具身智能应用的隐私政策友好度评价研究

doi:10.13998/j.cnki.issn1002-1248.25-0738

Abstract

Abstract:

[Purpose/Significance] The rapid development of embodied intelligence has fundamentally transformed the landscape of privacy protection. Unlike traditional digital services, embodied intelligent devices deeply embed themselves into users' physical environments, continuously collecting multimodal data through sensors and autonomously executing actions. This physical embodiment, social interaction, and action autonomy fundamentally reshape privacy risk boundaries, rendering existing privacy policy evaluation frameworks inadequate. While privacy policy evaluation has been extensively studied for websites and mobile applications, research specifically addressing the unique challenges of embodied intelligence remains scarce. This study aims to fill this gap by developing a user-friendliness evaluation framework tailored for embodied intelligence privacy policies. The theoretical innovation lies in extending the friendliness concept from traditional digital spaces to physical interaction scenarios, systematically incorporating the technical characteristics of embodied intelligence into the evaluation system. The practical significance is to reveal the current state of privacy policy design in the embodied intelligence industry, identify compliance gaps and interaction design deficiencies, and provide actionable recommendations for policy optimization and user rights protection. [Method/Process] This study constructed a comprehensive evaluation system grounded in user experience theory and legal requirements. Theoretically, we introduced Peter Morville's user experience honeycomb model as the foundational framework, which comprises seven interrelated dimensions: useful, usable, desirable, findable, accessible, credible, and valuable. This model provides systematic coverage of the entire user journey from initial perception to trust establishment. Legally, the indicator system refers to the personal information protection law and information security technology - personal information security specification. Methodologically, we innovatively adopt the matter-element extension model as the core evaluation approach. Compared to traditional methods like analytic hierarchy process and fuzzy comprehensive evaluation, this model offers unique advantages in handling multi-indicator, multi-grade, and subjective perception data, providing precise association degrees between each indicator and evaluation grades. Empirically, we collected 336 valid questionnaires from users with experience using embodied intelligence products, consulting five experts (three in user information behavior and two in human-computer interaction) to establish evaluation grade standards. Six mainstream embodied intelligence applications across different product categories were selected as evaluation objects, including DJI (autonomous drones), Ecovacs (sweeping robots), and NIO (in-vehicle intelligent systems). [Results/Conclusions] The evaluation results reveal three key findings. First, the overall user-friendliness of current embodied intelligence privacy policies ranges from moderate to good, exhibiting a middle-clustered distribution with no applications achieving excellent ratings, indicating the industry is transitioning from basic compliance to experience optimization. Second, regarding core obligation indicators, basic compliance is generally achieved, yet significant deficiencies exist in deepening user rights (e.g., inclusive protection for special groups) and transparent risk communication (e.g., specific disclosure of physical security risks). Notably, Yushu Technology and Qianglang Smart received poor ratings for user consent respect, as their interfaces lack substantive refusal options. Third, interaction friendliness indicators show uneven performance: DJI and Ecovacs excel in built-in functionality (achieving excellent ratings by integrating policy reading and settings within the app), while most applications underperform in visual design, language simplicity, and navigation convenience. This study validates the applicability of the matter-element extension model in privacy policy evaluation and provides actionable recommendations: optimize consent mechanisms to ensure clear refusal options, enhance visual hierarchy and layout design to reduce cognitive load, and specifically address unique embodied intelligence risks such as physical security and environmental monitoring. Future research should expand sample sizes, incorporate multimodal sensing data, and explore dynamic real-time evaluation mechanisms.

Key words: embodied intelligence, privacy policy, friendliness, matter-element extension model

CLC Number:

G250

YAN Wei, LIU Zichen, FENG Yangliu, WEI Lai. Evaluation of Privacy Policy Friendliness of Embodied Intelligence Applications[J].Journal of library and information science in agriculture, 2026, (): 1-16.

