Talk given by Saul Albert and Dirk vom Lehn, Gothenburg, Department of Philosophy, Linguistics, Theory of Science, 22nd September 2026

Abstract

Every cultural practice has standards: shared, often unspoken norms that let members recognise when something has been done well, badly, or not quite right. Sociologists and philosophers of aesthetics have long debated where such standards reside, whether in individual taste, institutional authority, or tacit embodied knowledge. Partner dance offers a useful site for addressing this question empirically, since its aesthetic norms must be visibly produced and assessed in real time. This talk draws on video recordings of a beginners’ Lindy Hop class, analysed using ethnomethodology and multimodal conversation analysis. I show how teachers and students assemble the idioculture of the dance through sequences of instruction, demonstration, attempt and correction. Steps are rarely learned as isolated forms. They become accountable as parts of a social structure of action: timed to the music, coordinated with a partner, and fitted to the local expectations of the class. Novices’ early ‘errors’ are analytically revealing because they expose the practical work of alignment that experienced dancers take for granted. I argue that aesthetic standards are best understood as interactional achievements, located in the observable details of coordinated movement.

Transcripts

Download transcripts and follow along with the analysis.

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Lauren Hall, Elizabeth Peel & Saul Albert

Download transcripts | Keywords: Agency, Virtual Assistants, Participation, Footing, Technology

How should we understand participation and agency in talk involving voice interfaces? ‘Conversational technologies’ are proliferating both in everyday life and, by extension, in conversation analytic research (Stokoe et al., 2024). Interactional studies involving a wide range of technologies have tended to simply transpose conversation analytic frameworks and methods onto the analysis of talk with machines (Mlynář et al., 2024). As Suchman’s (2023, p. 4) critique points out, uncritically adopting the framing of these technologies as social actors risks contributing to the mystification and rhetorical reproduction of ‘AI’ as a “singular and autonomous agent”. This paper contributes to debates emerging within ethnomethodological and conversation analytic (EM/CA) approaches to AI that interrogate our conceptual frameworks and analytic vocabularies for describing these supposed interactions and their ostensible ‘participants’ using examples of the multimodal organisation of talk with and around voice user interfaces.

A range of critical perspectives within EM/CA take issue with a key paradigm in human-computer interaction (HCI) that assumes users treat ‘Computers As Social Actors’ (CASA) (Nass et al., 1994). For example, CASA explains anthropomorphic use of politeness markers such as “please” and “thank you” as ‘mindless’ spill-overs of social rules into talk with machines (Nass & Moon, 2000). Reeves and Porcheron (2023) argue that the CASA paradigm has led to the misplaced ‘agentification’ of machines, misleading reifications of ‘conversation’ in design (Reeves, 2019), and analytic misrepresentations of ‘AI’ that obscure how sense-making is achieved by the situated, interactional work of human users. Habscheid et al., (2023) respecify the ontological concept of agency in HCI as an interactional achievement whereby users’ interactions place machines on a spectrum of being treated as more or less agentic. Similarly, Pelikan et al., (2022)  track how robots can be interactionally constructed as having sequentially and pragmatically differentiated types of ‘autonomous’, ‘hybrid’, ‘ascribed’, ‘potential’, or ‘non’-agency.

This paper contributes to EM/CA respecifications of participation and agency in talk involving machines by building on Goodwin’s (2007) concept of interactive footing. Goodwin extended Goffman’s (1981) dramaturgic taxonomy of agentic roles or ‘footing’ in talk by highlighting the co-constructed, embodied nature of social interaction. Interactive footing provides a framework for the study of participation that develops from a structural typology of participants (e.g., speaker, hearer, animator, principal, etc.) embedded in speech, to shift focus onto the temporal unfolding of joint actions. Drawing on a corpus of video-recordings collected by users of smart speakers in their homes, we focus on practices through which users invoke, implicate, or may exclude virtual assistants from talk and embodied action, both solo and with co-present others. For example, in Extract 1 we see Annabelle, who lives alone, rummaging in her sewing box after chastising her virtual assistant Alexa for not playing a particular song.

