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What ‘counts’ as explanation in social interaction?

Saul Albert∗, Hendrik Buschmeier, Katharina Cyra, Christiane Even, Magnus Hamann, Jakub Mlynář, Hannah Pelikan, Martin Porcheron, Stuart Reeves, Philippe Sormani & Sylvaine Tuncer†

Citation: Albert, S., Buschmeier, H. Cyra, K., Even, C., Hamann, M., Licoppe, C., Mlynář, J., Pelikan, H., Porcheron, M., Reeves, S., Rudaz, D., Sormani, P., Tuncer, S. (2023, November 6-7). What ‘counts’ as an explanation in  social interaction? 2nd TRR 318 Conference Measuring Understanding,  University of Paderborn, Paderborn, Germany.

Background

Measuring explainability in explainable AI (X-AI) usually involves technical methods for evaluating and auditing automated decision-making processes to highlight and eliminate potential sources of bias. By contrast, human practices of explaining usually involve doing explanation as a social action (Miller, 2019). X-AI’s transparent machine learning models can help to explain the proprietary ‘black boxes’ often used by high-stakes decision support systems in legal, financial, or diagnostic contexts (Rudin, 2019). However, as Rohlfing et al. (2021) point out, effective explanations (however technically accurate they may be), always involve processes of co-construction and mutual comprehension. Explanations usually involve at least two parties: the system and the user interacting with the system at a particular point in time, and ongoing contributions from both explainer and explainee are required. Without accommodating action, X-AI models appear to offer context-free, one-size-fits-all technical solutions that may not satisfy users’ expectations as to what constitutes a proper explanation.

What counts as an explanation?

If we accept that explanations are not simply stand-alone statements of causal relation, it can be hard to identify what should ‘count’ as an explanation in interaction (Ingram, Andrews, and Pitt, 2019). Research into explanation in ordinary human conversation has shown that explanations can be achieved through various practices tied to the local context of production cf. Schegloff, 1997. Moreover, explanations do not just appear anywhere in an interaction, but they are recurrently produced as responsive actions to fit an interactional ‘slot’ where someone has been called to account for something (Antaki, 1996). Sometimes explanations may also be produced as ‘initial’ moves in a sequence of action. In such cases, they are often designed to anticipate resistance and deal with. e.g., the routine contingencies that people cite when refusing to comply with an instruction (Antaki and Kent, 2012). Explanations as actions also perform and ‘talk into being’ social and institutional relationships such as doctor/patient, or teacher/student (Heritage and Clayman, 2010). Explainable AI systems, in this sense, become ‘accountable’ or ’transparent’ through their social uses (Button, 2003; Ehsan et al., 2021). We draw on concepts of explanation from Ethnomethodology and Conversation Analysis (Garfinkel, 1967; Garfinkel, 2002; Sacks, Schegloff, and Jefferson, 1974), Discursive Psychology (Edwards and Potter, 1992; Wiggins, 2016), and cognate fields like Distributed Cognition (Hutchins, 1995), and Enactivism (Di Paolo, Cuffari, and De Jaegher, 2018) to outline an empirical approach to explanation as a context-sensitive situated social practice (Suchman, 1987).

Explanations as joint actions Shared understanding is co-constructed through the achievement of coordinated social action (see e.g., Clark, 1996; Linell, 2009; Goodwin, 2017). Necessary and sufficient explanations cannot, therefore, be predefined by AI designers. Instead, explanation may be achieved through the achievement of joint actions with an AI in a specific context. If a system displays its capabilities in ways that match users’ expectations, they may achieve explanation (as contingently shared understanding) for all present intents and purposes. Explanation in this sense can never be considered complete – it could always be elaborated (cf. Garfinkel, 1967, pp. 73–75). While similar situations would involve predictability and regularity, this concept of explanation requires that participants jointly establish the relevant criteria and form for sufficient explanation with reference to the tasks and present purposes at hand, such as formulations of examples (Lee and Mlynář, 2023).

