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Let’s consider an AI-powered customer support chatbot for a bank. The failure can happen with misunderstanding the input (AI misunderstood “block my card” as “check my card balance), unexpected question (customer asks about a new product or service that the AI hasn’t been trained on), or database access loss (AI could not connect to the bank’s backend systems) AI should detect failures based on a confidence-level threshold, flagging uncertainty. It should also detect unconvincing or incorrect answers when the same customer repeatedly asks the same query. Also, if the connection to the backend database fails, the AI should detect the outage immediately and clearly tell that it is having trouble retrieving account details right now. Another good option is to use the customer’s feedback rating for the query resolution. If the customer gives a low rating, the AI-based chatbot should also detect its failure in answering customer questions. In all such scenarios above, the AI-based chatbot should offer the option to create a ticket or to initiate a chat with a customer service representative. Quick answers to frequently asked questions and easy-to-connect customers to resolve complex queries, or in case of failure, help build trust. With human help, the AI-powered chatbot must be trained with new product information as it is released, documentation updated to address unexpected questions about current products, and the root cause of database connection loss identified. All this will help the AI chatbot improve continuously and quickly answer customers’ questions without human intervention.

In publishing, especially within editorial processes and content recommendation engines, AI takes inputs from author details, reader behaviour metrics, tagging information, citation databases, and current trends. But in practice, these data sources are often not synchronized or lack the latest updates. A manuscript might have missing keywords, reader feedback might be delayed, or citation data could be outdated. Therefore, the question is, how should AI respond when faced with these discrepancies? First, confidence without clarity is dangerous. The AI needs to signal uncertainty upfront when recommending articles or flagging content for review, but lacks key metadata. That will mean tagging the output as “based on partial data” and prompting the user to share any additional or missing information. Second, the estimation should be acceptable, but only when the stakes are low and the assumptions are clearly communicated. For example, publishing a medical research article on COVID-19 carries far more risk than approving a mathematics book’s manuscript. In higher-stakes decisions, the AI should delay or escalate. It is better to flag the issue to a human editor than to perform a high-risk step incorrectly. Escalation should follow clear, predefined rules. For instance, if essential details like conflict-of-interest statements or author credentials are missing, the system must stop and inform the appropriate team right away. Lastly, to promote responsible behaviour, the AI must have built-in safeguards. These include 1) confidence level thresholds 2) fallback protocols 3) audit trails When data is missing and the AI system's confidence level drops below the set threshold, it should either pause and ask for additional information or the missing information. In such cases, it might rely on historical patterns or standard templates as a backup, but only if it clearly flags those assumptions. For every decision made under uncertain conditions, maintaining a detailed record of the context in the form of an audit trail is essential so that human reviewers can understand the reasoning and intervene whenever needed.

Consider the use of AI in content moderation within the publishing industry, where AI reviews manuscripts submitted by authors before publication. It automatically flags plagiarism, ethical issues, or copyright violations. Confidence is just one signal. AI should weigh confidence against consequence, context, and historical reliability. A well-designed publishing AI will not just ask “Am I confident?”—it will ask “Am I confident enough, given what is at stake?” To determine when the AI should act automatically versus when it should escalate to a human, the AI needs a decision framework built around several key factors, as follows: 1. Risk associated with a decision: High-risk decisions should always involve human review, regardless of AI’s confidence level. For example, publishing a medical research article on COVID-19 carries far more risk than approving a mathematics book’s manuscript. 2. Ability to give reasoning with sources: If the AI can clearly explain its reasoning with the reference sources, its decisions can be trusted. e.g., “This article matches 95% of this article, and this is the website link”. 3. Past feedback through human overrides: The system should learn from past human overrides. If humans have often reversed the AI’s decisions in a specific domain, that domain should be flagged for mandatory human review until the model's accuracy improves. 4. Novelty of articles: AI algorithms are best at handling familiar patterns taught to them. If the manuscript content includes emerging technologies or an unknown subject, the system should escalate to humans. 5. Matters that can have legal consequences: When it comes to sensitive topics like politics or religion, even if the AI has a high confidence level, the reputational and legal risks are substantial to automate these decisions, and they must be escalated to humans.

Consider an AI tool in the publishing world that helps decide which manuscripts get forwarded for peer review. It looks at things like how relevant the manuscript is, its originality, the quality of the writing, its potential for citations, and whether it fits the journal's focus. When the AI suggests something—like turning down a submission—the explanation it gives has to hit that sweet spot: clear enough to understand without burying people in details. At the very least, it must provide a brief summary of the call along with a confidence score. That way, editors get the gist right away and know how sure the AI is feeling. For everyday calls, that's usually plenty. However, rejecting a good submission could be a game-changer; the AI should provide the reasoning behind it, including the main reasons, such as "This scored low on novelty and had poor citation potential." And if someone is pushing back, it can pull up supporting information, maybe topic breakdowns or citation stats. To avoid overwhelming users, make the information unfold in stages. Start with a summary and confidence, then let editors delve deeper into the why and the proof if they wish. It will give users control over the level of detail they want. The level of explanation should also depend on the user's role. Editors need concise, actionable insights with optional depth. Authors may need an apparent reason for rejection, but not the full technical breakdown. Reviewers might benefit from understanding why a manuscript was flagged for review. In the end, figuring out clarity versus overload comes down to three things: 1. How much the decision matters, 2. What hat is the user wearing, and 3. How tangled the explanation gets. True transparency is not data overload—it is earning trust by providing information that is smart, relevant, and tailored to the user.

Taking an example from the Publishing sector. The publishers publish research papers based on defined criteria. The common conflict is Speed vs. Quality of Publishing. Process When a manuscript is submitted to a Journal, AI tools are used to perform an initial assessment — checking relevance, ethics, plagiarism, and even estimating novelty or impact. The objective is to determine the appropriate speed at which the manuscript should be sent to peer reviewers or rejected outright based on the assessment. Conflict - Speed: Authors expect fast decisions. Hence, Journals want to reduce turnaround time to increase author satisfaction. - Quality: A deeper AI analysis (e.g., semantic similarity, citations, novelty) takes longer but improves decision accuracy in false rejections or sending the manuscripts to the peer reviewers. AI algorithm 1. Based on the Journal's reputation - For high-impact journals, it should prioritize quality by using deep Natural Language Processing and citation network analysis to assess novelty and relevance. - For low-impact journals, it relies on lightweight checks and prioritize speed. 2. Analysis confidence level score of the output AI assigns a confidence level to its triage decision based on the data for which it is trained. - If the confidence level is high, the decision is taken automatically by AI - If the confidence level is low, it will escalate to the editorial staff. Role of human oversight - Editors need to define the weightage of speed and accuracy (e.g., 75% accuracy, 25% speed) based on journal type (high impact/medium impact/low impact). - They must periodically audit AI decisions to ensure calibration with editorial standards. This will lead to refinement in the AI algorithms. - For manuscripts in emerging technologies or interdisciplinary areas, human editors must train AI to avoid false rejections.