Extract 1:

On line 4, Annabelle first invokes Alexa in third person, as an absent other, musing on having been ‘rude’ to her, then topicalizes her ‘programming’ as a source of trouble in lines 7-8. In line 10, she then shifts footing from talking about Alexa to using her name, though not to activate the device or solicit a response. Here Annabelle invokes Alexa, ‘off the record’, turning away from the device, insulting her as a “silly old woman” before shifting back to ‘self-talk’ about Alexa and musing on her troubles (lines 10-13).

This paper proposes virtual footing as a way of capturing the multimodality and dynamism of Goodwin’s (2007) extension of Goffman’s participation frameworks, while highlighting users’ clearly ‘mindful’ practices for implicating virtual assistants in talk. Our analysis shows how users themselves can deflate the ‘uncontroversial thingness’ of AI, as Suchman (2023) puts it, by reflexively exploiting and satirising the limitations of such technologies as a resource for participation and distributed agency in interaction.

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Download Transcript | Keywords: Virtual Assistants; AI; Wake Words; Summons; Turn design

‘Wake words’ such as “Alexa”, “OK Google” and “Hey Siri” have become commonplaces within everyday talk, used to ‘summon’ virtual assistants on ‘smart’ speakers and phones. However, relatively little is known about how wake words are designed and produced in everyday settings and interaction (though see Albert & Hamann, 2021; Leblebici, 2024). Wake words, as an interface design feature, aim to protect user privacy by monitoring the local audio environment, then uploading and processing data remotely only after detecting an utterance of that specific word. Systematic conversation analytic research into voice interfaces in everyday life has tended to follow the pioneering study design of Porcheron et al. (2018), which designed a ‘conditional voice recorder’ (CVR) to capture a minute of audio only after detecting a ‘wake word’ (see e.g., Barthel et al., 2023; Reineke & Helmer, 2024; Waldecker et al., 2024), mirroring the design of the ‘wake word’ system. However, this design leaves many wake words, and the embodied conduct associated with subsequent or unsuccessful ‘activations’ only partially available for systematic study. Drawing on a collection of over 100 hours of continuous video recordings (British English), gathered by smart speaker users in their own homes, this paper focuses on users’ multimodal practices for pursuing responses from—or abandoning interaction with—wake word activated virtual assistants.  Preliminary findings show that wake words, when done as a summons and/or a pursuit or ‘second’ summons, are designed with systematic variation and sequential specificity.

For example, in Extract 1, below, the arrows indicate Ted summoning Alexa, his virtual assistant, four times in quick succession. Prior to the start of this extract, Ted had used Alexa to turn on a heater via a connected ‘smart plug’, and then to set a ten-minute timer while his carer Anna waits in the next room for him to finish using his commode.

In this extract we see two summons/request turns produced as both iteratively ‘first’ and sequentially ‘initial’. Firstly, in line 1, Ted’s first initial summons is done with slightly rising intonation. Ted sits with his back to Alexa, unable to monitor the ‘wake light’ that shows when the wake word has successfully activated the device. Alexa’s wake light does not turn on, nor does Alexa enact nor respond to Ted’s ‘turn off heater’ request in line 3. Secondly, in line 17, Ted summons “Alexa:¿” at line 17 with similarly upward final intonation, then requests Alexa to ‘Call Anna’. Again, this is the first iteration of this summons/request pair in this extract. Contrast these two ‘first’, initially positioned iterations of ‘Alexa’ with the two intervening summonses. In line 5, Alexa sounds Ted’s ten-minute timer alarm, then in line 6, Ted summons Alexa again, though this time in a responsive sequential position in relation to the ‘initial’ sounding of the alarm, and this “Alexa.” is produced with a downward intonation. When Ted does a second iteration of his—initially failed—’turn off heater’ summons/request sequence (lines 11-13) the wake word “Alexa:,” is produced with a more level intonation.