Figures/Tables 10

Table 1

Evaluation indicator system for user-friendliness of embodied intelligence application privacy policies"

一级指标	二级指标	解释说明	参考依据
A1 有用性	A11 信息完整性	该产品的隐私政策是否完整地告知了用户个人信息，尤其是位置、环境图像、语音、行为模式等具身性特有数据的收集、使用、存储、共享、删除等全流程处理活动？	《中华人民共和国个人信息保护法》《新一代人工智能伦理规范》
	A12 目的明确性	该产品的隐私政策所收集的每项个人信息是否均有明确、合理的用途，且与具身智能设备的核心功能如导航、规避、个性化服务等直接相关，无过度索权？	《中华人民共和国个人信息保护法》《新一代人工智能伦理规范》
	A13 保护措施具体性	该产品的隐私政策是否明确阐述了为保障用户隐私权益所采取的具体技术与管理措施，特别是针对设备本地/边缘计算、实时数据传输等场景？	《规范》《新一代人工智能治理原则》
	A14 责任与反馈机制	该产品的隐私政策是否明确告知了个人信息保护的责任主体，并提供有效的投诉与举报渠道且承诺响应时限？	《网络数据安全管理条例》
	A15 包容性与特殊保护	该产品的隐私政策是否制定并告知了针对未成年人、老年人、残障人士等特殊群体使用具身智能设备的差异化个人信息保护措施？	《中华人民共和国个人信息保护法》《新一代人工智能伦理规范》
A2 可用性	A21 操作流程简便性	用户完成隐私政策同意、授权管理、权利行使等操作的流程是否步骤精简、路径直观？	《规范》《新一代人工智能伦理规范》
	A22 功能入口明确性	该产品的隐私政策中用户访问、更正、删除个人信息及进行隐私设置的入口是否清晰标示、易于发现？	《规范》
A3 合意性	A31 语言通俗性	该产品隐私政策的文本语言是否通俗易懂、符合通用语言习惯，避免使用有歧义或晦涩难懂的法律术语？	《规范》
	A32 视觉舒适性	该产品隐私政策文本的版式设计是否通过清晰的层次结构、舒适的行距排版，以及对关键条款的加粗、高亮等视觉设计提升阅读体验？	马家伟等^[24]
	A33 用户意愿尊重性	用户是否可以自主勾选同意隐私政策，而不是强制？撤回同意是否像勾选同意时一样方便？	《中华人民共和国个人信息保护法》《新一代人工智能伦理规范》
	A34 语气平等性	该产品隐私政策的文本是否采用中立、客观、协商的口吻，而非强制、命令或带有威胁性的语气？	朱候等^[26]
A4 可寻性	A41 入口显著性	该产品隐私政策的链接或入口是否置于设备主页、应用安装页、登录页等醒目位置？	《规范》《网络数据安全管理条例》
	A42 提示及时性	是否在用户首次打开应用、注册登录、政策更新等关键时机主动、显著地提示用户？	《规范》《网络数据安全管理条例》
	A43 导航便捷性	该产品隐私政策的文本内部是否提供目录、索引或章节锚点链接，帮助用户快速定位和跳转到所关心的具体条款？	张敏等^[27]
A5 可达性	A51 访问无障碍	该产品的隐私政策是否无需注册或登录即可查阅，并兼容读屏软件等辅助技术，满足无障碍需求？	《规范》
	A52 功能内置程度	该产品隐私政策的阅读、同意及设置功能是否均在应用内部完成，无需跳转至外部浏览器？	姚胜译等^[9]
A6 可信性	A61 信息准确性	该产品隐私政策的内容是否真实、准确，与设备实际行为是否一致？	《规范》
	A62 风险告知透明性	该产品的隐私政策是否坦诚告知用户使用具身智能设备可能存在的个人信息安全风险，如数据被意外录制、设备失控等安全风险，不使用模糊或误导性话术？	《中华人民共和国个人信息保护法》《新一代人工智能伦理规范》
A7 价值性	A71 用户赋能与获得感	该产品隐私政策文本与配套功能，是否能让用户清晰地感知到对个人信息尤其是设备生成的行为与环境数据，拥有充分的知情权和控制权？	姚山季等^[28] 《新一代人工智能伦理规范》
	A72 品牌信任构建	该产品隐私政策的内容是否体现了对社会责任与伦理规范的承诺，是否能够构建和巩固用户对品牌的深度信任与情感认同？	姚山季等^[28] 《新一代人工智能治理原则》