The self-explanatory nature of the social world Explanations are not only explicitly formulated, but are an inherent feature of the social world. Even without giving an explicit explanation, the design of an object provides ‘implicit’ explanations. Gibson (1979)’s concept of affordances, often used in system development, highlights that specific design features make specific actions relevant, e.g., a button that should be pressed, a lever that should be pulled (Norman, 1990). Situated social actions are also inherently recognisable (Levinson, 2013), even when mediated through AI such as in autonomous driving systems (Stayton, 2020). A slowly driving car will be recognised as not from the area (see, e.g., (Stayton, 2020)) and moving in certain ways can be recognised as, e.g., giving way to a pedestrian (Moore et al., 2019; Haddington and Rauniomaa, 2014). We could harness self-explanatory visibility (Nielsen, 1994) to design AI behaviours that are recognisable as specific social actions.

Miscommunication as explanation The practices of repair – the methods we use to recognise and deal with miscommunication (problems of speaking, hearing and understanding), as they occur in everyday interaction (Schegloff, Jefferson, and Sacks, 1977) – constitute pragmatic forms of explanation when they allow us to identify and resolve breakdowns of mutual understanding. For example, when someone says “huh?” in response to a ‘trouble source’ turn in spoken conversation, the speaker usually repeats the entire prior turn (Dingemanse, Torreira, and Enfield, 2013), whereas if the recipient had said “where?”, their response might only have solicited a repeat or reformulation of only the misheard place reference. These practices range from from tacit displays of uncertainty to explicit requests for clarification that solicit fully formed explanations and accounts (Raymond and Sidnell, 2019). These methods for real-time resolution of mutual (mis)understanding allows us, at least, to proceed with joint action in ways that establish and uphold explanation in action.

Explainability in action

This paper will use Conversation Analysis to examine episodes of Human-AI interactions, from a wide range of everyday interactional settings, and involving different technologies, user groups, and task orientations. Rather than attempting to establish a systematic or generalisable metric for explainability across interactional settings, the aim here is to encourage an extension of – and critical reflections on – our technical conceptualisation of explanation in X-AI.

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∗Corresponding author: s.b.albert@lboro.ac.uk. Order of authors is alphabetical.

†SA, MH: Loughborough University; HB: Bielefeld University; KC: University of Duisburg-Essen; CE: Ruprecht Karl University of Heidelberg; JM:HES-SO Valais-Wallis; HP: Linköping University; MP: Swansea University; SR: University of Nottingham; PS: University of Lausanne; ST: King’s College

The Atypical Interactant in a Smart Homecare Participation Framework

This paper contributes to research on the role of technology in ‘atypical’ interaction by examining a situation in which the technology takes on the stigma of atypicality. Building on our analysis, we argue that this approach provides a model for assistive technology research and development that moves away from a techno-medical model and focuses on how typicality (and atypicality) are achieved interactionally.

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Conversational User Interfaces in Smart Homecare Interactions: A Conversation Analytic Case Study

Saul Albert, Magnus Hamann, Elizabeth Stokoe

Abstract:

Policymakers are increasingly interested in using virtual assistants to augment social care services in the context of a demographic ageing crisis. At the same time, technology companies are market- ing conversational user interfaces (CUIs) and smart home systems as assistive technologies for elderly and disabled people. However, we know relatively little about how today’s commercially available CUIs are used to assist in everyday homecare activities, or how care service users and human care assistants interpret and adapt these technologies in practice. Here we report on a longitudinal conversation analytic case study to identify, describe, and share how CUIs can be used as assistive conversational agents in practice. The analysis reveals that, while CUIs can augment and support new capabilities in a homecare environment, they cannot replace the delicate interactional work of human care assistants. We ar- gue that CUI design is= best inspired and underpinned by a better understanding of the joint coordination of homecare activities