This illustrates a phenomenon observed across our data set: there are systematic variations in the design of sequentially and/or iteratively initial wake words.  This systematicity has several key implications. Firstly, it suggests that wake words, as Schegloff (1996) shows about person references, can be designed as either ‘initial’ or ‘subsequent’; that they can be done in sequentially/iteratively initial/subsequent positions; and that these variations constitute a interactionally rich resource. Secondly, where a device ‘fails’ to detect initial wake words, to ‘understand’ initial requests, and following failed second iterations, wake words can be done as complaints, insults, and blamings. Wake words thus constitute an interactional resource that, especially in multi-party settings, can mobilize devices in ways that implicate them in the moral order [AUT25] [SA6] of social interaction.

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References

Albert, S., & Hall, L. (2024). Distributed agency in smart homecare interactions: A conversation analytic case study. Discourse & Communication, 18(6). https://doi.org/10.1177/17504813241267059

Albert, S., & Hamann, M. (2021). Putting wake words to bed: We speak wake words with systematically varied prosody, but CUIs don’t listen. In CUI 2021: 3rd Conference on Conversational User Interfaces (pp. 1–5). Association for Computing Machinery. https://doi.org/10.1145/3469595.3469608

Barthel, M., Helmer, H., & Reineke, S. (2023). First users’ interactions with voice-controlled virtual assistants: A micro-longitudinal corpus study. In A. Lücking, C. Mazzocconi, & D. Verdonik (Eds.), Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue (SemDial) (pp. 105–116). University of Maribor.

Barthel, M. (2026, March 25–27). Methodological reflections on the use of conditional voice recorder data for (micro-)longitudinal analyses of human–voice agent interaction [Paper presentation]. 25th Conference on Discourse and Conversation Analysis (25. Arbeitstagung zur Gesprächsforschung), Mannheim, Germany.

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Garfinkel, H., & Wieder, D. L. (1992). Two incommensurable, asymmetrically alternate technologies of social analysis. In G. Watson & R. M. Seiler (Eds.), Text in context: Contributions to ethnomethodology (pp. 175–206). Sage.

Hall, L. (2025). Virtual assistants in smart homecare: An interactional qualitative analysis [Thesis, Loughborough University]. https://doi.org/10.26174/thesis.lboro.28730834.v1

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Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice interfaces in everyday life. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18) (Article 640, pp. 1–12). Association for Computing Machinery. https://doi.org/10.1145/3173574.3174214

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Saul Albert, [email protected], Loughborough University

Elliott Hoey, [email protected], Vrije Universiteit Amsterdam

Chase W. Raymond, [email protected], University of Colorado Boulder

This is a blog version of a talk I gave with Elliott Hoey and Chase Raymond at the International Conference on Conversation Analysis (ICCA) 2026 in Edmonton on the 28th June 2026. I’ve kept close to what I said on the day, and reproduced a few of the slides. It builds on our 2025 paper, The Case for Open Conversation Analysis Data (Albert, Hoey & Raymond, 2025). Full reference list is below.

Data sharing is not new to conversation analysis. It is one of the things that made CA possible. The panel’s question was how to adapt to the contemporary language of open and FAIR data, and the new challenges these bring for CA. Our answer is that CA’s data sharing is bootstrapped and constitutive, not policy-led, so we can understand, extend and adapt our own endogenous data-sharing practices rather than replicating expensive, policy-led open science wholesale.

Sharing data made CA possible

“Gail Jefferson’s transcription system, and her circulation of recordings and transcripts had a remarkable impact in bootstrapping the common culture of CA, and making sure it was founded in commonly witnessed and verifiable fact.”
— Heritage (2017, p. 181)

Heritage’s historical point can be read as a methodological one. Jefferson’s transcription system, and her habit of copying recordings and circulating transcripts, did a particular kind of field-building work. Along with data-session practices, it ensured that the field’s culture was founded in shared material people could witness and check for themselves. Sharing data was not a nicety. It was a constitutive methodological necessity, and that is in contrast to some ‘open data’ practices.

Different histories and logics of ‘data sharing’ and ‘open data’

These two have very different origins and characters. Without ever explicitly labelling it ‘open data’, CA went from Jefferson’s transcripts and tapes, to Goodwin’s CD-ROMs, to Schegloff’s early website; the data session as a working method; and the evidential norm of publishing detailed, re-analysable transcripts. That logic is constitutive.

