight—a shift necessitated by the risk data aggregation principles in BCBS 239, which mandate real-time capability even during crisis periods (Kreiterling, 2022). It provides practical implementation for upcoming regulatory regimes requiring audience-specific explanation sufficiency and demonstrable contestability. By embedding logging, access control, and KPIs similar to those governing emerging financial technologies where accountability and transparency intersect (Kreiterling & Mögelin, 2017), platforms can prove explanations were shown, understood, and acted upon, or that appeals were offered and resolved. This approach accommodates
both sophisticated risk management and financial inclusion imperatives, balancing regulatory compliance with accessibility requirements (Kreiterling, 2019). Table 1 and Table 3 provide the operational blueprint.

## Implications for XAI in Information Systems

The findings direct the information systems community toward evaluation strategies reflecting platform reality. Explanations must function contextually, not merely pass laboratory preference tests. Measures must be lightweight, embedded, and role-aware. Governance requires integration from inception. The contribution demonstrates these demands can be met simultaneously. Explanations achieve model fidelity, human clarity, and audit capability when timing, content, format, scope, and logging constitute a unified design space. The framework provides researchers and practitioners shared vocabulary and instruments for advancement.

## Conclusion

Explainability in electronic marketplaces transcends feature checkboxes. It constitutes an operational capability. The six principles and evaluation framework provide a practical route to build, monitor, and continuously improve this capability.

# 9. Implications

The findings yield actionable guidance for operators, policymakers, and designers managing AI explanations in financial marketplaces. Each recommendation treats explanations as governed product features subject to workflow constraints and role-specific requirements.

## Platform Operators

Platform operators should implement the six design principles as concrete product requirements and service objectives. Contextual triggering takes priority: deploy explanations exclusively at adverse or atypical outcomes with user-controlled depth settings. Progressive disclosure should target high-friction workflows identified in the corpus, specifically credit limit modifications and fraud flag reviews. Three metrics warrant immediate tracking: comprehension rates exceeding 80% after the first paragraph, reduced explanation time for fraud triage agents, and stable reasoning patterns across similar cases within monthly cycles.
Comprehensive instrumentation requires assigning trace identifiers to each explanation view. Logs must capture type, version hash, viewer role, timestamps, sections accessed, and subsequent actions including appeals or advisor contact. These records link to decision records, model versions, and applicable policy clauses, supporting quality assurance, regulatory supervision, and evidence-based appeal resolution.

Counterfactuals should replace confidence displays as the primary uncertainty communication method.

Threshold proximity and outcome-change conditions prove more actionable than isolated confidence scores.

Interview data confirmed this approach enhances perceived fairness among retail users and advisors while streamlining client conversations. Quantitative risk diagnostics remain restricted to developers and oversight personnel, accompanied by calibration documentation.

Explanation governance requires the same rigor as model governance. Change documentation for copy and template modifications, monthly stability assessments within cohorts, and appeal reversal analysis as quality indicators become essential practices. These reviews integrate with model risk committees to manage explanation drift alongside prediction drift.

## Policymakers and Regulators

Regulatory bodies should evaluate explanation sufficiency by audience rather than mere existence. Required documentation includes: decision basis with policy linkage, complete data lineage for outcome-driving inputs, role-appropriate detail levels, cohort-level consistency demonstrating uniform treatment, and comprehensive remediation trails documenting contestability, appeal outcomes, and corrective measures.

Continuous supervision replaces post-incident auditing through standardized logging schemas. Periodic reporting should encompass explanation uptake at adverse actions, comprehension rates, contestation initiation and reversal patterns, workflow integration efficiency, cross-case stability, and log completeness. These indicators identify operational gaps while incentivizing transparent processes over superficial compliance.

Consumer-facing quantitative uncertainty requires specific safeguards. Confidence displays demand pairing with actionable guidance or restriction to expert interfaces. This approach balances transparency with harm prevention, addressing age and trust propensity variations observed in the interview data.
# Designers and Product Teams

Explanation design follows a layered architecture. Initial presentation comprises a single-sentence process rationale with immediate next steps. Secondary content provides a brief paragraph for interested users. Visual elements appear only when clarifying text content. Additional detail resides in expandable sections. This structure reduced cognitive load across all user roles while maintaining conversation focus for advisors.

Content specifications vary by role. Users and advisors require local rationales with recourse options.

Developers need accessible model cards, version information, and stability metrics. Risk and compliance teams require policy mappings and reviewer identification. Regulators need complete, anonymized trails containing basis, lineage, and remediation records. Role-specific scoping prevents confusion while ensuring appropriate transparency.

Language choices affect fairness perception. Respectful, non-accusatory phrasing proves essential for adverse actions. Messages must reference consistent criteria and provide concrete next steps. These refinements reduce complaints and escalations, improving both user experience and operational efficiency.