Table 1

Table 2

Table 3

Matter-element model values for user-friendliness of DJI privacy policy"

评价指标及权重				评价等级					节域	实际值
一级指标	权重	二级指标	权重	N1（差）	N2（较差）	N3（一般）	N4（良好）	N5（优秀）	$N i p$	$N i$
A1	0.295	A11	0.041	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.737
		A12	0.049	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.709
		A13	0.060	[0.000,0.200)	[0.200,0.400)	[0.400,0.600)	[0.600,0.800)	[0.800,1.000]	[0,1]	0.661
		A14	0.072	[0.000,0.300)	[0.300,0.500)	[0.500,0.600)	[0.600,0.800)	[0.800,1.000]	[0,1]	0.693
		A15	0.073	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.622
A2	0.087	A21	0.038	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.852
		A22	0.049	[0.000,0.350)	[0.350,0.500)	[0.500,0.650)	[0.650,0.800)	[0.800,1.000]	[0,1]	0.674
A3	0.198	A31	0.039	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.704
		A32	0.048	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.678
		A33	0.056	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.789
		A34	0.055	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.664
A4	0.143	A41	0.043	[0.000,0.300)	[0.300,0.500)	[0.500,0.650)	[0.650,0.800)	[0.800,1.000]	[0,1]	0.789
		A42	0.052	[0.000,0.300)	[0.300,0.500)	[0.500,0.650)	[0.650,0.800)	[0.800,1.000]	[0,1]	0.769
		A43	0.048	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.679
A5	0.079	A51	0.038	[0.000,0.300)	[0.300,0.500)	[0.500,0.650)	[0.650,0.800)	[0.800,1.000]	[0,1]	0.798
		A52	0.041	[0.000,0.300)	[0.300,0.500)	[0.500,0.650)	[0.650,0.800)	[0.800,1.000]	[0,1]	0.811
A6	0.101	A61	0.044	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.728
		A62	0.057	[0.000,0.250)	[0.250,0.500)	[0.500,0.750)	[0.750,0.900)	[0.900,1.000]	[0,1]	0.682
A7	0.097	A71	0.058	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.742
		A72	0.039	[0.000,0.400)	[0.400,0.550)	[0.550,0.700)	[0.700,0.850)	[0.850,1.000]	[0,1]	0.704

Table 3

Table 4

Correlation degrees between secondary indicators and evaluation grades for DJI privacy policy"