The open science and FAIR tradition (findable, accessible, interoperable, reusable) has different roots, in free software licensing that became an ideological commitment in the 1990s. It values standardised formats and metadata, machine-readability for discovery and reuse, and large-scale distributed collaboration. Those are reasonable aims, but they were defined for other kinds of data and other kinds of work. Open-data definitions were modelled on early free software licences: written to keep source code out of commercial enclosure and to maintain shared infrastructure. That logic is policy-led.

CA’s sharing is differently endogenous and constitutive. So as we approach ‘open CA data’, we need to keep these differences in mind before deciding what to borrow.

What CA’s forms of ‘openness’ have enabled

Re-analysable primary data as an evidential norm allows for ‘disagreeable’ (if not falsifiable) findings. As Sacks put it:

“[O]thers could look at what I had studied and make of it what they could, if, for example, they wanted to be able to disagree with me.”
— Sacks (1984, p. 26)

It shows up in particular practices of data preparation, data sessions, publication and dissemination. The benefits are transparency, rigour and accountability across the research cycle, and community-building and training: shared corpora and familiarity are how new analysts learn to see and to explain. The data session is the performance of this kind of openness and disagreeableness: a clip and a transcript in front of a room, analysis done in public on the same material, where even beginners can hang on to an observation and disagree with an expert. That is openness in practice, before any explicit ‘open data policy’.

The limits of CA’s ‘openness’

CA’s informal openness can also exclude. Access can run along existing networks (Hoey & Raymond, 2022). Consent is ethically ambiguous, often implicit and contingent, though see recent efforts to make legacy data available in redacted form. And there is a representation problem: a small set of legacy recordings circulates privately and is reanalysed for decades, producing a narrow ‘canon’ that comes to be seen as representative of specific phenomena, and biased toward American and British English. This matters for what we can claim: our sense of what is general about interaction could become narrower than we would hope. A more intentional approach to open CA data could help to address some of this.

A partial landscape of online EM/CA corpora

In each case there are different configurations of who can get the data, under what licence, in what transcription format, and on what consent basis. CABank/TalkBank, FOLK, CLAPI, the classic legacy data, and our own CLDI all answer them differently. Here I want to focus on the CLDI, because it is ‘found’ data, and that status brings particular opportunities and challenges.

Example: the Corpus of Language Discrimination in Interaction (CLDI)

The CLDI is 80+ short videos found online of people being policed for the language they use in public space (Raymond et al., 2025). It is openly licensed (CC-BY-SA), catalogued on the EMCAwiki, and hosted on OSF, and we built it to support collection-based study, comparison across cases, teaching and community engagement. Each recording comes with ethnographic notes as well as links to sources, often including lay analysis, comments and interpretations.

One case, in a Walnut Creek café, is one where a woman objects to customers speaking Korean, tells them to speak English, and it escalates to the police being called. The recording posted online is what makes the episode analysable. It was not recorded by us, and it was not shared with consent. That raises three specific challenges.

Challenge 1: found ‘open data’ and informed consent

Online data does not formally require consent, at least at Loughborough’s ethics board. But in other open-data projects we can rely on participants’ own endogenous practices for doing data privacy. Mondada (2014) describes members’ methods of anonymisation: moving out of shot, lowering the voice, whispering, disguising sensitive talk. In these ‘found’ recordings, participants cannot always manage their exposure or (re)design what they say and do in situ; the camera-holder does a lot of that work. Confidentiality and exposure are produced in the interaction, not only applied later by the researcher.

So the ethical burden of such choices shifts onto the analyst, and each clip requires judgement. In our data, people were orienting to being on camera, aware that the clips were recorded to be archived, uploaded and reshared, but it was also clear (see image) that they were not necessarily happy about this. This does contravene some of the emerging field norms and principles set out by Joyce et al. (2025).