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White, G. W., Lloyd Simpson, J., Gonda, C., Ravesloot, C., & Coble, Z. (2010). Moving from Independence to Interdependence: A Conceptual Model for Better Understanding Community Participation of Centers for Independent Living Consumers. Journal of Disability Policy Studies20(4), 233–240. https://doi.org/10.1177/1044207309350561Post navigation

Wright, J. (2021). The Alexafication of Adult Social Care: Virtual Assistants and the Changing Role of Local Government in England. International Journal of Environmental Research and Public Health, 18(2), Article 2. https://doi.org/10.3390/ijerph18020812

Wright, J. (2023). Robots won’t save Japan: An ethnography of eldercare automation. ILR Press, an imprint of Cornell University Press.

An artificial turn in social interaction research?

Jakub Mlynář, Andreas Liesenfeld, Lynn de Renata Topinková, Wyke Stommel, Lynn de Rijk, and Saul Albert for the 6th Copenhagen Multimodality Day: Interacting with AI

The turn towards multimodality and embodiment in interaction research has yielded new terminology and representational schema in key publications (Nevile 2015). At the intersections between multidisciplinary fields, e.g., ethnomethodological and conversation analytic (EMCA) research exploring interactions between humans and ‘AI’, social robots, and conversational user interfaces, such methodological changes are even harder to track. How do these approaches to the meticulous, naturalistic study of technologies in (and of) social interaction reframe the key terms, schema and practices that constitute AI as a field of technosocial activity? Largely grounded in the EMCA Wiki bibliography, we map this emerging field and report on a bibliometric review of 90 publications directly relevant to EMCA studies of AI (broadly defined) including social robots and their components such as voice interfaces.

We found that the most works cited in the EMCA+AI corpus are classics from the canon of human interaction research (Garfinkel, Sacks, Schegloff, Goffman), including multimodality (Goodwin, Heath), human-machine interaction (Suchman), and STS (Latour). The most frequently cited texts are: Sacks, Schegloff and Jefferson’s (1974) ‘turn-taking paper’ (in 45% of items from the corpus), Garfinkel’s (1967) Studies (40%), and Suchman’s (1987) book (31%). Dealing specifically with AI from an EMCA perspective, Porcheron et al.’s 2018 paper on voice user interfaces is the most cited (11%). Apart from this one, two other texts feature as citation hubs: Alač’s (2016) and Pitsch et al.’s (2013) papers on social robots and embodiment. The study aims to provide a starting point for discussion about how concepts such as embodiment, agency and interaction are shared, used and understood through the practice of academic citation.

References 

Nevile, M. (2015). The Embodied Turn in Research on Language and Social Interaction. Research on Language and Social Interaction, 48(2), 121–151.

The interactional coordination of virtual and personal assistants in a homecare setting

Saul Albert, Magnus Hamann & Elizabeth Stokoe (for the 6th Copenhagen Multimodality Day), October 2021.

Abstract

Policymakers and care service providers are increasingly looking to technological developments in AI and robotics to augment or replace health and social care services in the context of a demographic ageing crisis (House of Lords, 2021; Kingston et al., 2018; Topol, 2019, pp. 54–55). However, there is still little evidence as to how these technologies might be applied to everyday social care situations (Maguire et al., 2021). This paper uses conversation analysis of ~100 hours of video recorded interactions between a disabled person, their virtual assistant (Alexa), and their (human) personal assistant to explore how routine care tasks are organized in a domestic setting. We focus on how the human participants organize conversational turn-space around ‘turns-at-use’ with the virtual assistant. Specifically, how turns-at-use ostensibly designed for the virtual assistant can recruit overhearing others. Further, we show how participants include the virtual assistant in their shared taskscape by, for example, putting ongoing activities and conversations on hold, visibly reorienting their bodies, or explicitly making themselves available for – or requesting – assistance when coordination trouble emerges between the machine-human dyad. Our findings show that virtual assistants expand the affordances of a homecare environment but do not replace the work of personal assistants.