二级指标	$K 1 v i k$	$K 2 v i k$	$K 3 v i k$	$K 4 v i k$	$K 5 v i k$	关联度最大值	评价等级
A11	-0.650	-0.475	0.051	-0.046	-0.382	0.051	N3
A12	-0.613	-0.419	0.162	-0.122	-0.396	0.162	N3
A13	-0.576	-0.435	-0.152	0.304	-0.291	0.304	N4
A14	-0.562	-0.387	-0.124	0.289	-0.258	0.289	N4
A15	-0.495	-0.243	0.486	-0.253	-0.424	0.486	N3
A21	-0.753	-0.671	-0.506	-0.012	0.012	0.012	N5
A22	-0.534	-0.347	-0.067	0.157	-0.279	0.157	N4
A31	-0.506	-0.342	-0.012	0.025	-0.331	0.025	N4
A32	-0.464	-0.285	0.145	-0.063	-0.348	0.145	N3
A33	-0.719	-0.579	-0.157	0.262	-0.344	0.262	N4
A34	-0.441	-0.254	0.238	-0.096	-0.356	0.238	N3
A41	-0.699	-0.579	-0.398	0.071	-0.048	0.071	N4
A42	-0.669	-0.537	-0.339	0.210	-0.120	0.210	N4
A43	-0.466	-0.288	0.137	-0.060	-0.347	0.137	N3
A51	-0.711	-0.595	-0.421	0.017	-0.012	0.017	N4
A52	-0.730	-0.623	-0.461	-0.057	0.057	0.057	N5
A61	-0.637	-0.456	0.088	-0.075	-0.387	0.088	N3
A62	-0.656	-0.484	0.032	-0.030	-0.380	0.032	N3
A71	-0.506	-0.342	-0.012	0.025	-0.331	0.025	N4
A72	-0.470	-0.293	0.122	-0.054	-0.346	0.122	N3

Table 4

Table 5

Correlation degrees between primary indicators and evaluation grades for DJI privacy policy"

一级指标	$K 1 N 1$	$K 2 N 2$	$K 3 N 3$	$K 4 N 4$	$K 5 N 5$	关联度最大值	评价等级
A1	-0.569	-0.379	0.094	0.042	-0.346	0.094	N3
A2	-0.630	-0.489	-0.259	0.083	-0.152	0.083	N4
A3	-0.538	-0.371	0.054	0.038	-0.346	0.054	N3
A4	-0.610	-0.466	-0.197	0.078	-0.174	0.078	N4
A5	-0.721	-0.609	-0.442	-0.021	0.024	0.024	N5
A6	-0.648	-0.472	0.056	-0.050	-0.383	0.056	N3
A7	-0.491	-0.322	0.042	-0.007	-0.337	0.042	N3

Table 5

Table 6

Comprehensive correlation degrees and evaluation grade for DJI privacy policy"

总目标	$K 1 (N)$	$K 2 (N)$	$K 3 (N)$	$K 4 (N)$	$K 5 (N)$	关联度最大值	综合评价等级
A	-0.587	-0.421	-0.038	0.031	-0.278	0.031	N4

Table 6

Table 7

Standardized comprehensive correlation degrees and grade variable characteristic values for DJI privacy policy"

评价对象	$K ˜ 1 N$	$K ˜ 2 N$	$K ˜ 3 N$	$K ˜ 4 N$	$K ˜ 5 N$	j^**
A	0.000	0.268	0.889	1.000	0.499	3.652

