RESEARCH TOPIC / GOVERNANCE-RISK
Accountability, fairness, audit, and risk acceptance.
GROUNDING MAP
Explore 22 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
LLMs can shift answers toward a user's stance or pushback, and ratings miss it. Investment reviews should not count AI agreement as independent verification, should re-verify AI objections, and should probe how models update at rollout and on change.
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Design evidence for AI-agent actions around a chain of approval, execution, and state change—not full-content retention. Capture disputed content selectively under protection, then test reconstructability and minimization as separate release gates.
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A role-and-output reading of EU AI Act Article 50, translating human-facing disclosure, machine-readable marking, exceptions, and version evidence into one placement record.
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Instead of reusing a human session, separate the principal from the agent and evaluate a delegation that binds resource, action, audience and lifetime at execution time.
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After work has been redesigned, decide how remaining steps combine core systems, APIs, RPA, AI, and people—and what proves the operation can run without its builder.
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Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.
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Do not mistake general research on psychological safety for AI-specific causal evidence; integrate dissent, error reporting, stop proposals, and retaliation checks into the execution procedures for AI implementation.
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Given habituation to repeated warnings, false alarms, and ignoring due to irrelevant notifications, design a warning budget by risk, display variations, and mandatory stops.
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We design procedures that do not equate fairness with merely displaying explanations, but instead enable individuals to understand, present their circumstances, correct data, and proceed to human review and relief.
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Do not deploy based solely on the overall score of translation benchmarks; determine the scope of provision by analyzing culture-dependent items, native-speaker evaluations, worst-case performance per language, and relief pathways.
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We establish boundaries to return to human evaluation by auditing not only the agreement rate with human evaluators but also through reversal of answer order, redundancy, self-preference, and difficult cases.
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We organize work design by separating allocation, monitoring, evaluation, and reward by function, and setting minimum standards for autonomy, connectivity pressure, anxiety, and the ability to appeal, in addition to efficiency.
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This article demonstrates an audit design that distinguishes between the fluency of Chain-of-Thought and internal judgment reproduction, focusing on answer accuracy, causal fidelity, hidden cues, and separated execution logs.
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We design an evidentiary procedure that distinguishes AI text detectors from generation-time watermarks and includes paraphrase resistance, unknown models, group-based false positives, the author's version history, and avenues for appeal.
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Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.
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We do not leave AI privacy measures solely to post-collection anonymization; we reduce necessity, retention, duplication, learning, logging, and access across the entire lifecycle.
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By tracking data quality not only in terms of accuracy but also meaning, proxy indicators, and on-site correction pathways, we identify where numbers lead to judgment errors.
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This section outlines a method for managing model, data, search infrastructure, and guardrail updates by linking change scope, comparative evidence, approval, monitoring, and rollback.
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We outline an incident design that connects damage assessment, causality, evidence of versions/data/decisions, external reporting, and recurrence prevention, rather than ending with model suspension alone.
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We organize methods for auditing fairness without reducing it to a single score, by examining the usage context, harm, types of errors, intersecting populations, and post-deployment changes.
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We organize methods for separately viewing training, generation, and publication, and for making rights confirmation and provenance recording a single operational audit trail, drawing from primary sources in Japan, the EU, and C2PA.
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Proper support helps; flawed support amplifies errors. Drawing on research into automation bias and critical thinking, we consider how to build organizations that can question, stop, and revert.
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