Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Human Emotions in AI Explanations

  • Conference paper
  • First Online:
Explainable Artificial Intelligence (xAI 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2156))

Included in the following conference series:

Abstract

For adapting to humans, explainable AI (XAI) quality can benefit from human expressions beyond verbal expressions. This is particularly true for emotions, as emotions affect how information is processed and subsequent decision-making. We analyze how different explanation strategies of XAI are perceived when the human interaction partner shows high arousal and/or valence. In a between-subjects experimental setting, we show that individuals with low arousal follow advice with no attempt to any explanation. On the contrary, individuals in a highly aroused state respond best to explanations with some justification (guided explanations). Concerning varying levels of valence, we find no similar pattern. Our results suggest that specific XAI strategies should be adapted not only to humans’ cognitive needs but also to humans’ information processing capacities and needs, which depend on emotional arousal.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 74.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Note, however, that personality traits have been shown to affect advice-taking: Matarese et al. [41] found that explainees who were high in agreeableness were more likely to follow robot advice than people with low agreeableness.

  2. 2.

    See also Lammert et al. [28] for more explanation about the method and materials.

References

  1. Bailey, P.E., Leon, T., Ebner, N.C., Moustafa, A.A., Weidemann, G.: A meta-analysis of the weight of advice in decision-making. Curr. Psychol. 42(28), 24516–24541 (2023)

    Article  Google Scholar 

  2. Bechara, A., Damasio, H., Damasio, A.R.: Role of the amygdala in decision-making. Ann. N. Y. Acad. Sci. 985(1), 356–369 (2003)

    Article  Google Scholar 

  3. Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)

    Article  Google Scholar 

  4. Collins, N.L.: Working models of attachment: implications for explanation, emotion, and behavior. J. Pers. Soc. Psychol. 71(4), 810 (1996)

    Article  Google Scholar 

  5. Comstock, L.M., Hooper, E.M., Goodwin, J.M., Goodwin, J.S.: Physician behaviors that correlate with patient satisfaction. Acad. Med. 57(2), 105–12 (1982)

    Article  Google Scholar 

  6. Dietvorst, B.J., Simmons, J.P., Massey, C.: Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144(1), 114 (2015)

    Article  Google Scholar 

  7. Duncan, S., Barrett, L.F.: Affect is a form of cognition: a neurobiological analysis. Cogn. Emot. 21(6), 1184–1211 (2007)

    Article  Google Scholar 

  8. Fernandes, M.A., Koji, S., Dixon, M.J., Aquino, J.M.: Changing the focus of attention: the interacting effect of valence and arousal. Vis. Cogn. 19(9), 1191–1211 (2011)

    Article  Google Scholar 

  9. Fox, C.R., Tannenbaum, D.: The elusive search for stable risk preferences. Front. Psychol. 2, 298 (2011)

    Article  Google Scholar 

  10. Fredrickson, B.L.: Chapter one - positive emotions broaden and build. Adv. Exp. Social Psychol. 47, 1–53 (2013). https://doi.org/10.1016/B978-0-12-407236-7.00001-2. https://www.sciencedirect.com/science/article/pii/B9780124072367000012

  11. Frenzel, A.C., Goetz, T., Lüdtke, O., Pekrun, R., Sutton, R.E.: Emotional transmission in the classroom: exploring the relationship between teacher and student enjoyment. J. Educ. Psychol. 101(3), 705 (2009)

    Article  Google Scholar 

  12. Gasper, K., Clore, G.L.: Attending to the big picture: mood and global versus local processing of visual information. Psychol. Sci. 13(1), 34–40 (2002)

    Article  Google Scholar 

  13. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)

    Google Scholar 

  14. Griffith, C.H., III., Wilson, J.F., Langer, S., Haist, S.A.: House staff nonverbal communication skills and standardized patient satisfaction. J. Gen. Intern. Med. 18(3), 170–174 (2003)

    Article  Google Scholar 

  15. Grimm, J.: State-trait-anxiety inventory nach spielberger. Deutsche Lang-und Kurzversion. Methodenforum der Universität Wien: MF-Working Paper 2, 2009 (2009)

    Google Scholar 

  16. Groß, A., et al.: Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue. Front. Rob. AI 10 (2023)

    Google Scholar 

  17. Hanselle, J., Kornowicz, J., Heid, S., Thommes, K., Hüllermeier, E.: Comparing humans and algorithms in feature ranking: a case-study in the medical domain (2023)

