Jooyeol Kim

CIEA-M1

Customer-Facing Bottleneck Module

Status: Public module v0.4.2
Parent: CIEA Core
Type: Domain-specific audit module

CIEA-M1 applies the CIEA audit engine to customer-facing and user-facing interfaces.


Core question

When an organization claims efficiency, automation, service optimization, or cost reduction at the interface, did it actually improve the core, or did it move waiting, frustration, distrust, verification burden, and emotional labor onto customers and frontline workers?

Shorter:

Did the bottleneck disappear, or was it pushed into someone’s time, anger, body, or verification burden?


Target interfaces


Main mechanisms

Mechanism Short meaning
Bottleneck Externalization Organizational processing cost becomes customer time or frontline emotional labor.
Anger Routing Failed layers concentrate frustration at the final human interface.
Organizational Analgesia Support channels narrow, making the organization quieter but less sensitive.
Last-Bottleneck Memory A final bottleneck can overwrite an otherwise acceptable experience.
Risk Classification Burden Users must decide whether an answer is low-risk information or consequential guidance.
Reliance–Warranty Split The interface creates reliance while the responsibility layer weakens or withdraws warranty.
Output QA Displacement The system produces answers faster while moving verification and correction work to users or workers.

New v0.4.2 mechanism: Reliance–Warranty Split

This mechanism is especially important for AI customer support, public AI counseling, financial-service automation, platform appeals, and other interfaces where a generated answer may look actionable.

Structure:

interface looks helpful
→ answer appears actionable
→ user relies or prepares to act
→ terms / disclaimer / responsibility layer says output is not binding or must be verified
→ user inherits risk classification and verification burden
→ human or institutional layer receives escalation later

Diagnostic sentence:

The interface creates trust; the responsibility layer withdraws warranty.

This does not prove deception by itself.

It means the interface must be audited against:


M1 counter-surfaces for AI support claims

Claimed improvement Required counter-surfaces
chatbot use increased issue completion rate, user comprehension, escalation need
answer speed improved accuracy, reliance risk, verification burden
call volume decreased abandonment, quiet exit, unresolved demand
human calls reduced remaining-case complexity, frontline emotional labor
complaint volume decreased complaint access difficulty, external complaints
self-service increased retry burden, vulnerable-user access, failure rate
staffing optimized worker stress, complex-case concentration, burnout
customer satisfaction improved recontact, churn, trust after failure

M1 audit move

interface claim
→ real service core
→ decision table
→ excluded customer/user burden
→ excluded frontline burden
→ reliance-warranty split check
→ counter-surfaces
→ displacement judgment

Public sample

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