Pausing for Reflection: How Design Friction Shapes Environmentally Responsible Artificial Intelligence Use and Trust

Abstract

The rapid growth of artificial intelligence (AI) has significant environmental impacts that are rarely communicated. This study leverages the concept of design friction to communicate AI’s environmental costs during user interaction with a system, and examines its effects on trust, perceived trust calibration, and responsible AI use. In an experiment (N = 171), cue-based friction indirectly enhanced trust-related outcomes through the transparency heuristic and perceived social responsibility, whereas action-based friction influenced both trust-related outcomes and responsible AI use through heightened cognitive elaboration but reduced perceptions of user agency. Implications for conceptualizing different forms of design friction and promoting responsible AI design are discussed.

Keywords

generative AI design friction environmental sustainability trust in AI responsible AI use

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References

Carfora

Zeiske

van der Werff

Steg

Catellani

(2022). Adding dynamic norm to environmental information in messages promoting the reduction of meat consumption. Environmental Communication, 16(7), 900–919. https://doi.org/10.1080/17524032.2022.2062019

Chan

Mak

M. K. F.

(2022). Mediation analysis and warranted inferences in media and communication research: Examining research design in communication journals from 1996 to 2017. Journalism & Mass Communication Quarterly, 99(2), 463–486. https://doi.org/10.1177/1077699020961519

Chen

Lee

Jang

Sundar

S. S.

(2024, September 15–18). Is your prompt detailed enough? Exploring the effects of prompt coaching on users’ perceptions, engagement, and trust in text-to-image generative AI tools [Paper presentation]. TAS ‘24: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems, Austin, TX, United States. https://doi.org/10.1145/3686038.3686060

Chen

Liao

Sundar

S. S.

(2024, September 15–18). When to explain? Exploring the effects of explanation timing on user perceptions and trust in AI systems [Paper presentation]. TAS ‘24: Proceedings of the Second International Symposium on Trustworthy Autonomous Systems, Austin, TX, United States. https://doi.org/10.1145/3686038.3686066

Chen

Liao

Phadnis

A. A.

High

Abdullah

S. M.

Sundar

S. S.

(2026). Prompt coaching for inclusiveness: A media literacy approach to increase users’ awareness of algorithmic bias and prompting efficacy. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, Barcelona, Spain. https://doi.org/10.1145/3772318.3791542

Chen

Sundar

S. S.

(2023, April 23). Is this AI trained on credible data? The effects of labeling quality and performance bias on user trust [Conference session]. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. https://doi.org/10.1145/3544548.3580805

Chen

Sundar

S. S.

(2024). Communicating and combating algorithmic bias: Effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust. Human-Computer Interaction. Advance online publication. https://doi.org/10.1080/07370024.2024.2392494

Chen

Zhao

(2026). Let’s think step by step: Effects of chain-of-thought prompt coaching on users’ perceptions and trust in image generative AI tools. International Journal of Human–Computer Interaction, 42, 2943–2964. https://doi.org/10.1080/10447318.2025.2530762

Chen

Shi

(2024). Communication technology and environmental communication: Challenges, opportunities, and emerging agendas. Environmental Communication, 18(5), 519–524. https://doi.org/10.1080/17524032.2024.2382476

10.

Choung

David

Ross

(2023). Trust and ethics in AI. AI & Society, 38(2), 733–745. https://doi.org/10.1007/s00146-022-01473-4

11.

Cox

A. L.

Gould

S. J. J.

Cecchinato

M. E.

Iacovides

Renfree

(2016, May 7). Design frictions for mindful interactions: The case for microboundaries [Conference session]. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, San Jose, CA, United States. https://doi.org/10.1145/2851581.2892410

12.

Crawford

(2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

13.

Crawford

(2024, February 20). Generative AI’s environmental costs are soaring-and mostly secret. Nature. https://www.nature.com/articles/d41586-024-00478-x

14.

Dabholkar

P. A.

Sheng

(2012). Consumer participation in using online recommendation agents: Effects on satisfaction, trust, and purchase intentions. The Service Industries Journal, 32(9), 1433–1449. https://doi.org/10.1080/02642069.2011.624596

15.

Distler

Lenzini

Lallemand

Koenig

(2020). The framework of security-enhancing friction: How UX can help users behave more securely. New Security Paradigms Workshop, 2020, 45–58. https://doi.org/10.1145/3442167.3442173

16.

Egelman

Cranor

L. F.

Hong

(2008, April 5–10). You’ve been warned: An empirical study of the effectiveness of web browser phishing warnings [Conference session]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy. https://doi.org/10.1145/1357054.1357219

17.

European Commission. (2019). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

18.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312

19.

Global Footprint Network. (n.d.). Footprint calculator. https://www.footprintcalculator.org/home/en

20.

Goethals

Ziegelmayer

J. L.

(2022). Anxiety buffers and the threat of extreme automation: A terror management theory perspective. Information Technology & People, 35(1), 96–118. https://doi.org/10.1108/ITP-06-2019-0304

21.

Greenhouse Gas Equivalencies Calculator. (n.d.). United States environmental protection agency. https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator

22.

Groza

M. D.

Pronschinske

M. R.

Walker

(2011). Perceived organizational motives and consumer responses to proactive and reactive CSR. Journal of Business Ethics, 102(4), 639–652. https://doi.org/10.1007/s10551-011-0834-9

23.

Guadamuz

(2025, April 14). How many people are using generative AI on a daily basis? A Gemini report. TechnoLlama. https://www.technollama.co.uk/a-gemini-report-how-many-people-are-using-generative-ai-on-a-daily-basis-a-gemini-report

24.

Hedeen

(2020, October 8). When design friction is a good thing. Medium. https://uxdesign.cc/when-design-friction-is-a-good-thing-d4ab56c4049e

25.

Heikkilä

(2023, December 1). Making an image with generative AI uses as much energy as charging your phone. MIT Technology Review. https://www.technologyreview.com/2023/12/01/1084189/making-an-image-with-generative-ai-uses-as-much-energy-as-charging-your-phone/

26.

Johnson

Grayson

(2005). Cognitive and affective trust in service relationships. Journal of Business Research, 58(4), 500–507. https://doi.org/10.1016/S0148-2963(03)00140-1

27.

Kline

R. B.

(2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

28.

Lang

V. S.

Bhalla

N. K.

Gurdasani

S. S.

Niknejadi

(2025). Know your footprint—Evaluation of the professional carbon footprint for individual researchers in high energy physics and related fields. Npj Climate Action, 4(1), 28. https://doi.org/10.1038/s44168-025-00232-7

29.

Lee

H. M.

Yadav

Lee

Govindarazan

Chen

Sundar

S. S.

(2025, May 26). While we wait. . . How users perceive waiting times and generation cues during AI image generation [Conference session]. CHI EA ’25: The CHI Conference on Human Factors in Computing Systems, Yokohama, Japan.

30.

Lee

J. D.

See

K. A.

(2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392

31.

Yang

Islam

M. A.

Ren

(2025). Making AI less “thirsty.” Communications of the ACM, 68(7), 54–61. https://doi.org/10.1145/3724499

32.

Liao

Sundar

S. S.

(2025, June). Chat but verify: Combating misinformation by Generative AI with verification affordance [Conference session]. The 75th Annual Conference of the International Communication Association, Denver, CO, United States.

33.

Liao

Q. V.

Sundar

S. S.

(2022, June 20). Designing for responsible trust in AI systems: A communication perspective [Conference session]. 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea. https://doi.org/10.1145/3531146.3533182

34.

Lin

Lobo

Leckie

(2017). The role of benefits and transparency in shaping consumers’ green perceived value, self-brand connection and brand loyalty. Journal of Retailing and Consumer Services, 35, 133–141. https://doi.org/10.1016/j.jretconser.2016.12.011

35.

Littlejohn

S. W.

Foss

K. A.

(Eds.). (2009). Encyclopedia of communication theory. Sage. https://doi.org/10.4135/9781412959384

36.

Mejtoft

Hale

Söderström

(2019). Design friction: How intentionally added friction affect users level of satisfaction. Umeå universitet, Institutionen för tillämpad fysik och elektronik. https://doi.org/10.1145/3335082.3335106

37.

Mejtoft

Parsjö

Norberg

Söderström

(2023, June 16). Design friction and digital nudging: Impact on the human decision-making process [Conference session]. Proceedings of the 2023 5th International Conference on Image, Video and Signal Processing, Singapore. https://doi.org/10.1145/3591156.3591183

38.

Molina

M. D.

Sundar

S. S.

(2022). When AI moderates online content: Effects of human collaboration and interactive transparency on user trust. Journal of Computer-Mediated Communication, 27(4), zmac010. https://doi.org/10.1093/jcmc/zmac010

39.

Moore

J. W.

(2016). What is the sense of agency and why does it matter? Frontiers in Psychology, 7, Article 1272. https://doi.org/10.3389/fpsyg.2016.01272

40.

Mulder

Ferrer

Van Lente

(2017). What is sustainable technology? Perceptions, paradoxes and possibilities. Routledge.

41.

Naiseh

Al-Mansoori

R. S.

Al-Thani

Jiang

Ali

(2021, October 29–31). Nudging through Friction: An Approach for Calibrating Trust in Explainable AI [Conference session]. 2021 8th International Conference on Behavioral and Social Computing (BESC), Doha, Qatar. https://doi.org/10.1109/BESC53957.2021.9635271

42.

Narayanan

(2024). Against the green schema: How Gen-AI negatively impacts green influencer posts. Psychology & Marketing, 42(4), 970–986. https://doi.org/10.1002/mar.22159

43.

Nielsen

(2023, May 25). The articulation barrier: Prompt-driven AI UX hurts usability. https://www.linkedin.com/pulse/prompt-driven-ai-ux-hurts-usability-jakob-nielsen/

44.

Obringer

Rachunok

Maia-Silva

Arbabzadeh

Nateghi

Madani

(2021). The overlooked environmental footprint of increasing Internet use. Resources, Conservation and Recycling, 167, 105389. https://doi.org/10.1016/j.resconrec.2020.105389

45.

Penman

(1994). Environmental matters and communication challenges. Australian Journal of Communication, 21(3), 26–39. https://search.informit.org/doi/10.3316/ielapa.950606064

46.

Perse

E. M.

(1990). Involvement with local television news: Cognitive and emotional dimensions. Human Communication Research, 16(4), 556–581. https://doi.org/10.1111/j.1468-2958.1990.tb00222.x

47.

Petty

R. E.

Cacioppo

J. T.

(1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2

48.

Rainie

(2025). Close encounters of the AI kind: The increasingly human-like way people are engaging with language models. Imagining the Digital Future Center, Elon University. https://imaginingthedigitalfuture.org/reports-and-publications/close-encounters-of-the-ai-kind/close-encounters-of-the-ai-kind-main-report/

49.

Riquelme

I. P.

Román

(2014). Is the influence of privacy and security on online trust the same for all type of consumers? Electronic Markets, 24, 135–149. https://doi.org/10.1007/s12525-013-0145-3

50.

Schlossberg

(2022). Inconspicuous consumption: The environmental impact you don’t know you have (First trade paperback edition). Grand Central Publishing; WorldCat.

51.

Shin

(2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability. Journal of Broadcasting & Electronic Media, 64(4), 541–565. https://doi.org/10.1080/08838151.2020.1843357

52.

Shin

Koerber

Lim

J. S.

(2024). Impact of misinformation from generative AI on user information processing: How people understand misinformation from generative AI. New Media & Society, 27, 4017–4047. https://doi.org/10.1177/14614448241234040

53.

Siau

Wang

(2020). Artificial Intelligence (AI) ethics: Ethics of AI and ethical AI. Journal of Database Management, 31(2), 74–87. https://doi.org/10.4018/JDM.2020040105

54.

Sleeth-Keppler

Perkowitz

Speiser

(2017). It’s a matter of trust: American judgments of the credibility of informal communicators on solutions to climate change. Environmental Communication, 11(1), 17–40. https://doi.org/10.1080/17524032.2015.1062790

55.

Sundar

S. S.

(2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In Metzger

M. J.

Flanagin

A. J.

(Eds.), Digital media, youth, and credibility (pp. 73–100). MIT Press. https://library.oapen.org/bitstream/handle/20.500.12657/26088/1003998.pdf

56.

Sundar

S. S.

(2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026

57.

Sundar

S. S.

Jia

Waddell

T. F.

Huang

(2015). Toward a theory of interactive media effects (TIME): Four models for explaining how interface features affect user psychology. In Sundar

S. S.

(Ed.), The handbook of the psychology of communication technology (1st ed.; pp. 47–86). Wiley. https://doi.org/10.1002/9781118426456.ch3

58.

Sundar

S. S.

Kim

Rosson

M. B.

Molina

M. D.

(2020). Online privacy heuristics that predict information disclosure [Conference session]. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, United States. https://doi.org/10.1145/3313831.3376854

59.

Tomsett

Preece

Braines

Cerutti

Chakraborty

Srivastava

Pearson

Kaplan

(2020). Rapid trust calibration through interpretable and uncertainty-aware AI. Patterns, 1(4), 100049. https://doi.org/10.1016/j.patter.2020.100049

60.

Vinuesa

Azizpour

Leite

Balaam

Dignum

Domisch

Felländer

Langhans

S. D.

Tegmark

Fuso Nerini

(2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. https://doi.org/10.1038/s41467-019-14108-y

61.

Wan

Lee

(2024). The role of anthropomorphism and racial homophily of virtual influencers in encouraging low- versus high-cost pro-environmental behaviors. Psychology & Marketing, 41(8), 1833–1853. https://doi.org/10.1002/mar.22013

62.

Wang

Rau

P.-L. P.

Yuan

(2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768

63.

Wang

Sundar

S. S.

(2022). Liking versus commenting on online news: Effects of expression affordances on political attitudes. Journal of Computer-Mediated Communication, 27(6), zmac018. https://doi.org/10.1093/jcmc/zmac018

64.

Wang

Tanes-Ehle

(2023). Examining the effects of conversational chatbots on changing conspiracy beliefs about science: The paradox of interactivity. Journal of Broadcasting & Electronic Media, 67(1), 68–89. https://doi.org/10.1080/08838151.2022.2153842

65.

Wang

Xiong

Olya

(2020, October). Toward an understanding of responsible artificial intelligence practices [Conference session]. Hawaii International Conference on System Sciences, Maui, HI, United States. https://doi.org/10.24251/HICSS.2020.610

66.

Wathuge

Sedera

(2023). Reducing the negative environmental impact of consumerization of IT: An individual-level approach. Sustainability, 15(16), 12160. https://doi.org/10.3390/su151612160

67.

Wathuge

Sedera

(2025). Exploring strategies to mitigate Internet’s environmental damage: An investigation of intrinsic, extrinsic, and price-based strategies. Business Strategy and the Environment, 34(1), 483–496. https://doi.org/10.1002/bse.4000

68.

Watts

(2015). Corporate social responsibility reporting platforms: Enabling transparency for accountability. Information Technology and Management, 16(1), 19–35. https://doi.org/10.1007/s10799-014-0192-2

69.

Wilkins

E. J.

Miller

H. M.

Tilak

Schuster

R. M.

(2018). Communicating information on nature-related topics: Preferred information channels and trust in sources. PLOS ONE, 13(12), Article e0209013. https://doi.org/10.1371/journal.pone.0209013

70.

C.-J.

Raghavendra

Gupta

Acun

Ardalani

Maeng

Chang

Behram

F. A.

Huang

Bai

Gschwind

Gupta

Ott

Melnikov

Candido

Brooks

Chauhan

Lee

H.-H. S.

, . . . Hazelwood

(2022). Sustainable AI: Environmental implications, challenges and opportunities [Conference session]. Proceedings of the 5th MLSys Conference, Santa Clara, CA, United States. https://doi.org/10.48550/arXiv.2111.00364

71.

Yang

Tian

(2021). “Others are more vulnerable to fake news than I Am”: Third-person effect of COVID-19 fake news on social media users. Computers in Human Behavior, 125, 106950. https://doi.org/10.1016/j.chb.2021.106950

72.

Zainol

Brotosaputro

Chen

S. C.

(2025). Designing ethical AI systems for sustainable technology development. ADI Journal on Recent Innovation, 6(2), 201–211. https://doi.org/10.34306

73.

Zewe

(2025, January 17). Explained: Generative AI’s environmental impact. MIT News. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117#:~:text=Rapid%20development%20and%20deployment%20of,electricity%20demand%20and%20water%20consumption

74.

Zhao

Austin

L. L.

(2024). How information repertoire affects vaccine hesitancy: Processes of information verification and cognitive elaboration. Health Communication, 39, 62–72. https://doi.org/10.1080/10410236.2022.2154017