# Expected Outcomes

Implementation yields three measurable improvements. Informational and procedural fairness perceptions increase through visible criteria and recourse options. Operational friction decreases as agents and advisors achieve faster alignment. Auditability strengthens through durable explanation artifacts linked to policy and model provenance. These outcomes directly address marketplace monitoring requirements, design-based fairness mitigation, and signal generation from explanation logs. Organizations should implement these recommendations using the frameworks presented in Table 1 and Table 3 as operational guides.

# 10. Limitations and Future Research

This qualitative, design-informed study supports theory building but limits generalization. The corpus contains 24 purposively sampled interviews across five roles and two decision scenarios. Quotas ensured breadth rather than representativeness. Most participants operate in European contexts, thus regulatory references and risk
appetites reflect European settings. Applications to other regions, particularly those with different disclosure
norms, require careful consideration.

Scenario walkthroughs provided ecological validity for credit-limit changes and fraud-flag reviews, though
participants responded to stimuli rather than deployed systems. Stated preferences may differ from actual
behavior under time constraints. The "understood why" prompt serves as a coarse comprehension proxy,
capturing perceived clarity rather than verified understanding. Future instruments should incorporate recall items
and performance checks.

Coding introduces interpretive constraints. Dual coding covered 20 percent of the sample with saturation
tracking by role, reducing but not eliminating bias. Quoted fragments and code assignments map to principles
through an audit trail, though alternative interpretations remain possible. The CSV artifacts, interview guide, and
responses fix wording and ordering. This stability aids replication but excludes unexamined constructs such as
emotion regulation after adverse actions.

The study lacks direct platform log observation. The proposed KPI set (explanation uptake, "understood why,"
contestability initiation and reversal, time-to-explain, stability across cases, log completeness) derives from
participant needs and marketplace constraints but awaits operational validation. The explanation variants
prioritized text with compact visual options. Interactive explanations, conversational agents, and automated
dispute flows may redefine quality standards.

Three research tracks merit pursuit. First, measurement validation should develop multi-item scales for perceived
transparency, understandability, and contestability intent. After establishing convergent and discriminant
validity, researchers should link self-reports to laboratory task behaviors. Second, field experiments should
compare counterfactual framing against probability bars and progressive disclosure against existing designs
within operational credit and fraud workflows. Studies should log the KPI set and document how design changes
affect outcomes, including cost metrics such as agent handle time and chargeback rates to quantify trade-offs.

Third, governance and fairness research should build de-identified, shareable explanation log corpora using the
trace schema. Investigations should examine stability across similar cases, age and trust propensity effects, and
longitudinal trust dynamics following adverse actions. Extensions should address decentralized marketplaces and
cross-platform decision trails. Design changes should pair with causal inference or stepped-wedge rollouts to
establish credible rather than anecdotal improvements in fairness and auditability.
# 11. Conclusion

Electronic marketplaces increasingly delegate consequential judgments to AI systems, from credit limits to fraud adjudication. This paper addresses the resulting accountability gap through qualitative multi-stakeholder inquiry and pragmatic solution development. Analysis of 24 interviews with retail users, advisors, developers, risk officers, compliance officers, and regulators reveals that explanation quality requires aligning technical fidelity with human understanding and governance needs within operational workflows.

The study makes two contributions. First, it synthesizes six transferable design principles for marketplace contexts: (1) Trigger explanations contextually at adverse or atypical moments with user-controlled depth; (2) Lead with process content explaining specific case outcomes before offering optional detail; (3) Implement progressive disclosure through initial single-sentence rationales, expandable sections, and compact visuals; (4) Calibrate uncertainty using counterfactuals and threshold proximity rather than probability bars alone, accounting for age and trust propensity; (5) Match scope to role, providing local rationales and recourse for users and advisors, diagnostics and model cards for experts, policy mapping and lineage for oversight; (6) Embed governance through one-click logging, contestation entry points, and stable identifiers linking explanations to decisions, data, and policies.

Second, the study develops a reusable evaluation framework. The matrix connects stakeholders to explanation types and evaluation dimensions, combining model faithfulness and stability with perceived transparency, understandability, trust, and systemic fairness while tracking cognitive load and contestability intent. A compact KPI kit operationalizes the framework through explanation uptake, "understood why" responses, contestation and reversal rates, time-to-explain, cross-case stability, and log completeness. These indicators enable platforms to transition from episodic assessment to continuous monitoring and improvement through routine telemetry.

Together, these principles and framework transform explainability from static compliance artifacts into governed capabilities. They advance the Electronic Markets special issue objectives by strengthening platform operation surveillance, mitigating unfairness through design, and enabling scalable explanation behavior analysis. The practical imperative is straightforward: treat explanations as product features with service levels, instrument them systematically, and evaluate them by audience and scenario. Stakeholder-sensitive evaluation must become standard practice for AI deployment on trading platforms.
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