References

Alač, M., Gluzman, Y., Aflatoun, T., Bari, A., Jing, B., & Mozqueda, G. (2020). How Everyday Interactions with Digital Voice Assistants Resist a Return to the Individual. Evental Aesthetics, 9(1), 51.

Amazon Echo. (2019). Amazon Alexa: Sharing is Caring. https://www.youtube.com/watch?v=225Wlg3pkdo

Archibald, M. M., & Barnard, A. (2018). Futurism in nursing: Technology, robotics and the fundamentals of care. Journal of Clinical Nursing, 27(11–12), 2473–2480. https://doi.org/10.1111/jocn.14081

Bedaf, S., Gelderblom, G. J., de Witte, L., Syrdal, D., Lehmann, H., Amirabdollahian, F., Dautenhahn, K., & Hewson, D. (2013). Selecting services for a service robot: Evaluating the problematic activities threatening the independence of elderly persons. 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), 1–6. https://doi.org/10.1109/ICORR.2013.6650458

Casey, D., Felzmann, H., Pegman, G., Kouroupetroglou, C., Murphy, K., Koumpis, A., & Whelan, S. (2016). What People with Dementia Want: Designing MARIO an Acceptable Robot Companion. In K. Miesenberger, C. Bühler, & P. Penaz (Eds.), Computers Helping People with Special Needs (pp. 318–325). Springer International Publishing. https://doi.org/10.1007/978-3-319-41264-1_44

Chappell, N. L., Dlitt, B. H., Hollander, M. J., Miller, J. A., & McWilliam, C. (2004). Comparative Costs of Home Care and Residential Care. The Gerontologist, 44(3), 389–400. https://doi.org/10.1093/geront/44.3.389

Dowling, S., Williams, V., Webb, J., Gall, M., & Worrall, D. (2019). Managing relational autonomy in interactions: People with intellectual disabilities. Journal of Applied Research in Intellectual Disabilities, 32(5), 1058–1066. https://doi.org/10.1111/jar.12595

García-Soler, Á., Facal, D., Díaz-Orueta, U., Pigini, L., Blasi, L., & Qiu, R. (2018). Inclusion of service robots in the daily lives of frail older users: A step-by-step definition procedure on users’ requirements. Archives of Gerontology and Geriatrics, 74, 191–196. https://doi.org/10.1016/j.archger.2017.10.024

Goodwin, C. (2000). Action and embodiment within situated human interaction. Journal of Pragmatics, 32(10), 1489–1522. https://doi.org/10.1016/S0378-2166(99)00096-X

Harmo, P., Taipalus, T., Knuuttila, J., Vallet, J., & Halme, A. (2005). Needs and solutions—Home automation and service robots for the elderly and disabled. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3201–3206. https://doi.org/10.1109/IROS.2005.1545387

House of Lords. (2021). Ageing: Science, Technology and Healthy Living (p. 132). House of Lords Science and Technology Select Committee. https://publications.parliament.uk/pa/ld5801/ldselect/ldsctech/183/183.pdf

Kachouie, R., Sedighadeli, S., Khosla, R., & Chu, M.-T. (2014). Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review. International Journal of Human–Computer Interaction, 30(5), 369–393. https://doi.org/10.1080/10447318.2013.873278

Kendrick, K. H., & Drew, P. (2016). Recruitment: Offers, Requests, and the Organization of Assistance in Interaction. Research on Language and Social Interaction, 49(1), 1–19. https://doi.org/10.1080/08351813.2016.1126436

Kingston, A., Comas-Herrera, A., & Jagger, C. (2018). Forecasting the care needs of the older population in England over the next 20 years: Estimates from the Population Ageing and Care Simulation (PACSim) modelling study. The Lancet Public Health, 3(9), e447–e455. https://doi.org/10.1016/S2468-2667(18)30118-X

Krummheuer, A. L., Rehm, M., & Rodil, K. (2020). Triadic Human-Robot Interaction. Distributed Agency and Memory in Robot Assisted Interactions. Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 317–319. https://doi.org/10.1145/3371382.3378269

