Abstract
Achieving climate neutrality will require a major transformation of the transportation sector, likely leading to a surge in demand for electric vehicles (EVs). This poses a challenge to grid stability due to supply fluctuations of renewable energy resources. At the same time, EVs offer the potential to improve grid stability through managed charging. The complexity of this charging process can limit user flexibility and require more cognitive effort. Smart charging agents powered by artificial intelligence (AI) can address these challenges by optimizing charging profiles based on grid load predictions, but users must trust such systems to attain collective goals in a collaborative manner. In this study, we focus on traceability as a prerequisite for understanding and predicting system behavior and trust calibration. Subjective information processing awareness (SIPA) differentiates traceability into transparency, understandability, and predictability. The study aims to investigate the relationship between traceability, trust, and prediction performance in the context of smart charging agents through an online experiment. N = 57 participants repeatedly observed cost calculations made by a schematic algorithm, while the amount of disclosed information that formed the basis of the cost calculations was varied. Results showed that higher amount of disclosed information was related to higher reported trust. Moreover, traceability was partially higher in the high-information group than the medium and low-information groups. Conversely, participants’ performance in estimating the booking costs did not vary with amount of disclosed information. This pattern of results might reflect an explainability pitfall: Users of smart charging agents might trust these systems more as traceability increases, regardless of how well they understand the system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergmeir, C., et al.: Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy (2022). https://doi.org/10.48550/ARXIV.2212.10723
Buckless, F.A., Ravenscroft, S.P.: Contrast coding: a refinement of ANOVA in behavioral analysis. Account. Rev. 65, 933–945 (1990). https://www.jstor.org/stable/247659
Chiou, E.K., Lee, J.D.: Trusting automation: designing for responsivity and resilience. Hum. Factors 65(1), 137–165 (2021). https://doi.org/10.1177/00187208211009995
Cohen, J.: A power primer. Psychol. Bull. 112, 155–159 (1992). https://doi.org/10.1037//0033-2909.112.1.155
Colmenar-Santos, A., Muñoz-Gómez, A.-M., Rosales-Asensio, E., López-Rey, Á.: Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario. Energy 183, 61–74 (2019). https://doi.org/10.1016/j.energy.2019.06.118
Ding, W., Abdel-Basset, M., Hawash, H., Ali, A.M.: Explainability of artificial intelligence methods, applications and challenges: a comprehensive survey. Inform. Sci. 615, 238–292 (2022). https://doi.org/10.1016/j.ins.2022.10.013
Dunn, T.J., Baguley, T., Brunsden, V.: From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation. Br. J. Psychol. 105, 399–412 (2014). https://doi.org/10.1111/bjop.12046
Ehsan, U., Riedl, M.O.: Explainability Pitfalls: Beyond Dark Patterns in Explainable AI (2021). http://arxiv.org/abs/2109.12480
Eisinga, R., Grotenhuis, M.T., Pelzer, B.: The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? Int. J. Public Health 58, 637–642 (2013). https://doi.org/10.1007/s00038-012-0416-3
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37, 32–64 (1995). https://doi.org/10.1518/001872095779049543
European Commission, European Green Deal. https://www.consilium.europa.eu/en/policies/green-deal/. Accessed 22 June 2023
Finger, H., Goeke, C., Diekamp, D., Standvoß, K., König, P.: LabVanced: A Unified JavaScript Framework for Online Studies (2017). https://www.labvanced.com/static/2017_IC2S2_LabVanced.pdf
Franke, T., Attig, C., Wessel, D.: A personal resource for technology interaction: development and validation of the affinity for technology interaction (ATI) scale. Int. J. Hum.-Comput. Int. 35, 456–467 (2019). https://doi.org/10.1080/10447318.2018.1456150
Franke, T., Trantow, M., Günther, M., Krems, J.F., Zott, V., Keinath, A.: Advancing electric vehicle range displays for enhanced user experience: the relevance of trust and adaptability. In: Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 249–256. ACM, Nottingham United Kingdom (2015). https://doi.org/10.1145/2799250.2799283
Hoc, J.-M.: Towards a cognitive approach to human–machine cooperation in dynamic situations. Int. J. Hum.-Comput. St. 54, 509–540 (2001). https://doi.org/10.1006/ijhc.2000.0454
Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance. Front. Comput. Sci. 5, 1096257 (2023). https://doi.org/10.3389/fcomp.2023.1096257
Jacovi, A., Marasović, A., Miller, T., Goldberg, Y.: Formalizing trust in artificial intelligence: prerequisites, causes and goals of human trust in AI. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 624–635. ACM, Virtual Event Canada (2021). https://doi.org/10.1145/3442188.3445923
Klein, G., Feltovich, P.J., Bradshaw, J.M., Woods, D.D.: Common ground and coordination in joint activity. In: Rouse, W.B., Boff, K.R. (eds.) Organizational Simulation, pp. 139–184. John Wiley & Sons Inc, Hoboken, NJ, USA (2005). https://doi.org/10.1002/0471739448.ch6
Kramer, J., Petzoldt, T.: A matter of behavioral cost: contextual factors and behavioral interventions interactively influence pro-environmental charging decisions. J. Environ. Psychol. 84, 101878 (2022). https://doi.org/10.1016/j.jenvp.2022.101878
Lee, J.D., See, K.A.: Trust in automation: designing for appropriate reliance. Hum. Fact. 46, 50–80 (2004). https://doi.org/10.1518/hfes.46.1.50.30392
Leys, C., Ley, C., Klein, O., Bernard, P., Licata, L.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013). https://doi.org/10.1016/j.jesp.2013.03.013
Rosenthal, R., Rosnow, R.L., Rubin, D.B.: Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press (1999)
Sadeghian, O., Oshnoei, A., Mohammadi-ivatloo, B., Vahidinasab, V., Anvari-Moghaddam, A.: A comprehensive review on electric vehicles smart charging: solutions, strategies, technologies, and challenges. J. Energy Stor. 54, 105241 (2022). https://doi.org/10.1016/j.est.2022.105241
Schrills, T., Franke, T.: How do users experience traceability of AI systems? examining subjective information processing awareness in automated insulin delivery (AID) systems. ACM Trans. Interact. Intell. Syst. 3588594 (2023). https://doi.org/10.1145/3588594
Schrills, T., Kargl, S., Bickel, M., Franke, T.: Perceive, Understand & Predict – Empirical Indication for Facets in Subjective Information Processing Awareness (2022) https://psyarxiv.com/3n95u/download
Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. Int. J. Hum.-Comput. St. 146, 102551 (2021). https://doi.org/10.1016/j.ijhcs.2020.102551
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Attig, C. et al. (2023). Enhancing Trust in Smart Charging Agents—The Role of Traceability for Human-Agent-Cooperation. In: Degen, H., Ntoa, S., Moallem, A. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14059. Springer, Cham. https://doi.org/10.1007/978-3-031-48057-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-48057-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48056-0
Online ISBN: 978-3-031-48057-7
eBook Packages: Computer ScienceComputer Science (R0)