---
title: "Where China's Draft AI Anthropomorphic-Interaction Measures Need Work — A Scholar's Reform Map"
author: "DCC Editorial"
published: 2026-02-01T03:00:00.000Z
url: https://datacompliancechina.com/posts/anthropomorphic-ai-measures-reform-directions/
description: "Li Wenlong (科技利维坦) walks through the directions in which he would amend China's draft Interim Measures for the Administration of AI Anthropomorphic Interaction Services (人工智能拟人化互动服务管理办法) — the country's first dedicated rule on 'companion'-style AI. His critique is structural, not cosmetic: the core definition of '拟人化 (anthropomorphisation)' is too broad because it anchors on human-like expression rather than the real harm (relational dependency); the invented concept of '交互数据 (interaction data)' should be deleted and folded back into PIPL rather than blanket-prohibited; Chapter 2 mixes three incompatible duty types and should be split; the '1M registered / 100k MAU' security-assessment trigger is borrowed from other regimes and does not track real risk; and the training-data duties are horizontal obligations misplaced in a vertical rule. For overseas counsel building companion-AI or emotional-AI products for the China market: this is a map of where the draft is likely to move, and which duties fall on deployers versus base-model providers."
tags: ["ai-governance", "companion-ai", "anthropomorphic-ai", "pipl", "genai", "rulemaking", "academic-commentary"]
laws_cited: ["ai-anthropomorphic-interaction-measures", "pipl", "genai-services-interim-measures"]
domains: ["ai-governance", "personal-information"]
account: "keji-leviathan"
original_title: "AI拟人化互动服务管理暂行办法可以考虑修改的几个方向"
original_author: "李汶龙 (Li Wenlong)"
original_publication: "科技利维坦 WeChat Official Account"
original_url: "https://mp.weixin.qq.com/s/iJYQs1bRzGCLx_Zi43HmNQ"
source_language: "zh"
---
> *Editor's Note — DCC.*
>
> This brief summarises 《AI拟人化互动服务管理暂行办法可以考虑修改的
> 几个方向》by Li Wenlong (李汶龙) on the 科技利维坦 channel — a set of
> reform proposals he submitted as feedback on China's draft
> [Interim Measures for the Administration of AI Anthropomorphic
> Interaction Services](/laws/ai-anthropomorphic-interaction-measures/)
> (人工智能拟人化互动服务管理办法). The draft is China's first rule
> aimed specifically at "companion"-style AI — systems built to
> simulate human personality and hold emotional conversations. Li is
> broadly positive about the draft ("a quality piece of
> quasi-legislation"), so this is not a takedown; it is a structural
> critique from someone who thinks the rule matters and wants it to
> work. DCC is running it because the draft has no clean foreign
> analogue, and because Li's reform map is the most concrete public
> guide to where the text is likely to move. The takeaway for overseas
> counsel: if you are building emotional or companion AI for the China
> market, watch the definition clause — it decides whether your product
> is in scope at all — and watch the deployer-versus-model-provider
> split, because the draft currently risks putting training-data duties
> on parties who never trained a model.

## Why this rule is different

Most of China's data and AI rulebook — from the
[Personal Information Protection Law](/laws/pipl/) down through the
algorithm and [generative-AI](/laws/genai-services-interim-measures/)
measures — is built around *data governance*: collection and
processing, algorithmic transparency, fairness, accountability. The
anthropomorphic-interaction draft opens a different front that Li calls
*relationship governance* (关系治理). The thing it is trying to control
is not primarily a data flow; it is a *relationship* between a user and
a system designed to be felt as a companion. That shift is why Li
treats the rule as "concept-heavy" (很吃概念): the existing normative
vocabulary does not map cleanly onto it, and the draft therefore lives
or dies on how well it defines its new terms.

His proposals divide into six directions.

## 1. The core definition is too broad

The draft defines "anthropomorphisation (拟人化)" in Article 2 as
simulating "human personality traits, thinking patterns and
communication styles … and conducting emotional interaction with
humans." Li's objection: because of how the clause reads, the weight
falls on the first half — *simulating human traits* — rather than the
operative second half, *emotional interaction*. That over-widens scope,
sweeping in any tool with a human-like register.

He argues the Western framing of **"companion AI" (陪伴型AI)** gets
closer to the regulatory essence: the problem is not *simulation* —
talking like a human has been an internal driver of computing since
Turing — it is *over-dependence*. Anthropomorphisation, in the
psychological sense, is the user projecting intent, emotion and agency
onto a non-human system, which triggers over-trust, relational
attachment and behavioural compliance. The techniques that actually
manufacture that risk are specific design choices: persona-setting,
long-term memory, data retention, proactively initiating conversation,
emotional expression — all deployed to deepen trust and emotional
investment until it becomes *relational attachment* (关系依附).