Table 7

Table 8

Table 9

Table 10

References 30

[1]	中国电子学会. 人工智能学科发展报告-2022-2023[M]. 北京: 中国科学技术出版社, 2024.
[2]	国务院国有资产监督管理委员会, 工业和信息化部. 关于印发《关于规范中央企业采购管理工作的指导意见》的通知[EB/OL]. [2025-12-08].
[3]	国务院. 政府工作报告_中国政府网[EB/OL]. [2025-12-08].
[4]	Rutter J. Understanding the decision to click on the internet privacy policy[D]. California: College of Business Administration of Touro University International, 2007.
[5]	徐文渊, 冀晓宇, 闫琛, 等. 具身智能安全治理[J]. 中国科学院院刊, 2025, 40(3): 429-439.
	Xu Wenyuan, Ji Xiaoyu, Yan Chen, et al. Embodied artificial intelligence security and governance[J]. Bulletin of Chinese Academy of Sciences, 2025, 40(3): 429-439.
[6]	王苑. 具身智能体的隐私风险及法律应对——以“人形机器人”为例的展开[J]. 东方法学, 2024(3): 64-76.
	Wang Yuan. Privacy risks and legal responses to embodied intelligent agents[J]. Oriental Law, 2024(3): 64-76.
[7]	Putra R V W, Marchisio A, Zayer F, et al. Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack[C]//2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). Piscataway, NJ: IEEE, 2024: 612-619.
[8]	李智, 陈盈盈. 具身智能体数据隐私风险的合作治理[J]. 上海财经大学学报(哲学社会科学版), 2025, 27(5): 138-152.
	Li Zhi, Chen Yingying. Cooperative governance of data privacy risks of embodied intelligent agents[J]. Journal of Shanghai University of Finance and Economics (Philosophy and Social Sciences Edition), 2025, 27(5): 138-152.
[9]	姚胜译, 吴丹. APP隐私政策用户友好度评价研究[J]. 信息资源管理学报, 2021, 11(1): 30-39, 58.
	Yao Shengyi, Wu Dan. Assessment research to APP privacy policy user-friendliness[J]. Journal of Information Resources Management, 2021, 11(1): 30-39, 58.
[10]	廖秉宜, 狄鹤仙. 我国生成式人工智能产品的隐私政策研究——基于67例隐私政策的文本分析[J]. 中国编辑, 2025(10): 56-63.
	Liao Bingyi, Di Hexian. Research on privacy policy of generative artificial intelligence products in China—Based on the text analysis of 67 cases of privacy policy[J]. Chinese Editors Journal, 2025(10): 56-63.
[11]	曹梓勤, 李奕凡. 敏感个人信息安全视角下运动健身类智能应用的隐私政策研究[J]. 大众电视, 2025(11): 56-59.
	Cao Ziqin, Li Yifan. Research on privacy policies of fitness apps from the perspective of sensitive personal information security[J]. Popular Television, 2025(11): 56-59.
[12]	Zadushlivy N, Biviji R, Williams K S. Exploration of reproductive health apps' data privacy policies and the risks posed to users: Qualitative content analysis[J]. Journal of Medical Internet Research, 2025, 27: e51517.
[13]	张艳丰, 王羽西, 彭丽徽. 硬规则下移动短视频App隐私政策用户感知测度实证研究[J]. 情报理论与实践, 2021, 44(7): 94-100, 110.
	Zhang Yanfeng, Wang Yuxi, Peng Lihui. Empirical research on user perception measure of mobile short video app privacy policy under hard rules[J]. Information Studies: Theory & Application, 2021, 44(7): 94-100, 110.
[14]	王伟正, 乔鸿, 李肖俊, 等. 基于AIDUA框架的生成式人工智能使用意愿研究[J]. 农业图书情报学报, 2024, 36(2): 36-50.
	Wang Weizheng, Qiao Hong, Li Xiaojun, et al. User willingness to use generative artificial intelligence based on AIDUA framework[J]. Journal of Library and Information Science in Agriculture, 2024, 36(2): 36-50.
[15]	蔡文. 可拓集合和不相容问题[J]. 科学探索学报, 1983(1): 83-97.
	Cai Wen. Extension set and non-compatible problems[J]. Scientific Exploration Journal, 1983(1): 83-97.
[16]	蔡文. 物元模型及其应用[M]. 北京: 科学技术文献出版社, 1994: 1-11.
