AI可以相信到什么程度？ / How Far Should You Trust AI?

AI’s most important capability is not answering everything for you. It is helping you distinguish answers that can support action from answers that require a stop and verification.

This bilingual directory brings together 20 question-led articles. Each begins with a direct answer and then provides evidence, a decision framework, a checklist and related questions. Instead of asking whether AI is good in the abstract, the series helps you decide how much work to delegate, how far to verify and who remains responsible.

Read the four chapters in sequence to build a complete framework, or open the question closest to the problem in front of you.

1. Answers and Verification

Decide which tasks AI should receive, then build a minimum reliable process for facts, citations and data.

  1. Which Tasks Should You Give to AI, and Which Should You Keep?
    Allocate AI by cost of error, verifiability and reversibility rather than by product brand.

  2. What Are Humans Still Responsible for After AI Gives an Answer?
    AI can generate an answer but cannot assume responsibility for goals, evidence, final decisions and remedy.

  3. Why Can AI Sound Confident and Still Be Completely Wrong?
    Fluency and confidence come from generation; they do not prove that facts were checked.

  4. How Can You Verify an AI Answer Without Redoing All the Work?
    Verify decision-changing claims first and sample by risk instead of repeating every step.

  5. How Can You Check Whether AI-Generated Papers, Data and Links Are Real?
    Citation existence, source authenticity, numerical accuracy and support for the adjacent claim are separate checks.

2. Judgement and Oversight

Understand model agreement, facts and inferences, human review and the limits of high-consequence automation.

  1. Does Agreement Between Two AI Systems Make an Answer More Reliable?
    Two models may share training, retrieval and reasoning errors; agreement is evidence, not independent proof.

  2. How Can You Separate Facts, Inferences and Advice in an AI Answer?
    Label facts, inferences and advice separately to reveal what needs checking, judgement or choice.

  3. When Should AI Not Make the Decision for You?
    General AI should not finally decide matters affecting rights, health, safety, livelihood or substantial property.

  4. Why Does Human Review Still Miss AI Errors?
    Nominal human review can fail through anchoring, missing expertise, hidden evidence and impossible workload.

  5. When Is the Cost of Error Too High for AI Automation?
    Safety impact, irreversibility, scale and late detection can each justify prohibiting autonomous execution.

3. Data, Documents and Records

Protect uploaded information, detect semantic loss in summaries and rewrites, and keep material work traceable.

  1. What Should You Check Before Uploading Personal or Company Files to AI?
    Before uploading, confirm authority, sensitive content, provider handling, minimum data and deletion.

  2. What Does AI Most Often Miss When Summarising Long Documents?
    Long-document summaries readily lose conditions, exceptions, numerical context, dissent, versions and cross-section links.

  3. Can AI Rewriting Quietly Change What You Mean?
    Rewriting can quietly shift certainty, actors, obligations, scope, negation and technical terminology.

  4. Why Shouldn’t AI-Generated Meeting Notes Be Treated as the Official Record?
    Recording, transcript, summary and official record are different evidence layers; approval creates official status.

  5. How Should You Preserve Sources, Versions and Edits in AI-Assisted Work?
    Preserve sources, input scope, model and prompt, raw output, human edits, verification and final approval.

4. Agents, Change, Value and Rules

Once AI moves from answering to acting, it needs stronger controls for permissions, change, value and accountability.

  1. What Must AI Confirm Before Sending Emails, Making Payments or Editing Files?
    Before action, an agent must confirm authority, unique target, final content, consequence and least privilege.

  2. Why Can the Same Prompt Produce Different Answers After a Model Update?
    The same visible prompt is not the same system condition; upgrades need regression, staged release and fallback.

  3. How Can You Tell Whether AI Saves Time or Adds Review Cost?
    Measure complete qualified tasks and include verification, rework, maintenance and tail failures in cost.

  4. Who Is Responsible When AI Causes Loss?
    Allocate responsibility by real control while giving affected people one direct route to correction and remedy.

  5. How Can You Write Practical AI Use Rules for Yourself or a Small Team?
    Put uses, data red lines, verification, action authority, records and incident response into a usable one-page rule.

How to use this series

  • Before a new AI task, start with articles 1, 10 and 11 to define task, loss and data boundaries.
  • Before connecting AI to real tools, concentrate on articles 15–20 for records, permissions, upgrades and accountability.
  • Models, products and policies change. For material decisions, also check the cited primary sources and current service terms.

See later updates: AI Judgment category archive