Levine, D. M., Ouchi, K., Blanchfield, B., Diamond, K., Licurse, A., Pu, C. T., & Schnipper, J. L. (2018). Hospital-Level Care at Home for Acutely Ill Adults: A Pilot Randomized Controlled Trial. Journal of General Internal Medicine, 33(5), 729–736. https://doi.org/10.1007/s11606-018-4307-z

Maguire, D., Honeyman, M., Fenney, D., & Jabbal, J. (2021). Shaping the future of digital technology in health and social care. The King’s Fund. https://www.kingsfund.org.uk/publications/future-digital-technology-health-social-care

Share, P., & Pender, J. (2018). Preparing for a Robot Future? Social Professions, Social Robotics and the Challenges Ahead. Irish Journal of Applied Social Studies, 18(1). https://doi.org/10.21427/D7472M

Stokoe, E., Sikveland, R. O., Albert, S., Hamann, M., & Housley, W. (2020). Can humans simulate talking like other humans? Comparing simulated clients to real customers in service inquiries. Discourse Studies, 22(1), 87–109. https://doi.org/10.1177/1461445619887537

Topol, E. (2019). The Topol Review: Preparing the healthcare workforce to deliver the digital future (p. 103). Health Education England. https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf

Tuisku, O., Pekkarinen, S., Hennala, L., & Melkas, H. (2018). “Robots do not replace a nurse with a beating heart”: The publicity around a robotic innovation in elderly care. Information Technology & People, 32(1), 47–67. https://doi.org/10.1108/ITP-06-2018-0277

White, G. W., Lloyd Simpson, J., Gonda, C., Ravesloot, C., & Coble, Z. (2010). Moving from Independence to Interdependence: A Conceptual Model for Better Understanding Community Participation of Centers for Independent Living Consumers. Journal of Disability Policy Studies, 20(4), 233–240. https://doi.org/10.1177/1044207309350561

Putting wake words to bed

Magnus Hamann and I wrote a provocation paper for the third conference on Conversational User Interfaces 2021.

In it, we argue (hopefully provocatively), that voice user interface designers should stop using wake words like “Alexa” and “Hey Siri” that are crowding each other out of the audible environment of the smart home. Our point is that, as interface elements, wake words are misleading for users who seem to treat them like fully-fledged interactional summons, when they’re really little more than glorified ‘on’ buttons.

We got a surprisingly positive response from the technically-inclined audience at the conference. I found it surprising mostly because wake words are so ubiquitous and central to the branding and functionality of today’s voice interfaces that it seems hard to imagine them being phased out in favour of something more prosaic.

You can read the full paper on the ACM site, or a preprint here.

