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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: 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

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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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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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.

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.

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:

Windows:

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.