    Google Scholar 

  18. Harvey, N., Fischer, I.: Taking advice: accepting help, improving judgment, and sharing responsibility. Organ. Behav. Hum. Decis. Process. 70(2), 117–133 (1997)

    Article  Google Scholar 

  19. Hegel, F., Eyssel, F., Wrede, B.: The social robot ‘flobi’: key concepts of industrial design. In: 19th International Symposium in Robot and Human Interactive Communication, pp. 107–112. IEEE (2010)

    Google Scholar 

  20. Hidi, S., Renninger, K.A.: The four-phase model of interest development. Educ. Psychol. 41(2), 111–127 (2006)

    Article  Google Scholar 

  21. Hoffmann, C., Thommes, K.: Can digital feedback increase employee performance and energy efficiency in firms? evidence from a field experiment. J. Econ. Behav. Organ. 180, 49–65 (2020)

    Article  Google Scholar 

  22. Hofheinz, C., Germar, M., Schultze, T., Michalak, J., Mojzisch, A.: Are depressed people more or less susceptible to informational social influence? Cogn. Ther. Res. 41, 699–711 (2017)

    Article  Google Scholar 

  23. Holt, C.A., Laury, S.K.: Risk aversion and incentive effects. Am. Econ. Rev. 92(5), 1644–1655 (2002)

    Article  Google Scholar 

  24. Hudon, A., Demazure, T., Karran, A., Léger, P.-M., Sénécal, S.: Explainable artificial intelligence (XAI): how the visualization of ai predictions affects user cognitive load and confidence. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B., Müller-Putz, G. (eds.) NeuroIS 2021. LNISO, vol. 52, pp. 237–246. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88900-5_27

    Chapter  Google Scholar 

  25. Kaptein, F., Broekens, J., Hindriks, K., Neerincx, M.: The role of emotion in self-explanations by cognitive agents. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 88–93. IEEE (2017)

    Google Scholar 

  26. Kim, T., Hinds, P.: Who should i blame? effects of autonomy and transparency on attributions in human-robot interaction. In: ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 80–85. IEEE (2006)

    Google Scholar 

  27. Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., Prendinger, H.: Deep learning for affective computing: text-based emotion recognition in decision support. Decis. Support Syst. 115, 24–35 (2018)

    Article  Google Scholar 

  28. Lammert, O., Richter, B., Schütze, C., Thommes, K., Wrede, B.: Humans in xai: increased reliance in decision-making under uncertainty by using explanation strategies. Front. Behav. Econ. 3 (2024). https://doi.org/10.3389/frbhe.2024.1377075

  29. Laux, L., Glanzmann, P., Schaffner, P., Spielberger, C.D.: Das state-trait-angstinventar [the state-trait anxiety inventory]. Hogrefe, Göttingen (in German) (1981)

    Google Scholar 

  30. Lee, T.H., Sakaki, M., Cheng, R., Velasco, R., Mather, M.: Emotional arousal amplifies the effects of biased competition in the brain. Social Cogn. Affect. Neurosci. 9(12), 2067–2077 (2014)

    Article  Google Scholar 

  31. Leong, Y.C., Dziembaj, R., D’Esposito, M.: Pupil-linked arousal biases evidence accumulation toward desirable percepts during perceptual decision-making. Psychol. Sci. 32(9), 1494–1509 (2021)

    Article  Google Scholar 

  32. Lerner, J.S., Han, S., Keltner, D.: Feelings and consumer decision making: extending the appraisal-tendency framework. J. Consum. Psychol. 17(3), 181–187 (2007)

    Article  Google Scholar 

  33. Lerner, J.S., Tiedens, L.Z.: Portrait of the angry decision maker: how appraisal tendencies shape anger’s influence on cognition. J. Behav. Decis. Mak. 19(2), 115–137 (2006)

    Article  Google Scholar 

  34. Levitt, S.D., List, J.A.: What do laboratory experiments measuring social preferences reveal about the real world? J. Econ. Perspect. 21(2), 153–174 (2007)

    Article  Google Scholar 

  35. Lighthall, G.K., Vazquez-Guillamet, C.: Understanding decision making in critical care. Clin. Med. Res. 13(3–4), 156–168 (2015)

    Article  Google Scholar 

  36. Loewenstein, G.: Hot-cold empathy gaps and medical decision making. Health Psychol. 24(4S), S49 (2005)

    Article  Google Scholar 

  37. Logg, J.M., Minson, J.A., Moore, D.A.: Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process. 151, 90–103 (2019)