References

  1. Charles Goodwin. 2007. Interactive footing. In Reporting Talk, Elizabeth Holt and Rebecca Clift (eds.). Cambridge University Press, Cambridge, 16–46. DOI:https://doi.org/10.1017/CBO9780511486654.003
  2. Alexa Hepburn and Galina B Bolden. 2017. Transcribing for social research. Sage, London.
  3. William Housley, Saul Albert, and Elizabeth Stokoe. 2019. Natural Action Processing. In Proceedings of the Halfway to the Future Symposium 2019 (HTTF 2019), Association for Computing Machinery, Nottingham, United Kingdom, 1–4. DOI:https://doi.org/10.1145/3363384.3363478
  4. Razan Jaber, Donald McMillan, Jordi Solsona Belenguer, and Barry Brown. 2019. Patterns of gaze in speech agent interaction. In Proceedings of the 1st International Conference on Conversational User Interfaces – CUI ’19, ACM Press, Dublin, Ireland, 1–10. DOI:https://doi.org/10.1145/3342775.3342791
  5. Seung-Hee Lee. 2006. Second summonings in Korean telephone conversation openings. Language in Society. 35, 02. DOI:https://doi.org/10.1017/S0047404506060118
  6. Gene H Lerner. 2003. Selecting next speaker: The context-sensitive operation of a context-free organization. Language in Society. 32, 02, 177–201. DOI:https://doi.org/10.1017/S004740450332202X
  7. Ewa Luger and Abigail Sellen. 2016. “Like Having a Really Bad PA”: The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16), Association for Computing Machinery, New York, NY, USA, 5286–5297. DOI:https://doi.org/10.1145/2858036.2858288
  8. Robert J. Moore and Raphael Arar. 2019. Conversational UX design: A practitioner’s guide to the natural conversation framework. Association for Computing Machinery, New York, NY, USA.
  9. Clifford Nass and Youngme Moon. 2000. Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues 56, 1 (2000), 81–103. DOI:https://doi.org/10.1111/0022-4537.00153
  10. Hannah R. M. Pelikan and Mathias Broth. 2016. Why That Nao? In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI \textquotesingle16, ACM Press. DOI:https://doi.org/10.1145/2858036.2858478
  11. Danielle Pillet-Shore. 2018. How to Begin. Research on Language and Social Interaction 51, 3 (July 2018), 213–231. DOI:https://doi.org/10.1080/08351813.2018.1485224
  12. Martin Porcheron, Joel E Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice Interfaces in Everyday Life. In Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems – CHI’18, ACM Press. DOI:https://doi.org/10.1145/3173574.3174214
  13. Stuart Reeves, Martin Porcheron, and Joel Fischer. 2018. “This is not what we wanted”: designing for conversation with voice interfaces. Interactions 26, 1, 46–51. DOI:https://doi.org/10.1145/3296699
  14. Harvey Sacks. 1995. Lectures on conversation. Wiley-Blackwell, London.
  15. Emanuel A Schegloff. 1968. Sequencing in Conversational Openings. American Anthropologist 70, 6, 1075–1095. DOI:https://doi.org/10.1525/aa.1968.70.6.02a00030
  16. Emanuel A Schegloff. 1988. Presequences and indirection: Applying speech act theory to ordinary conversation. Journal of Pragmatics 12, 1 (1988), 55–62.
  17. Emanuel A Schegloff. 2007. Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge University Press, Cambridge.

Digital transcription for EM/CA research

I have put my introduction to digital transcription workshop materials and tutorials online, here’s a little blog outlining some of the reasons I started developing the workshop, and how I hope researchers will use it.

There are very few – if any – software tools designed specifically for conversation analytic transcription, partly because so few conversation analysts use them, so there’s not really a ‘market’ for software developers to cater to.

Instead, we have to make do with tools that were designed for more generic research workflows, and which often build in analytic assumptions, constraints and visual metaphors that don’t necessarily correspond with EM/CA’s methodological priorities.

Nonetheless, most researchers that use digital transcription systems choose between two main paradigms.

  1. the ‘list-of-turns’-type system represents interaction much like a Jeffersonian transcript: a rendering of turn-by-turn talk, line by line, laid out semi-diagrammatically so that lines of overlapping talk are vertically aligned on the page.
  2. the ‘tiers-of-timelines’ system uses a horizontal scrolling timeline like a video editing interface, with multiple layers or ‘tiers’ representing e.g., each participant’s talk, embodied actions, and other types of action annotated over time.

 

A key utility of both kinds of digital transcription systems is that they allow researchers to align media and transcript, and to use very precise timing tools to check the order and timing of their analytic observations.

I used these terms to describe this distinction between representational schema in a short ‘expert box’ for Alexa Hepburn and Galina Bolden’s excellent (2017) book Transcribing for Social Research entitled “how to choose transcription software for conversation analysis“, where I tried to explain what is at stake in choosing one or the other type of system .

For the most part, researchers choose lists-of-turns tools when their analysis is focused on conversation and audible turn-space, and tiers-of-timelines when their analysis focuses on video analysis of visible bodily action.