	Cai Wen. Matter-element model and its application[M]. Beijing: Scientific and Technical Documents Publishing House, 1994: 1-11.
[17]	蔡文, 杨春燕, 林伟初. 可拓工程方法[M]. 北京: 科学出版社, 1997: 8.
	Cai Wen, Yang Chunyan, Lin Weichu. Extension engineering method[M]. Beijing: Science Press, 1997: 8.
[18]	万君, 王玥. 基于物元可拓模型的金砖五国创新能力评级与建议[J]. 情报探索, 2021(2): 17-25.
	Wan Jun, Wang Yue. Innovation capability rating and recommendation of BRICS based on matter-element extension model[J]. Information Research, 2021(2): 17-25.
[19]	马永红, 王晓彤. 基于熵权物元可拓的区域创新系统成熟度评价[J]. 统计与决策, 2014, 30(22): 48-51.
	Ma Yonghong, Wang Xiaotong. Maturity evaluation of regional innovation system based on entropy weight matter-element extension[J]. Statistics and Decision, 2014, 30(22): 48-51.
[20]	朱思文. 基于综合权重和物元可拓法的企业突破性创新能力评价[J]. 世界科技研究与发展, 2015, 37(5): 600-604.
	Zhu Siwen. Evaluation about radical technology innovation ability of enterprises based on comprehensive weight and matter element extension method[J]. World Sci-Tech R & D, 2015, 37(5): 600-604.
[21]	洪闯, 李贺, 彭丽徽, 等. 在线健康咨询平台信息服务质量的物元模型及可拓评价研究[J]. 数据分析与知识发现, 2019, 3(8): 41-52.
	Hong Chuang, Li He, Peng Lihui, et al. Evaluating information services of online health Q & a platform[J]. Data Analysis and Knowledge Discovery, 2019, 3(8): 41-52.
[22]	赵蓉英, 王嵩. 基于熵权物元可拓模型的图书馆联盟绩效评价[J]. 图书情报工作, 2015, 59(12): 12-18.
	Zhao Rongying, Wang Song. Performance evaluation of library consortia based on entropy-weighted and matter-element model[J]. Library and Information Service, 2015, 59(12): 12-18.
[23]	赵又霖, 曹宏楠. 面向应急管理的政务微博信息交流效率及其影响因素研究[J]. 农业图书情报学报, 2022, 34(9): 72-85.
	Zhao Youlin, Cao Hongnan. Government microblog information exchange efficiency and its influencing factors for emergency management[J]. Journal of Library and Information Science in Agriculture, 2022, 34(9): 72-85.
[24]	马家伟, 张艳丰, 唐思慧. 我国在线健康咨询类APP隐私政策合规性用户感知测度实证研究[J]. 图书情报工作, 2022, 66(15): 86-96.
	Ma Jiawei, Zhang Yanfeng, Tang Sihui. Empirical research on user perception measurement of privacy policy compliance of online health consulting APP in China[J]. Library and Information Service, 2022, 66(15): 86-96.
[25]	Morville P. User experience design[EB/OL]. [2026-01-19].
[26]	朱侯, 张明鑫, 路永和. 社交媒体用户隐私政策阅读意愿实证研究[J]. 情报学报, 2018, 37(4): 362-371.
	Zhu Hou, Zhang Mingxin, Lu Yonghe. Empirical study on users' reading intention of privacy policies for social media[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(4): 362-371.
[27]	张敏, 沈嘉裕, 刘华玮, 等. 我国互联网医院APP的隐私政策评价研究——基于认知负荷与内容合规双重视域[J]. 现代情报, 2023, 43(3): 110-122.
	Zhang Min, Shen Jiayu, Liu Huawei, et al. Privacy policy evaluation of Internet hospital apps in China from the perspective of cognitive load and content compliance[J]. Journal of Modern Information, 2023, 43(3): 110-122.
[28]	杨瑞仙, 李航毅, 孙倬. 社交网络数据隐私保护：溯源、技术、政策、展望[J]. 农业图书情报学报, 2024, 36(4): 4-20.
	Yang Ruixian, Li Hangyi, Sun Zhuo. Privacy protection of social network data: Traceability, technology, policy and perspectives[J]. Journal of Library and Information Science in Agriculture, 2024, 36(4): 4-20.
[29]	中华人民共和国科学技术部. 发展负责任的人工智能: 新一代人工智能治理原则发布[EB/OL]. [2025-12-21].
[30]	《新一代人工智能伦理规范》发布-中华人民共和国科学技术部[EB/OL]. [2025-12-21].