Challenge 2: ‘openness’ in CA data transcripts

There are two senses in which a transcript is ‘open’. One is the open data format: TEI/XML-compatible, shareable, re-analysable, released under an open licence, and machine-readable. The other is the openness of the transcription system itself: Jeffersonian transcription balances descriptive richness with intuitive simplicity, and is mutable given the phenomena in question.

Open licensing is straightforward. Strict machine-readability is not always compatible with how we transcribe. Take overlapping talk, a breathy laugh, a stretched sound: Jeffersonian shows these at a glance, where CHAT might replace a laugh with “&laughs;”, and a strict machine schema often loses exactly the ambiguities and questions that explain why we transcribed it in the first place (Albert, 2020; Ayaß, 2015). Until there are more standardised and coherent approaches to these issues, we went for publishing in as many formats as we reasonably could: the same CLDI transcript in CLAN, DOTE and ELAN.

Challenge 3: the costs of standardisation

CA’s methods remain ‘open’ to capture the shifting indexicality of members’ methods in various ways. Combining cross-corpus collections and re-transcribing to track the different ways participants display their orientations is a key CA method. This mutability is not methodological sloppiness; it is a purposeful choice that avoids premature formalisation of findings and methods.

We saw the tension in the CLDI’s own metadata: our ‘challenger’, ‘target’ and ‘employee’ categories are specific to the phenomenon of language discrimination that the corpus was built to study. The same recordings could be taken up for almost any question: service encounters, multi-party gaze, bystander intervention, second-language instruction. Our choices do not constrain, and should not pre-determine, the range of uses the data can be put to, but the risk of standardising is that categories harden into a fixed schema that tells us what the data is ‘about’ a priori. We weren’t sure how to deal with this, but kept things open by cataloguing the CLDI on the community-editable EMCAwiki, even though the files live on OSF.

Tools, digitisation, and the WorkBench

Very few tools are actually built for CA, so most need adapting. When we interviewed 20+ CA researchers, most were using Word, QuickTime and Excel (Hofstetter & Albert, 2024). Platforms anticipate uses and can foreclose others: TalkBank is largely used as a resource for re-transcription rather than as a CA repository; ELAN won’t easily keep something as basic as underlining for emphasis, or hand you a data-session-ready transcript. DOTE is the exception, built for CA, supporting Jeffersonian and Mondadian transcription.

One nice example of a CA-specific workflow is much older: the WorkBench, created by Gene Lerner with Schegloff’s expert-user help in the late 1980s and early 1990s. It was one of the first digital transcription tools that let you play synchronous sound while creating and viewing a transcript. The example here uses what might be a familiar piece of data, a digitised version of the Santa Barbara ‘Ladies’. You can see the transcript ‘data card’, catalogued neatly and filed in a collection; a contextual note and a pop-up ‘post-it’ (digitised versions of Harvey Sacks’s notes on the physical transcripts); and codings at the left, like the location of other-initiation. Schegloff, coding things. Heretical?

Alongside these digital equivalents of analogue work practices, the WorkBench has a feature they call ‘Link Rings’, designed to capture proto-collections before the phenomenon in question has a name. Schegloff and Lerner describe recognising a possible phenomenon while going through data, then seeing another one, or just grabbing at interesting things you can return to later. Schegloff likened it to the brass-ring carousel on Santa Monica pier: as you go round your data again and again, you spot some regularity that you want to grab and store ad hoc as you go along. You can’t get more situated and CA-specific than a feature based on the metaphor of a ride on the Santa Monica pier.

Next steps: bootstrapping open CA data as method

A few take-aways, and some proposed next steps for how we might bootstrap our common culture for open CA data. What is online, and what is shareable, is not a neutral sample: it steers topic choice and researcher access, so sensitive settings or minority languages may be studied less. Publishing large, openly licensed corpora of recorded interaction is not a neutral act in a political economy of data where every available kilobyte is being gobbled up by LLMs (Jones, 2024). Non-commercial licensing (TalkBank, FOLK, CLAPI) is one response, though can it be enforced?