Li's fix: anchor the definition on "emotional companionship as the
primary functional goal," using a denser term — he floats
"companion-style interaction service," "emotion-oriented interaction
service," "anthropomorphic companionship interaction service" — so the
rule binds to relational-dependency risk rather than to human-like
expression, and does not capture the large class of tools that merely
*sound* human without creating any binding risk.

## 2. Delete "interaction data" — don't blanket-prohibit it

The draft's second conceptual innovation is **"interaction data
(交互数据)"** — created mainly to capture the prompts, interaction
behaviour, persona settings and occasionally uploaded files that are
most valuable for model training and tuning (what Western privacy
policies loosely call "content"). Whether such data may be used for
model R&D is, Li notes, the single most contested question in
GDPR-style AI regulation right now.

He argues *against* the draft's instinct to prohibit its use
outright in this measure, for three reasons:

- **It isn't unique to companion AI.** Carving out a special prohibition
  here leaves every other kind of generative AI unaddressed, and is hard
  to reconcile with PIPL.
- **The global trend is the other way.** Across the dozen-plus
  jurisdictions Li tracked over the past year, the direction is toward
  *easing* — treating model R&D as a lower-tier risk than the
  consent-governed services (personalised advertising, facial
  recognition) — often via a **legitimate-interest** basis. China's PIPL
  pointedly *did not* adopt a legitimate-interest ground, which makes a
  blanket prohibition here even more dissonant with the parent statute.
- **It creates a "default-illegal" posture.** Absent legislation or
  precedent, prohibiting a still-contested processing purpose in a
  departmental measure manufactures presumptive illegality with shaky
  legal basis.

His recommendation: delete the "interaction data" concept and its
articles, or reduce it to a factual description ("chat logs and other
historical interaction information generated in the course of using the
service") and return the whole question to the existing PIPL
personal-information-processing rules.

More broadly, Li warns that the mechanism will "spin in the air" unless
the draft supplies *measurable, operable, verifiable* base concepts —
for relational structure, manipulation mechanisms, risk triggers and
liability boundaries — and that definitions belong at the **front** of
the instrument, not in an appendix, because they set scope.

## 3. Split Chapter 2 — it mixes three kinds of duty

Chapter 2 ("Service Norms") currently blends three rule types with
different risk logic and different compliance paths:

1. behavioural prohibitions and content red-lines;
2. capability-building and organisational duties (detection capability,
   emergency mechanisms, content moderation, staffing);
3. product and interaction-design duties (exit mechanisms, reminder
   mechanisms, a minors mode).

Mixing them — and interleaving governance *tools* (security assessment,
early warning, sandbox) with substantive *duties* — makes the chapter
hard to read and hard to action, and will blur enforcement. Li proposes
splitting it into (i) product/interaction-design duties; (ii)
vulnerable-group protection (minors, the elderly, psychologically
vulnerable users, plus intervention mechanisms); and (iii) risk
assessment, monitoring and correction — and adding a new chapter on
**platform responsibility and ecosystem governance** that pins down what
app-distribution platforms owe in listing review, misjudgement
correction and collaborative governance.

## 4. Make "tiered and classified regulation" real

The general provisions promise "inclusive and prudent, tiered and
classified regulation (包容审慎、分类分级监管)," but the structure never
delivers a risk grading or differentiated duties. Li wants that
operationalised so the burden is proportionate:

- **Reserve the heavy duties for the real risk class.** Interaction-design
  and risk-monitoring duties should fall mainly on services whose primary
  function is emotional companionship *with* a relational-binding
  mechanism; pure-tool, customer-service and OA scenarios should get
  lighter general duties (basic transparency, an exit right) plus
  case-by-case triggers. California and New York offer referenceable
  paths here.
- **Don't put model duties on deployers.** Many companion services do not
  train a base model — they call a third-party model or API and have
  limited control over training data or base-model safety. Imposing
  training-data-governance duties or model-training prohibitions on those
  *deployers* is unreasonable and unenforceable, and invites
  form-over-substance compliance and liability mismatch. Deployers should
  owe controllable product-design, prompting, exit and complaint duties;
  base-model and key-capability providers should be reached through
  collaborative liability and capability-boundary disclosure.
- **Build in elasticity.** Over-rigid rules become "structural
  illegality." His example: Article 13's flat ban on "simulating an
  elderly user's relatives or specific related persons" is aimed at
  protecting older users, but anchoring the duty on *simulation* would
  wrongly hit fictional role-play, literary and dramatic characters, and
  user-set fictional identities; what should be prohibited is
  impersonating a *real, specific* natural person to induce trust,
  transfers or control. Regulatory sandboxes, much-discussed, still lack
  any stable organisational mechanism — and a sandbox without real ceded
  space, concrete risk relief and safe-harbour is just a slogan.

## 5. Handle "path dependency" carefully

The draft leans on tools migrated from earlier regimes. Two cautions:

- **The borrowed toolbox may not fit.** The security-assessment trigger
  — registered users ≥ 1 million / monthly actives ≥ 100,000 — is lifted
  from other contexts and does not track *this* risk: a companion service
  can be very niche yet very high-risk. Li proposes a tailored
  **"significant operational change (显著运行变化)"** concept with
  qualitative and quantitative indicators — e.g., within three
  consecutive calendar months, registered users / MAU / average daily
  use up more than 50% year-on-year or quarter-on-quarter; a marked rise
  in the share of single users averaging over four hours of continuous
  daily use; or a marked rise in the share triggering manual
  intervention, risk warnings or complaints.
- **Restatement dilutes focus.** The general-duty enumerations (e.g.,
  Articles 8–9) largely repeat obligations that already exist and do not
  create new ones; piling them in makes it harder for companies to
  identify what is genuinely new and harder for regulators to enforce
  precisely. Better to connect horizontal requirements by reference
  ("comply with the relevant provisions") and reserve the article space
  for this measure's distinctive relational-risk controls.

## 6. Profiling and emotion inference cut against PIPL

Li's sharpest data-protection warning: protecting vulnerable users
through precise identification and intervention requires *more*
profiling — and a well-intentioned rule can become a pretext for
exactly the high-risk processing (continuous emotion inference,
psychological profiling, automated decision-making) that PIPL's
**data-minimisation principle (最小必要原则)** and **sensitive-personal-
information rules** are meant to restrain. His position: intervention
duties should bite only in genuinely high-risk situations — a user
clearly signalling suicide, self-harm or serious harm, or explicitly
requesting mental-health support — and the rule should not push
providers into *default* continuous emotional monitoring.

## A coda on legal coherence

Li's own biggest concern — and, he notes wryly, the one practice cares
least about — is system coherence. The draft contains at least two rule
sets: one genuinely novel (identifying, intervening in and preventing
anthropomorphism-driven dependency), and one of general data/AI duties,
part of which merely restates existing law and part of which maps onto
nothing — de facto quasi-legislation. Articles 10 and 15 on
training-data governance, for instance, are *horizontal* obligations
that belong in the generative-AI framework or a dedicated training-data
norm, not embedded in a vertical companion-AI rule (especially where the
deployer never trained the model). And the security-assessment articles
(21–22) have an unclear pedigree — some trace them to the "new
technology / new application" assessment, some to algorithm-governance
guidance — which, Li argues, is exactly the kind of ambiguity a
principled, PIPL-anchored interpretation should resolve.

## Why overseas counsel should care

- **Scope turns on one clause.** Whether your product is regulated as
  "anthropomorphic interaction" depends entirely on the Article 2
  definition. If Li's narrowing prevails, plain assistants and tools that
  merely sound human fall out; companion products built for emotional
  attachment stay firmly in.
- **Know which layer you are.** The deployer-versus-model-provider split
  is the live drafting question. A company that only fine-tunes or calls
  an API should be pressing — as Li does — for product-design duties
  rather than training-data duties.
- **The PIPL baseline still governs.** Where the measure is silent or
  over-reaches, Li's repeated answer is to fall back to PIPL —
  minimisation, sensitive-PI rules, and the absence of a legitimate-
  interest ground. That is the stable layer under a moving draft.

This brief also connects to DCC's note on
[system prompts as a regulatory instrument](/posts/system-prompts-as-regulatory-instrument/),
Li's other piece on where AI rulemaking is hooking into model internals.

## DCC sources

- Original: 李汶龙 (Li Wenlong), 《AI拟人化互动服务管理暂行办法可以
  考虑修改的几个方向》, 科技利维坦 WeChat Official Account
  ([source](https://mp.weixin.qq.com/s/iJYQs1bRzGCLx_Zi43HmNQ)).
- Rule under discussion: [Interim Measures for the Administration of AI
  Anthropomorphic Interaction Services](/laws/ai-anthropomorphic-interaction-measures/)
  (draft).
- Related Chinese instruments referenced: the
  [Personal Information Protection Law](/laws/pipl/) (minimisation,
  sensitive-PI rules, absence of a legitimate-interest ground) and the
  [Generative AI Services Interim Measures](/laws/genai-services-interim-measures/).

> This is an editorial summary, not a translation of Li Wenlong's
> piece. The reform proposals and framings are his; any simplification,
> error of emphasis, or operational extrapolation is DCC's. **Not legal
> advice.**