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

调查题目	选项	人数/人	比例/%
性别	女	152	45.23
	男	184	54.77
年龄	18岁以下	5	1.48
	18~25岁	88	26.04
	26~35岁	118	35.15
	36~45岁	77	22.99
	46岁及以上	48	14.34
教育程度	高中及以下	30	8.97
	大专	51	15.30
	本科	101	30.03
	硕士及以上	154	45.70
职业	在校学生	52	15.50
	公司职员	175	52.03
	政府及事业单位人员	37	11.07
	其他	72	21.40

待评对象	N1	N2	N3	N4	N5	综合关联度最大值	综合等级变量特征值	综合评价等级
大疆	-0.587	-0.421	-0.038	0.031	-0.278	0.031	3.652	N4
擎朗智能	-0.493	-0.267	0.123	-0.084	-0.334	0.123	3.356	N3
米家	-0.587	-0.403	-0.103	0.030	-0.247	0.030	3.685	N4
科沃斯	-0.613	-0.443	-0.175	-0.001	-0.193	-0.001	3.782	N4
蔚来	-0.505	-0.285	0.064	-0.136	-0.271	0.064	3.443	N3
宇树科技	-0.437	-0.201	0.137	-0.097	-0.360	0.137	3.210	N3

评价指标	A11信息完整性	A12目的明确性	A15包容性与特殊保护	A33用户意愿尊重性	A62风险告知透明性
大疆	0.051（N3）	0.162（N3）	0.486（N3）	0.262（N4）	0.032（N3）
擎朗	0.185（N4）	0.244（N3）	0.356（N3）	0.444（N2）	0.042（N3）
米家	0.333（N4）	0.177（N3）	0.177（N3）	0.225（N4）	0.045（N4）
科沃斯	0.126（N5）	0.096（N4）	0.260（N3）	0.301（N3）	0.059（N3）
蔚来	0.152（N4）	0.481（N3）	0.230（N3）	0.362（N3）	0.436（N3）
宇树科技	0.072（N3）	0.169（N4）	0.362（N3）	0.039（N2）	0.097（N3）

评价指标	A31语言通俗性	A32视觉舒适性	A34语气平等性	A43导航便捷性	A52功能内置程度
大疆	0.025（N4）	0.145（N3）	0.238（N3）	0.137（N3）	0.057（N5）
擎朗	0.198（N3）	0.444（N3）	0.222（N3）	0.346（N3）	0.432（N3）
米家	0.398（N4）	0.098（N4）	0.242（N3）	0.018（N4）	0.398（N2）
科沃斯	0.214（N4）	0.143（N4）	0.052（N4）	0.050（N3）	0.323（N5）
蔚来	0.181（N3）	0.387（N3）	0.126（N3）	0.325（N3）	0.384（N3）
宇树科技	0.174（N3）	0.108（N2）	0.293（N4）	0.182（N3）	0.485（N3）

Evaluation of Privacy Policy Friendliness of Embodied Intelligence Applications

RichHTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 10

References 30

Related Articles 4

Metrics

Comments

Recommended 0

[1]	WU Dan, XU Huaqing. Affective Computing for Social Robots from the Perspective of Human-AI Interaction: A Literature Review and Theoretical Model Construction [J]. Journal of library and information science in agriculture, 2026, 38(1): 4-17.
[2]	HAO Yali, LIANG Ying, DING Ruoxi. Mechanism Analysis and Risk Regulation of the Embedding of Emotional-Functional Embodied Intelligence in Social Governance: From the Perspective of "Instrument and Value" Rationality [J]. Journal of library and information science in agriculture, 2026, 38(1): 18-27.
[3]	ZHANG Bin, ZHOU Yonghong. Investigation into and Reflection of User Privacy Policies of Reading Apps [J]. Journal of library and information science in agriculture, 2021, 33(7): 35-45.
[4]	TIAN Tian. Content Analysis of the Library of Congress's Privacy Policy [J]. Journal of library and information science in agriculture, 2013, 25(12): 109-111.