What we tentatively propose:

• Develop shared standards, platforms and tools for open CA data, owned, governed, and funded by the CA community.
• Use them to widen access and participation, and to broaden who and what is represented in our corpora.
• Keep transcription legible and faithful to the practice, not only machine-readable.
• Treat consent, anonymisation and licensing as methodological questions, handled differently for found data and for data we generate ourselves.
• Throughout, stay critically engaged with what openness does.

The aim is to extend Jefferson’s habit of circulation and the way it bootstrapped the common culture of CA, not to simply apply open science norms and practices that may efface the specifics of CA methods, epistemological commitments, and practices.

An example: ad-hoc pedagogical corpora (low/no funding)

One examples of this kind of attitude of ‘bootstrapping’ communal culture comes from two community resources, neither a well-funded project. EMCA4RJ keeps an open data library and a sample syllabus, built to diversify the data we teach with and to make us examine our own disciplinary culture at the same time; the invitation to download, use and extend it stands. And ORCA, the Open Repository for CA at Loughborough, which I am currently worknig on: students collect clips from consented open recordings, transcribe them incrementally (verbatim, then Jeffersonian, then Mondadian, in DOTE), then refine and pool transcripts across cohorts. Teaching becomes a way of producing open data, not just consuming it. The ethics are complex, and a public release is still pending.

A caution

“All of this is an infrastructure for research. It’s not research … to avail ourselves of the best infrastructural support we can mobilise, without letting it mobilise us … the serious intellectual work still has to get done by humans, not machines.”
— Schegloff & Lerner (WorkBench launch talk, UCLA, 1990)

As Schegloff and Lerner (1990) point out in their launch talk for the WorkBench, we still have to do the hard work of data collection and analysis – there’s no chance AI is going to replace CA researchers yet!

Links and thanks

• The CLDI: https://emcawiki.net/CLDI
• Open-data study protocols (USA/UK templates): https://emcawiki.net/Example_CA_Study_Protocols
• EMCA4RJ data library: http://emca4rj.conversationanalysis.org
• The paper this talk builds on: Albert, Hoey & Raymond (2025), The Case for Open Conversation Analysis Data, ROLSI. https://doi.org/10.1080/08351813.2025.2484992

Thanks to Sarah M. Adams, Natalie Grothues, Jacob Henry, Olivia H. Marrese, Megan Pielke, Emily Reynolds and Regina Gayou Tom.

How to cite this talk

Albert, S., Raymond, C. W., & Hoey, E. M. (2026, June 28). Bootstrapping the common culture of CA: Open CA data as method [Conference presentation]. International Conference on Conversation Analysis (ICCA) 2026, Edmonton, Alberta, Canada.

Keywords: Open Data; Conversation Analysis; Methodology; Technology; Transcription

References

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Joyce, J. B., Benwell, B., Parry, R., & Rhys, C. S. (2025). Accessing and using data without informed consent: Guiding principles from conversation analysis. Research on Language and Social Interaction, 58(2), 113–120. https://doi.org/10.1080/08351813.2025.2484986

Joyce, J. B., Douglass, T., Benwell, B., Rhys, C. S., Parry, R., Simmons, R., & Kerrison, A. (2023). Should we share qualitative data? Epistemological and practical insights from conversation analysis. International Journal of Social Research Methodology, 26(6), 645–659. https://doi.org/10.1080/13645579.2022.2087851

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Raymond, C. W., Albert, S., Hoey, E. M., Adams, S. M., Grothues, N., Henry, J., Marrese, O. H., Pielke, M., Reynolds, E., & Gayou Tom, R. (2025). Language policy as interactional practice in everyday public space: The corpus of language discrimination in interaction. Language. https://doi.org/10.1353/lan.0.a952926

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In 2020 I did a talk entitled ‘Three meeting points between Conversation Analysis and AI’ – this is a one-slide updated lit review. A lot has happened!