The problem for EM/CA researchers working with both these approaches, however, is that neither representational schema on its own, (nor any schema save whatever schema may have been constituted through the original interaction itself), is ideal for exploring and describing participants’ sense-making processes and resources.

Tiers-of-timelines representations are great for showing the temporal unfolding of simultaneous action, but it is hard to read more than a few seconds of activity at a glance. By contrast, lists-of-turns use the same basic schema as our well-practiced, mundane reading abilities to scan a page of text and take in the overall structure of a conversation, but reduce the fine-grained timing and multi-activity organization of complex embodied activities.

In any case, neither of these representational schema, nor any currently available transcription tools adequately capture the dynamics of movement in the way that, for example, specialized graphical methods and life drawing techniques were developed to achieve (although our Drawing Interactions prototype points to some possibilities).

The reason I put this digital transcription workshop together was to combine existing, well-used software tools for digital transcription from both major paradigms, and to show how to work on a piece of data using both approaches. It’s not intended as a comprehensive ‘solution’, and there are many unresolved practical and conceptual issues, but I think it gives researchers the best chance to address their empirical concerns to help break away from the conceptual and disciplinary constraints that come from analyzing data using one, uniform type of user interface.

The workshop materials include slides (so people can use them to teach collaborators/students) as well as a series of short tutorial videos accompanying each practical exercise in the slides, along with some commentary from me.

My hope is that researchers will use and improve these materials, and possibly extend them to include additional tools (e.g., EXMARaLDA project tools, with which I’m less familiar). If you do, and you find ways to improve them with additional tips, hacks, or updated instructions that take into account new versions, please do let me know.

Moving into step: The embodiment of social structures of action

The abstract for a forthcoming article by myself and Dirk vom Lehn, soon to be liberated from the stalled pandemic year R&R cycle. Draft available if you’re willing to give feedback!

Abstract 

While dance has often featured in sociological theory, there are relatively few empirical studies that explore the social practices through which people learn to dance together. This paper takes as its point of departure the way that partner dance is often featured as a metaphor to illustrate theories about social order and interaction. We examine a corpus of video data gathered as part of a day-long workshop and explore how novice dancers learn to perform some of the basic steps of a social dance in time with their partner and with the rhythmical environment. The analysis shows how dancers use rhythm, bodies, language and other resources to organize their social interactions and shows how ethnomethodology and conversation analysis provide a critical standpoint for examining sociological theories about the relationship between the body and the social.  

Keywords: ethnomethodology, conversation analysis, multimodality, dance, culture, 

Three meeting points between CA and AI

I gave this keynote at the first European Conference on Conversation Analysis (ECCA 2020), which, due to C-19, had to be delivered as a video instead of a stand-up talk.

I tried to make a mix between a film essay and a research presentation of work-in-progress, so it didn’t always work to put references on every slide. I’ve added them below with links to the data used where available.

Abstract

Sacks’ (1963) first published paper on ‘sociological description’ uses the metaphor of a mysterious ‘talking-and-doing’ machine, where researchers from different disciplines come up with incompatible, contradictory descriptions of its functionality. We may soon find ourselves in a similar situation to the one Sacks describes as AI continues to permeate the social sciences, and CA begins to encounter AI either as a research object, as a research tool, or more likely as a pervasive feature of both.

There is now a thriving industry in ‘Conversational AI’ and AI-based tools that claim to emulate or analyse talk, but both the study and use of AI within CA is still unusual. While a growing literature is using CA to study social robotics, voice interfaces, and  conversational user experience design (Pelikan & Broth, 2016; Porcheron et al., 2018), few conversation analysts even use digital tools, let alone the statistical and computational methods that underpin conversational AI. Similarly, researchers and developers of conversational AI rarely cite CA research and have only recently become interested in CA as a possible solution to hard problems in natural language processing (NLP). This situation presents an opportunity for mutual engagement between conversational AI and CA (Housley et al., 2019). To prompt a debate on this issue, I will present three projects that combine AI and CA very differently and discusses the implications and possibilities for combined research programmes.

