ABSTRACT
Intelligent Personal Assistants (IPAs) have become increasingly ubiquitous, yet they remain primarily reactive, non-personalised, and inscrutable. Moreover, concerns regarding user control, data stewardship, and communication design persist in the literature. Aiming to shape an appropriate human-assistant interaction framework, we organised a multidisciplinary expert discussion focusing on proactive IPAs for time management – a bounded yet complex domain that may catalyse identifying and tackling paradigmatic challenges. We invited the experts to propose, debate, and chart interaction scenarios, desired characteristics and constraints, and user modelling for proactive IPAs. This paper presents the thematic analysis of the discussion and the resulting interaction diagram. The ability of the user to scrutinise the assistant’s models that underpin personalisation and to receive adequate explanations were identified to be of paramount significance. Moreover, a proposed onboarding and control mechanism that may help align the user’s perception of the system and the system’s actual capabilities is discussed.
- Shashank Ahire, Aaron Priegnitz, Oguz Önbas, Michael Rohs, and Wolfgang Nejdl. 2021. How Compatible is Alexa with Dual Tasking? — Towards Intelligent Personal Assistants for Dual-Task Situations. In Proceedings of the 9th International Conference on Human-Agent Interaction (Virtual Event, Japan) (HAI ’21). Association for Computing Machinery, New York, NY, USA, 103–111. https://doi.org/10.1145/3472307.3484165Google ScholarDigital Library
- Vince Bartle, Janice Lyu, Freesoul El Shabazz-Thompson, Yunmin Oh, Angela Anqi Chen, Yu-Jan Chang, Kenneth Holstein, and Nicola Dell. 2022. “A Second Voice”: Investigating Opportunities and Challenges for Interactive Voice Assistants to Support Home Health Aides. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 45, 17 pages.Google ScholarDigital Library
- Pauline M. Berry, Thierry Donneau-Golencer, Khang Duong, Melinda Gervasio, Bart Peintner, and Neil Yorke-Smith. 2017. Evaluating intelligent knowledge systems: experiences with a user-adaptive assistant agent. Knowledge and Information Systems 52, 2 (2017).Google Scholar
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101.Google ScholarCross Ref
- Leigh Clark, Nadia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Justin Edwards, Brendan Spillane, Emer Gilmartin, Christine Murad, Cosmin Munteanu, Vincent Wade, and Benjamin R. Cowan. 2019. What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12.Google ScholarDigital Library
- Benjamin R. Cowan, Nadia Pantidi, David Coyle, Kellie Morrissey, Peter Clarke, Sara Al-Shehri, David Earley, and Natasha Bandeira. 2017. "What Can i Help You with?": Infrequent Users’ Experiences of Intelligent Personal Assistants. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vienna, Austria) (MobileHCI ’17). Association for Computing Machinery, New York, NY, USA, Article 43, 12 pages.Google ScholarDigital Library
- Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, and Andrés Monroy-Hernández. 2017. Calendar.Help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 2382–2393.Google ScholarDigital Library
- Valdemar Danry, Pat Pataranutaporn, Yaoli Mao, and Pattie Maes. 2020. Wearable Reasoner: Towards Enhanced Human Rationality Through A Wearable Device With An Explainable AI Assistant. In Proceedings of the Augmented Humans International Conference (Kaiserslautern, Germany) (AHs ’20). Association for Computing Machinery, New York, NY, USA, Article 23, 12 pages.Google ScholarDigital Library
- Justin Edwards, He Liu, Tianyu Zhou, Sandy J. J. Gould, Leigh Clark, Philip Doyle, and Benjamin R. Cowan. 2019. Multitasking with Alexa: How Using Intelligent Personal Assistants Impacts Language-Based Primary Task Performance. In Proceedings of the 1st International Conference on Conversational User Interfaces (Dublin, Ireland) (CUI ’19). Association for Computing Machinery, New York, NY, USA, Article 4, 7 pages. https://doi.org/10.1145/3342775.3342785Google ScholarDigital Library
- Delaram Golpayegani, Harshvardhan J. Pandit, and Dave Lewis. 2023. Comparison and Analysis of 3 Key AI Documents: EU’s Proposed AI Act, Assessment List for Trustworthy AI (ALTAI), and ISO/IEC 42001 AI Management System. In 30th Irish Conference on Artificial Intelligence and Cognitive Science (AICS).Google Scholar
- Shreepriya Gonzalez-Jimenez, Danilo Gallo, Ricardo Sosa, Eduardo Benitez Sandoval, Tommaso Colombino, and Maria Antonietta Grasso. 2022. A Decision Support Design Framework for Selecting a Robotic Interface. In Proceedings of the 10th International Conference on Human-Agent Interaction (Christchurch, New Zealand) (HAI ’22). Association for Computing Machinery, New York, NY, USA, 104–113. https://doi.org/10.1145/3527188.3561913Google ScholarDigital Library
- Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 51, 5, Article 93 (aug 2018), 42 pages.Google ScholarDigital Library
- Eduardo Islas-Cota, J. Octavio Gutierrez-Garcia, Christian O. Acosta, and Luis-Felipe Rodríguez. 2022. A systematic review of intelligent assistants. Future Generation Computer Systems 128 (2022), 45–62.Google ScholarDigital Library
- Jovan Jeromela. 2022. Scrutability of Intelligent Personal Assistants. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (Barcelona, Spain) (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 335–340. https://doi.org/10.1145/3503252.3534355Google ScholarDigital Library
- Jovan Jeromela and Owen Conlan. 2023. Voicing Suggestions and Enabling Reflection: Results of an Expert Discussion on Proactive Assistants for Time Management. In Proceedings of the 5th International Conference on Conversational User Interfaces (Eindhoven, Netherlands) (CUI ’23). Association for Computing Machinery, New York, NY, USA, Article 48, 6 pages. https://doi.org/10.1145/3571884.3604317Google ScholarDigital Library
- Judy Kay. 1999. A scrutable user modelling shell for user-adapted interaction. Doctoral dissertation. University of Sydney.Google Scholar
- Judy Kay and Bob Kummerfeld. 2013. Creating Personalized Systems That People Can Scrutinize and Control: Drivers, Principles and Experience. ACM Trans. Interact. Intell. Syst. 2, 4, Article 24 (jan 2013), 42 pages.Google ScholarDigital Library
- Hassan Khosravi, Simon Buckingham Shum, Guanliang Chen, Cristina Conati, Yi-Shan Tsai, Judy Kay, Simon Knight, Roberto Martinez-Maldonado, Shazia Sadiq, and Dragan Gašević. 2022. Explainable Artificial Intelligence in education. Computers and Education: Artificial Intelligence 3 (2022), 100074.Google ScholarCross Ref
- Veton Këpuska and Gamal Bohouta. 2018. Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC).Google ScholarCross Ref
- Yaniv Leviathan and Yossi Matias. 2018. Google Duplex: An AI system for accomplishing real-world tasks over the phone. Last access: 2021-11-14.Google Scholar
- Ewa Luger and Abigail Sellen. 2016. "Like Having a Really Bad PA": The Gulf between User Expectation and Experience of Conversational Agents., 5286–5297 pages.Google Scholar
- Christian Meurisch, Maria-Dorina Ionescu, Benedikt Schmidt, and Max Mühlhäuser. 2017. Reference model of next-generation digital personal assistant: integrating proactive behavior. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 149–152.Google ScholarDigital Library
- Christian Meurisch, Cristina A. Mihale-Wilson, Adrian Hawlitschek, Florian Giger, Florian Müller, Oliver Hinz, and Max Mühlhäuser. 2020. Exploring User Expectations of Proactive AI Systems. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 4 (2020), Article 146.Google ScholarDigital Library
- Nykan Mirchi, Vincent Bissonnette, Recai Yilmaz, Nicole Ledwos, Alexander Winkler-Schwartz, and Rolando F. Del Maestro. 2020. The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLOS ONE 15, 2 (02 2020), 1–15.Google Scholar
- Roger K. Moore. 2017. Is Spoken Language All-or-Nothing? Implications for Future Speech-Based Human-Machine Interaction. Springer Singapore, Singapore, 281–291. https://doi.org/10.1007/978-981-10-2585-3_22Google ScholarCross Ref
- Roger K. Moore and Mauro Nicolao. 2017. Toward a Needs-Based Architecture for ‘Intelligent’ Communicative Agents: Speaking with Intention. Frontiers in Robotics and AI 4 (2017). https://doi.org/10.3389/frobt.2017.00066Google ScholarCross Ref
- Sheza Naveed, Hamza Naveed, Mobin Javed, and Maryam Mustafa. 2022. ”Ask This from the Person Who Has Private Stuff”: Privacy Perceptions, Behaviours and Beliefs Beyond W.E.I.R.D. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 105, 17 pages. https://doi.org/10.1145/3491102.3501883Google ScholarDigital Library
- Stefan Olafsson, Teresa O’Leary, and Timothy Bickmore. 2019. Coerced Change-Talk with Conversational Agents Promotes Confidence in Behavior Change. In Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare (Trento, Italy) (PervasiveHealth’19). Association for Computing Machinery, New York, NY, USA, 31–40. https://doi.org/10.1145/3329189.3329202Google ScholarDigital Library
- Danielle Marie Olson and Yan Xu. 2021. Building Trust Over Time in Human-Agent Relationships. In Proceedings of the 9th International Conference on Human-Agent Interaction (Virtual Event, Japan) (HAI ’21). Association for Computing Machinery, New York, NY, USA, 193–201. https://doi.org/10.1145/3472307.3484178Google ScholarDigital Library
- Martin Porcheron, Joel E. Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice Interfaces in Everyday Life. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12.Google ScholarDigital Library