References

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Albert, S. (2020). Three meeting points between conversation analysis and artificial intelligence [Keynote address]. European Conference on Conversation Analysis (ECCA), online. /blog/three-meeting-points-between-ca-and-ai/

Deppermann, A., & Gubina, A. (2025). Coding actions in social interaction: Potentials and problems. Research on Language and Social Interaction, 58(3), 258–280. https://doi.org/10.1080/08351813.2025.2528494

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Annual Finnish Social Psychology Conference, Tampere, 8 May 2026

Abstract

Public and scientific discourses about ‘AI’ treat ‘artificiality’ as an inherent property of machines, and ‘intelligence’ as an inherent property of persons. Drawing on conversation analysis and discursive psychology, I will argue that both are better understood as interactional achievements, produced turn-by-turn in the practical work of participants. This shift in analytic starting point opens up new questions for social psychology about agency, competence, and membership and how we conceptualise ‘AI’.

In everyday interaction, we often need to distinguish between ‘real’ or ‘hoax’, for example, when we receive spam calls or emails. Similarly, the ‘intelligence’ of some participants in conversation is contingent on interactional performance: a beat too long of silence, or one too many disfluencies can lead to the withdrawal of attributions of competence, agency, and personhood. These are not edge cases – they are the ordinary methods through which we constitute one another as competent, intelligent, accountable persons in the first place.

I will frame the argument with story of a study that shows how LLM-based systems are using social psychology to learn about and simulate the work of emergency dispatchers. I will then provide empirical grounding for the argument by presenting a study of how an LLM-based ‘smart speaker’ and a woman with a learning disability interact in a context where attributions of intelligence, agency, and personhood are at stake. Studying the details of interaction in these these settings can show us how categories like ‘intelligence’ are constructed through the “technology of conversation” (Sacks, 1984), rather than premised on the technical or cognitive (in)capabilities of participants.

The talk will pose a question for the conference more broadly: what becomes possible for social psychology if we treat ‘AI’ not as a self-evident object of study, but as a site for respecifying basic questions about agency, intelligence, and membership in interaction? I will close with some suggestions for researchers interested in taking up that challenge.

References

Albert, S., Housley, W., & Stokoe, E. (2019). In case of emergency, order pizza: An urgent case of action formation and recognition. Proceedings of the 1st International Conference on Conversational User Interfaces, CUI ’19, 15:1–15:2. https://doi.org/10.1145/3342775.3342800

Albert, S., & Hall, L. (2024). Distributed agency in smart homecare interactions: A conversation analytic case study. Discourse & Communication, 18(6), 892–904. https://doi.org/10.1177/17504813241267059

Albert, S., Hamann, M., & Stokoe, E. (2023). Conversational User Interfaces in Smart Homecare Interactions: A Conversation Analytic Case Study. Proceedings of the 5th International Conference on Conversational User Interfaces, CUI ’23, 1–12. https://doi.org/10.1145/3571884.3597140

Albert, S., Housley, W., Sikveland, R. O., & Stokoe, E. (2025). The conversational action test: Detecting the artificial sociality of artificial intelligence. New Media & Society, 27(10), 5592–5621. https://doi.org/10.1177/14614448251338277

Antaki, C., Finlay, W., Walton, C., & Pate, L. (2008). Offering choices to people with intellectual disabilities: An interactional study. Journal of Intellectual Disability Research, 52(12), 1165–1175. https://doi.org/10.1111/j.1365-2788.2008.01101.x

Antaki, C., Crompton, R. J., Walton, C., & Finlay, W. M. L. (2017). How adults with a profound intellectual disability engage others in interaction. Sociology of Health & Illness, 39(4), 581–598. https://doi.org/10.1111/1467-9566.12500

Antaki, C., Chinn, D., Walton, C., Finlay, W. M. L., & Sempik, J. (2020). To initiate repair or not? Coping with difficulties in the talk of adults with intellectual disabilities. Clinical Linguistics & Phonetics, 34(10–11), 954–976. https://doi.org/10.1080/02699206.2019.1680734

Barnes, S., & Bloch, S. (2020). Communication disorders, enchrony, and other-participation in repair. Clinical Linguistics & Phonetics, 34(10–11), 887–893. https://doi.org/10.1080/02699206.2020.1749886