The first project uses a series of single case analyses to explore recordings in which an advanced conversational AI successfully makes appointments over the phone with a human call-taker. The second revisits debates on using automated speech recognition for CA transcription (Moore, 2015) in light of significant recent advances in AI-based speech-to-text, and includes a live demo of ‘Gailbot’, a Jeffersonian automated transcription system. The third project both uses and studies AI in an applied CA context. Using video analysis, it asks how a disabled man and his care worker interact while using AI-based voice interfaces and a co-designed ‘home automation’ system as part of a domestic routine of waking, eating, and personal care. Data are drawn from a corpus of ~500 hours of video data recorded by the participants using a voice-controlled, AI-based ‘smart security camera’ system.

These three examples of CA’s potential interpretations and uses of AI’s ‘talking-and-doing’ machines provide material for a debate about how CA research programmes might conceptualize AI, and use or combine it with CA in a mutually informative way.

Videos (in order of appearance)

The Senster. (2007, March 29). https://www.youtube.com/watch?v=wY85GrYGnyw

MIT AI Lab. (2011, September 25). https://www.youtube.com/watch?v=hp9NHNKTV-M

Keynote (Google I/O ’18). (2018, May 9). https://www.youtube.com/watch?v=ogfYd705cRs

Online Data

Linguistic Data Consortium. (2013). CABank CallHome English Corpus [Data set]. Talkbank. https://ca.talkbank.org/access/CallHome/eng.html

Jefferson, G. (2007). CABank English Jefferson NB Corpus [Data set]. TalkBank. https://doi.org/10.21415/T58P4Z

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Collecting data from streaming cameras with youtube-dl

I’ve been fascinated by a live camera stream showing a UK street since the start of the lockdown on the 23rd March 2020 because it’s shown how pedestrians interpret the 2m physical distancing rule.

Some of the data from this camera was incorporated into a very nice ROLSI blog post by Eric Laurier, Magnus Hamann and Liz Stokoe that I helped with about the emergence of the ‘social swerve’.

I thought others might find it useful to read a quick how-to about grabbing video from live cameras – it’s a great way to get a quick and dirty bit of data to test a working hunch or do some rough analysis.

There are thousands of live cameras that stream to youtube, but it can be a bit cumbersome to capture more than a few seconds via more straightforward screen capture methods.

NB: before doing this for research purposes, check that doing so is compliant with relevant regional/institutional ethical guidelines.

Step 1: download and configure youtube-dl

Youtube-dl is a command line utility, which means you run it from the terminal window of your operating system of choice – it works fine on any Unix, on Windows or on Mac Os.

Don’t be intimidated if you’ve never used a command line before, you won’t have to do much beyond some copying and pasting.

I can’t do an installation how-to, but there are plenty online:

Mac:

https://www.youtube.com/watch?v=NhOkYXB2_QQ

Windows:

https://www.youtube.com/watch?v=xyPAaYq3H9E

I’ll assume that if you’re a Unix user, you know how to do this.

Step 2: copy and paste the video ID from the stream

Every Youtube video has a video ID that you can copy from the address bar of your browser. Here’s the one I used for the blog post mentioned above – which we’ve affectionately nicknamed the ‘kebab corpus’. The video ID is circled in red:

Step 3: use youtube-dl to begin gathering your video data

This bit is a little hacky – as in not really using the software as intended or documented, so I’ve created a short howto video. There might be better ways. If so, please let me know!

As I mention in that video – probably best not to leave youtube-dl running for too long on a stream as you might end up losing your video if something happens to interrupt the stream. I’ve captured up to half an hour at a time.

It’s possible to create scripts and automated actions for a variety of operating systems to do this all for you on a schedule – but if you need extensive video archives, I’d recommend contacting the owner of the stream to see if they can simply send you their high quality youtube archives.