    Article  Google Scholar 

  38. Lütkebohle, I., et al.: The bielefeld anthropomorphic robot head “flobi”. In: 2010 IEEE International Conference on Robotics and Automation, pp. 3384–3391. IEEE (2010)

    Google Scholar 

  39. Madhavan, P., Wiegmann, D.A.: Similarities and differences between human-human and human-automation trust: an integrative review. Theor. Issues Ergon. Sci. 8(4), 277–301 (2007)

    Article  Google Scholar 

  40. Matarese, M., Cocchella, F., Rea, F., Sciutti, A.: Ex (plainable) machina: how social-implicit xai affects complex human-robot teaming tasks. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 11986–11993. IEEE (2023)

    Google Scholar 

  41. Matarese, M., Cocchella, F., Rea, F., Sciutti, A.: Natural born explainees: how users’ personality traits shape the human-robot interaction with explainable robots. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1786–1793. IEEE (2023)

    Google Scholar 

  42. Mather, M., Sutherland, M.R.: Arousal-biased competition in perception and memory. Perspect. Psychol. Sci. 6(2), 114–133 (2011)

    Article  Google Scholar 

  43. Moon, Y., Nass, C.: Are computers scapegoats? attributions of responsibility in human-computer interaction. Int. J. Hum Comput Stud. 49(1), 79–94 (1998)

    Article  Google Scholar 

  44. Paul, E.S., Sher, S., Tamietto, M., Winkielman, P., Mendl, M.T.: Towards a comparative science of emotion: affect and consciousness in humans and animals. Neurosci. Biobehav. Rev. 108, 749–770 (2020)

    Article  Google Scholar 

  45. Pekrun, R.: The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18, 315–341 (2006)

    Article  Google Scholar 

  46. Planalp, S., Fitness, J.: Thinking/feeling about social and personal relationships. J. Soc. Pers. Relat. 16(6), 731–750 (1999)

    Article  Google Scholar 

  47. Prahl, A., Van Swol, L.: Understanding algorithm aversion: when is advice from automation discounted? J. Forecast. 36(6), 691–702 (2017)

    Article  MathSciNet  Google Scholar 

  48. Rosenthal-von der Pütten, A.M., Krämer, N.C., Hoffmann, L., Sobieraj, S., Eimler, S.C.: An experimental study on emotional reactions towards a robot. Int. J. Social Rob. 5, 17–34 (2013)

    Google Scholar 

  49. Rabin, M., Thaler, R.H.: Anomalies: risk aversion. J. Econ. Perspect. 15(1), 219–232 (2001)

    Article  Google Scholar 

  50. Rauthmann, J.F.: Personality is (so much) more than just self-reported big five traits. Eur. J. Pers. 08902070231221853 (2023)

    Google Scholar 

  51. Reeves, B., Nass, C.: The media equation: how people treat computers, television, and new media like real people. Cambridge, UK 10(10) (1996)

    Google Scholar 

  52. Robertson, C.E., Pröllochs, N., Schwarzenegger, K., Pärnamets, P., Van Bavel, J.J., Feuerriegel, S.: Negativity drives online news consumption. Nat. Hum. Behav. 7(5), 812–822 (2023)

    Article  Google Scholar 

  53. Rohlfing, K.J., et al.: Explanation as a social practice: toward a conceptual framework for the social design of AI systems. IEEE Trans. Cogn. Dev. Syst. 13(3), 717–728 (2020)

    Article  Google Scholar 

  54. Roter, D.L., Frankel, R.M., Hall, J.A., Sluyter, D.: The expression of emotion through nonverbal behavior in medical visits: mechanisms and outcomes. J. Gen. Intern. Med. 21, 28–34 (2006)

    Article  Google Scholar 

  55. Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., Vössing, M.: A meta-analysis of the utility of explainable artificial intelligence in human-AI decision-making. In: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, pp. 617–626 (2022)

    Google Scholar 

  56. Schmidt, P., Biessmann, F., Teubner, T.: Transparency and trust in artificial intelligence systems. J. Decis. Syst. 29(4), 260–278 (2020)

    Article  Google Scholar 

  57. Schniter, E., Shields, T.W., Sznycer, D.: Trust in humans and robots: economically similar but emotionally different. J. Econ. Psychol. 78, 102253 (2020)

    Article  Google Scholar 

  58. Schoonderwoerd, T.A., Jorritsma, W., Neerincx, M.A., Van Den Bosch, K.: Human-centered xai: developing design patterns for explanations of clinical decision support systems. Int. J. Hum Comput Stud. 154, 102684 (2021)