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Mavrina, L., Szczuka, J., Strathmann, C., Bohnenkamp, L. M., Krämer, N., & Kopp, S. (2022). “Alexa, You’re Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant. Frontiers in Computer Science, 4. https://www.frontiersin.org/article/10.3389/fcomp.2022.791704

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Mlynář, J., de Rijk, L., Liesenfeld, A., Stommel, W., & Albert, S. (2024). AI in situated action: A scoping review of ethnomethodological and conversation analytic studies. AI & Society, 40, 1497–1527. https://doi.org/10.1007/s00146-024-01919-x

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The ‘intelligence’ of any participant in conversation is constantly in question. A beat too long of silence, one too many disfluencies, or an unaccountable action can occasion the withdrawal of attributions of competence, agency, and even personhood. Certain categories are especially vulnerable to inferences about atypicality and incompetence (Walton et al., 2020) including disabled people, young children, machines, and animals (Edwards 1994). However, when we analyse conversation involving parties conventionally seen as ‘atypical’ or ‘artificial’ such as in smart homecare settings involving disabled people, human carers, and virtual assistants, we see how these categories are constructed and reflexively used as interactional resources. This talk focuses on practices of repair in talk to, at, or around technologies that “travel under the sign of AI” (Suchman 2023) in a range of settings involving disabled people. In these settings, ‘assistive technology’ use is often circumscribed by rhetorics of techno-solutionism, tensions between conceptual models of disability and autonomy, and the everyday interactional experiences of disabled people (Cluly et al. 2020). Analysing the perspicuous setting of ‘smart homecare’ shows us how our interactional practices of talking with, at, and around ‘AI’ and other ‘atypical’ interactants reflexively construct attributions of intelligence and typicality (Albert et al., 2023; Albert & Hall, 2024). Treating ‘intelligence’ and ‘typicality’ as distributed, contingent accomplishments rather than as the preordained status of individual subjects allows us to look beyond conventional framings of AI as ‘conversational technology’ (Stokoe et al. 2024), to explore its role in the social infrastructure that Harvey Sacks (1984, p. 413) described as ‘the technology of conversation’.

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Antaki, C., Chinn, D., Walton, C., Finlay, W. M. L., & Sempik, J. (2020). To initiate repair or not? Coping with difficulties in the talk of adults with intellectual disabilities. Clinical Linguistics & Phonetics, 34(10–11), 954–976. https://doi.org/10.1080/02699206.2019.1680734

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Mair, M., Brooker, P., Dutton, W., & Sormani, P. (2021). Just what are we doing when we’re describing AI? Harvey Sacks, the commentator machine, and the descriptive politics of the new artificial intelligence. Qualitative Research, 21(3), 341–359. https://doi.org/10.1177/1468794120975988

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Maynard, D. W., & Turowetz, J. (2022). Autistic Intelligence: Interaction, Individuality, and the Challenges of Diagnosis. University of Chicago Press. https://doi.org/10.7208/chicago/9780226815992.001.0001

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Reineke, S., & Helmer, H. (2024). User practices in dealing with trouble in interactions with virtual assistants in German: Repeating, altering and insisting. Discourse & Communication, 175048132411271494. https://doi.org/10.1177/175048132411271494

Relieu, M., Sahin, M., & Francillon, A. (2019). Lenny the bot as a resource for sequential analysis: exploring the treatment of Next Turn Repair Initiation in the beginnings of unsolicited calls. https://doi.org/10.18420/muc2019-ws-645

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Repairing the Common Ground between Conversation Analysis and Conversational Technologies

Abstract

Computational linguistics and dialogue systems research share many terms and concepts with conversation analysis, but there are some irreconcilable di5erences in how key conversational phenomena are understood and operationalised between these fields. This leads to misunderstandings (at best) and fully fledged category errors (at worst) when we attempt to collaborate across disciplines that have much to gain from closer cooperation. In this talk, I will use examples from a recent special issue of Discourse Studies (Stokoe, Albert, Buschmeier & Stommel, 2024) to identify opportunities for reconciliation and targets for future cross-disciplinary work.

References

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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? 2^nd 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: [email protected]. 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

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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