- Nora Ptakauskaite, Anna L Cox, and Nadia Berthouze. 2018. Knowing what you’re doing or knowing what to do: how stress management apps support reflection and behaviour change. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. 1–6.Google ScholarDigital Library
- Samira Rasouli, Moojan Ghafurian, and Kerstin Dautenhahn. 2022. Students’ Views on Intelligent Agents as Assistive Tools for Dealing with Stress and Anxiety in Social Situations. In Proceedings of the 10th International Conference on Human-Agent Interaction (Christchurch, New Zealand) (HAI ’22). Association for Computing Machinery, New York, NY, USA, 23–31. https://doi.org/10.1145/3527188.3561932Google ScholarDigital Library
- Avi Rosenfeld and Ariella Richardson. 2019. Explainability in human–agent systems. Autonomous Agents and Multi-Agent Systems 33, 6 (2019), 673–705.Google ScholarDigital Library
- Emre Sezgin, Lisa K Militello, Yungui Huang, and Simon Lin. 2020. A scoping review of patient-facing, behavioral health interventions with voice assistant technology targeting self-management and healthy lifestyle behaviors. Translational Behavioral Medicine 10, 3 (2020), 606–628.Google ScholarCross Ref
- Leonie Nora Sieger, Julia Hermann, Astrid Schomäcker, Stefan Heindorf, Christian Meske, Celine-Chiara Hey, and Ayşegül Doğangün. 2022. User Involvement in Training Smart Home Agents: Increasing Perceived Control and Understanding. In Proceedings of the 10th International Conference on Human-Agent Interaction (Christchurch, New Zealand) (HAI ’22). Association for Computing Machinery, New York, NY, USA, 76–85. https://doi.org/10.1145/3527188.3561914Google ScholarDigital Library
- Ningyuan Sun and Jean Botev. 2021. Why Do We Delegate to Intelligent Virtual Agents? Influencing Factors on Delegation Decisions. In Proceedings of the 9th International Conference on Human-Agent Interaction (Virtual Event, Japan) (HAI ’21). Association for Computing Machinery, New York, NY, USA, 386–390. https://doi.org/10.1145/3472307.3484680Google ScholarDigital Library
- Nava Tintarev and Judith Masthoff. 2022. Beyond Explaining Single Item Recommendations. Springer US, New York, NY, 711–756.Google Scholar
- Carlos Toxtli, Andrés Monroy-Hernández, and Justin Cranshaw. 2018. Understanding Chatbot-Mediated Task Management. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–6.Google ScholarDigital Library
- Sruthi Viswanathan, Fabien Guillot, Minsuk Chang, Antonietta Maria Grasso, and Jean-Michel Renders. 2022. Addressing Hiccups in Conversations with Recommender Systems., 1243–1259 pages.Google Scholar
- Sarah Theres Völkel, Daniel Buschek, Malin Eiband, Benjamin R. Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 254, 15 pages.Google ScholarDigital Library
- Abbas Zabihzadeh, Mohammad Ali Mazaheri, Javad Hatami, Mohammad Reza Nikfarjam, Leili Panaghi, and Telli Davoodi. 2019. Cultural differences in conceptual representation of “Privacy”: A comparison between Iran and the United States. The Journal of Social Psychology 159, 4 (2019), 357–370. https://doi.org/10.1080/00224545.2018.1493676 arXiv:https://doi.org/10.1080/00224545.2018.1493676PMID: 30095370.Google ScholarCross Ref
- Nima Zargham, Leon Reicherts, Michael Bonfert, Sarah Theres Voelkel, Johannes Schoening, Rainer Malaka, and Yvonne Rogers. 2022. Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma. In Proceedings of the 4th Conference on Conversational User Interfaces (Glasgow, United Kingdom) (CUI ’22). Association for Computing Machinery, New York, NY, USA, Article 3, 14 pages.Google ScholarDigital Library
Index Terms
- Onboarding Stages and Scrutable Interaction: How Experts Envisioned Explainability in Proactive Time Management Assistants
Recommendations
Scrutability of Intelligent Personal Assistants
UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and PersonalizationIntelligent personal assistants (IPAs) have become widely available, yet they remain primarily used for discrete, straightforward tasks. By contrast, both user studies and literature reviews indicate that IPAs of the future are to be personalised, ...
Voicing Suggestions and Enabling Reflection: Results of an Expert Discussion on Proactive Assistants for Time Management
CUI '23: Proceedings of the 5th International Conference on Conversational User InterfacesWhile voice-controllable Intelligent Personal Assistants (IPAs) have become widespread in recent years, they remain primarily reactive with rather constrained calendaring capabilities. Anticipating more adaptive and complex assistants in the future, we ...
Transparent, Scrutable and Explainable User Models for Personalized Recommendation
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information RetrievalMost recommender systems base their recommendations on implicit or explicit item-level feedback provided by users. These item ratings are combined into a complex user model, which then predicts the suitability of other items. While effective, such ...
Comments