    Article  Google Scholar 

  59. Schultze, T., Rakotoarisoa, A.F., Stefan, S.H.: Effects of distance between initial estimates and advice on advice utilization. Judgm. Decis. Mak. 10(2), 144–171 (2015)

    Article  Google Scholar 

  60. Schütze, C., Lammert, O., Richter, B., Thommes, K., Wrede, B.: Emotional debiasing explanations for decisions in hci. In: Degen, H., Ntoa, S. (eds.) International Conference on Human-Computer Interaction, pp. 318–336. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-35891-3_20

    Chapter  Google Scholar 

  61. Schwarz, N., Clore, G.L.: Mood as information: 20 years later. Psychol. Inq. 14(3–4), 296–303 (2003)

    Article  Google Scholar 

  62. Song, Y., Luximon, Y.: Trust in AI agent: a systematic review of facial anthropomorphic trustworthiness for social robot design. Sensors 20(18), 5087 (2020)

    Article  Google Scholar 

  63. Sozialforschung: SOEP 2014 – Erhebungsinstrumente 2014 (Welle 31) des Sozio-oekonomischen Panels: Personenfragebogen, Altstichproben. SOEP Survey Papers 235: Series A. Berlin: DIW/SOEP (2014)

    Google Scholar 

  64. Springer, A., Whittaker, S.: Progressive disclosure: when, why, and how do users want algorithmic transparency information? ACM Trans. Interact. Intell. Syst. (TiiS) 10(4), 1–32 (2020)

    Article  Google Scholar 

  65. Stock-Homburg, R.: Survey of emotions in human-robot interactions: perspectives from robotic psychology on 20 years of research. Int. J. Soc. Robot. 14(2), 389–411 (2022)

    Article  Google Scholar 

  66. Wang, D., Yang, Q., Abdul, A., Lim, B.Y.: Designing theory-driven user-centric explainable AI. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–15 (2019)

    Google Scholar 

  67. Weber, E.U., Blais, A.R., Betz, N.E.: A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors. J. Behav. Decis. Mak. 15(4), 263–290 (2002)

    Article  Google Scholar 

  68. Wichary, S., Mata, R., Rieskamp, J.: Probabilistic inferences under emotional stress: how arousal affects decision processes. J. Behav. Decis. Mak. 29(5), 525–538 (2016)

    Article  Google Scholar 

  69. Wu, C.H., Wu, C.C., Kan, M.H., Bayarjargal, U.: Effect of online advertisement types on click behavior in Mongolia: mediating impact of emotion. In: Proceedings of the 4th Multidisciplinary International Social Networks Conference, pp. 1–8 (2017)

    Google Scholar 

  70. Yaniv, I.: The benefit of additional opinions. Curr. Dir. Psychol. Sci. 13(2), 75–78 (2004)

    Article  Google Scholar 

  71. Yaniv, I.: Receiving other people’s advice: influence and benefit. Organ. Behav. Hum. Decis. Process. 93(1), 1–13 (2004)

    Article  MathSciNet  Google Scholar 

  72. You, S., Yang, C.L., Li, X.: Algorithmic versus human advice: does presenting prediction performance matter for algorithm appreciation? J. Manag. Inf. Syst. 39(2), 336–365 (2022)

    Article  Google Scholar 

Download references

Acknowledgments

This research is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 - 438445824.

Author information

Authors and Affiliations

  1. Faculty of Business Administration and Economics, Department of Management, Paderborn University, Warburger Street 100, 33098, Paderborn, Germany

    Kirsten Thommes & Olesja Lammert

  2. Medical School OWL, Medical Assistance Systems, Bielefeld University, Morgenbreede 1, 33615, Bielefeld, Germany

    Christian Schütze, Birte Richter & Britta Wrede

  3. Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, 33619, Bielefeld, Germany

    Christian Schütze, Birte Richter & Britta Wrede

Authors
  1. Kirsten Thommes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olesja Lammert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Schütze
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Birte Richter
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Britta Wrede
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olesja Lammert .

Editor information

Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Fraunhofer Institute for Telecommunications, Berlin, Germany

    Sebastian Lapuschkin

  3. University of Marburg, Marburg, Germany

    Christin Seifert

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thommes, K., Lammert, O., Schütze, C., Richter, B., Wrede, B. (2024). Human Emotions in AI Explanations. In: Longo, L., Lapuschkin, S., Seifert, C. (eds) Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol 2156. Springer, Cham. https://doi.org/10.1007/978-3-031-63803-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-63803-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-63802-2

  • Online ISBN: 978-3-031-63803-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation