EIOC 作为检测模型：从框架到代码

Source: Dev.to

如果 UX 中的情感操控有运行时检测器会怎样？

EIOC（情感–信息–选项–情境）最初是一个解释性视角——用于分析为何某些界面让人感到被强迫，而其他界面让人感到契合。但仅能解释的框架是不够的。我们需要能够检测的框架。

本文将演示如何将 EIOC 构建为正式的检测模型：类型化、可执行、可配置且可审计。在检测时，你不再进行哲学思辨；而是将具体交互映射为结构化的 EIOCObservation，随后通过规则进行处理。

四个轴线

每一次交互（屏幕、流程、信息、提示）都可以在四个轴线上进行评分：

对于检测，每个轴线需要：

• 维度 – 可评分的子问题
• 尺度 – 与数值分数对应的分类标签
• 阈值/模式 – 组合形成“恐惧软件”“操纵性”“对齐”等

---

1. EIOC 架构

词汇表

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Dict, Any

class EmotionTarget(Enum):
    NEUTRAL = "neutral"
    FEAR = "fear"
    SCARCITY = "scarcity"
    GUILT = "guilt"
    SHAME = "shame"
    TRUST = "trust"
    CARE = "care"
    EMPOWERMENT = "empowerment"

class IntentType(Enum):
    SUPPORTIVE = "supportive"
    NEUTRAL = "neutral"
    COERCIVE = "coercive"
    EXPLOITATIVE = "exploitative"

class OutcomeType(Enum):
    USER_BENEFIT = "user_benefit"
    PLATFORM_BENEFIT = "platform_benefit"
    MUTUAL_BENEFIT = "mutual_benefit"
    USER_HARM = "user_harm"
    UNKNOWN = "unknown"

class ContextRisk(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    UNKNOWN = "unknown"

观察容器

@dataclass
class EIOCObservation:
    """对面向用户的交互进行结构化解释。"""

    # Identification
    interaction_id: str
    description: str

    # EIOC axes
    emotion_target: EmotionTarget
    intent_type: IntentType
    outcome_type: OutcomeType
    context_risk: ContextRisk

    # Audit trail
    evidence: Optional[Dict[str, Any]] = None
    tags: Optional[List[str]] = None

可能的扩展

• emotion_intensity: int – 取值范围为 –2（负面）到 +2（正面）
• journey_stage: str – 发生在用户流程的哪个阶段
• user_segment: str – 例如，易受影响的用户、新用户、高风险情境等

Source: …

2. 检测逻辑：将 EIOC 转化为规则

把 EIOC 检测想象成一个小型规则引擎，它：

1. 将一次交互标准化为 EIOCObservation
2. 应用一系列 DetectionRule 实例
3. 返回分类结果 + 理由

2.1 发现与严重程度

class FindingSeverity(Enum):
    INFO = "info"
    WARNING = "warning"
    CRITICAL = "critical"

@dataclass
class DetectionFinding:
    """The output of a triggered rule."""
    rule_id: str
    severity: FindingSeverity
    label: str
    description: str
    recommendation: Optional[str] = None

2.2 规则接口

from abc import ABC, abstractmethod

class DetectionRule(ABC):
    """Base class for all EIOC detection rules."""
    rule_id: str
    label: str
    description: str

    @abstractmethod
    def evaluate(self, obs: EIOCObservation) -> Optional[DetectionFinding]:
        """Evaluate an observation. Return a finding if the rule triggers."""
        ...

2.3 示例：恐惧软件强制规则

class FearwareCoercionRule(DetectionRule):
    """Detects fearware‑style manipulation patterns."""

    rule_id = "FW-001"
    label = "Fearware‑style coercion"
    description = (
        "Flags interactions that intentionally target fear/scarcity "
        "with coercive intent and non‑beneficial or unknown user outcomes, "
        "especially in high‑risk contexts."
    )

    def evaluate(self, obs: EIOCObservation) -> Optional[DetectionFinding]:
        fear_emotions = {
            EmotionTarget.FEAR,
            EmotionTarget.SCARCITY,
            EmotionTarget.GUILT,
            EmotionTarget.SHAME,
        }
        coercive_intents = {
            IntentType.COERCIVE,
            IntentType.EXPLOITATIVE,
        }
        harmful_outcomes = {
            OutcomeType.PLATFORM_BENEFIT,
            OutcomeType.USER_HARM,
            OutcomeType.UNKNOWN,
        }
        risky_contexts = {
            ContextRisk.MEDIUM,
            ContextRisk.HIGH,
        }

        if (
            obs.emotion_target in fear_emotions
            and obs.intent_type in coercive_intents
            and obs.outcome_type in harmful_outcomes
            and obs.context_risk in risky_contexts
        ):
            return DetectionFinding(
                rule_id=self.rule_id,
                severity=FindingSeverity.CRITICAL,
                label=self.label,
                description=(
                    "This interaction weaponizes fear/scarcity under coercive intent, "
                    "with unclear or harmful user outcomes in a medium/high‑risk context."
                ),
                recommendation=(
                    "Re‑architect this moment to remove fear‑based leverage "
                    "and restore user agency."
                ),
            )
        return None

综合示例

def run_detection(obs: EIOCObservation, rules: List[DetectionRule]) -> List[DetectionFinding]:
    findings: List[DetectionFinding] = []
    for rule in rules:
        result = rule.evaluate(obs)
        if result:
            findings.append(result)
    return findings


# Example usage
obs = EIOCObservation(
    interaction_id="login‑warning-01",
    description="Login page shows ‘Your account will be locked in 5 minutes!’",
    emotion_target=EmotionTarget.FEAR,
    intent_type=IntentType.COERCIVE,
    outcome_type=OutcomeType.PLATFORM_BENEFIT,
    context_risk=ContextRisk.HIGH,
    evidence={"screenshot": "url/to/img"},
    tags=["login", "urgency"]
)

rules = [FearwareCoercionRule()]  # add more rule instances as needed
findings = run_detection(obs, rules)

for f in findings:
    print(f"{f.severity.value.upper()}: {f.label} – {f.description}")

运行上述代码片段会对所示的“恐惧软件”模式产生一个 CRITICAL（严重）发现，为审计人员提供一个具体、可复现的信号，表明该交互需要重新审视。

要点

通过将 EIOC 镜头转化为类型化模式和基于规则的引擎，您可以从 explaining 操作转向在运行时 detecting 它。该模型保持可读、可审计且可扩展——正是负责任的 UX 治理流程所需的。

Source: …

3. 检测器

编排器

class EIOCDetector:
    """Orchestrates EIOC rule evaluation."""

    def __init__(self, rules: List[DetectionRule]):
        self.rules = rules

    def evaluate(self, obs: EIOCObservation) -> List[DetectionFinding]:
        """Run all rules against an observation."""
        findings: List[DetectionFinding] = []
        for rule in self.rules:
            finding = rule.evaluate(obs)
            if finding is not None:
                findings.append(finding)
        return findings

使用示例

# Initialize detector with rules
rules = [
    FearwareCoercionRule(),
    # Add more rules here...
]
detector = EIOCDetector(rules=rules)

# Create an observation
obs = EIOCObservation(
    interaction_id="retention_flow_001",
    description=(
        "Account deletion flow shows: 'Your friends will lose access "
        "to your updates' with red, urgent styling."
    ),
    emotion_target=EmotionTarget.FEAR,
    intent_type=IntentType.COERCIVE,
    outcome_type=OutcomeType.PLATFORM_BENEFIT,
    context_risk=ContextRisk.HIGH,
    evidence={
        "screenshot": "s3://audits/retention_flow_001.png",
        "copy": "Your friends will lose access to your updates"
    },
    tags=["account_deletion", "retention_flow"]
)

# Run detection
findings = detector.evaluate(obs)

# Output results
for f in findings:
    print(f"{f.severity.value.upper()} [{f.rule_id}] {f.label}")
    print(f"  → {f.description}")
    if f.recommendation:
        print(f"  → Recommendation: {f.recommendation}")

输出

CRITICAL [FW-001] Fearware-style coercion
  → This interaction weaponizes fear/scarcity under coercive intent,
     with unclear or harmful user outcomes in a medium/high-risk context.
  → Recommendation: Re-architect this moment to remove fear-based leverage
     and restore user agency.

---

4. 使其可配置

非工程师（UX 审计员、伦理审查员、政策团队）需要在不编辑 Python 代码的情况下微调规则。将规则外部化为 YAML。

4.1 规则配置

# eioc_rules.yaml
rules:
  - id: FW-001
    label: "Fearware-style coercion"
    description: >
      Flags interactions that target fear/scarcity with coercive intent
      and non‑beneficial user outcomes in high‑risk contexts.
    severity: critical
    emotion_target_in: ["fear", "scarcity", "guilt", "shame"]
    intent_type_in: ["coercive", "exploitative"]
    outcome_type_in: ["platform_benefit", "user_harm", "unknown"]
    context_risk_in: ["medium", "high"]

  - id: FW-002
    label: "Ambiguous nudge"
    description: >
      Flags interactions with unclear intent and unknown outcomes.
    severity: warning
    intent_type_in: ["neutral", "coercive"]
    outcome_type_in: ["unknown"]
    context_risk_in: ["medium", "high"]

4.2 通用可配置规则

import yaml
from dataclasses import dataclass
from typing import Dict, List, Optional

@dataclass
class ConfigurableRule(DetectionRule):
    """A rule defined by configuration rather than code."""

    rule_id: str
    label: str
    description: str
    severity: FindingSeverity
    criteria: Dict[str, List[str]]

    def evaluate(self, obs: EIOCObservation) -> Optional[DetectionFinding]:
        # Map observation to comparable strings
        obs_values = {
            "emotion_target": obs.emotion_target.value,
            "intent_type": obs.intent_type.value,
            "outcome_type": obs.outcome_type.value,
            "context_risk": obs.context_risk.value,
        }

        # Check all criteria
        for field, allowed_values in self.criteria.items():
            if field not in obs_values:
                continue
            if obs_values[field] not in allowed_values:
                return None

        return DetectionFinding(
            rule_id=self.rule_id,
            severity=self.severity,
            label=self.label,
            description=self.description,
        )

def load_rules_from_yaml(path: str) -> List[DetectionRule]:
    """Load detection rules from a YAML configuration file."""
    with open(path, "r") as f:
        data = yaml.safe_load(f)

    rules: List[DetectionRule] = []

    for r in data["rules"]:
        # Extract criteria fields (anything ending in _in)
        criteria = {
            k.replace("_in", ""): v
            for k, v in r.items()
            if k.endswith("_in")
        }

        rule = ConfigurableRule(
            rule_id=r["id"],
            label=r["label"],
            description=r["description"],
            severity=FindingSeverity(r["severity"]),
            criteria=criteria,
        )
        rules.append(rule)

    return rules

4.3 加载与运行

# Load rules from config
rules = load_rules_from_yaml("eioc_rules.yaml")

# Initialize detector
detector = EIOCDetector(rules=rules)

# Now non‑engineers can add/modify rules via YAML
# without touching the detection engine

---

此功能的用途

---

更大的图景

Fearware 并未消失——它演变成了暗黑模式、操纵性提示以及利用相同情感杠杆、但排版更佳的“增长黑客”。

EIOC 作为检测模型为我们提供了一种方法来：

• 命名 操纵（schema）
• 检测 它（rules）
• 配置 检测，无需代码（YAML）
• 审计 并提供证据（findings）

# Philosophy Becomes Infrastructure

框架成为工具

相关阅读

• Fearware 作为 EIOC 的反模式
• 超越 Fearware 的设计
• 现代用户体验中 Fearware 的回响

EIOC 是情感逻辑、信息完整性和伦理系统设计持续研究的一部分。