Blog – Saul Albert

Virtual assistants and personal assistants in homecare interactions: conversational user interfaces as assistive technologies

October 20, 2021

Abstract:

This paper explores how conversational user interfaces (CUIs) and virtual assistants such as the Amazon Echo’s Alexa are integrated into the daily routines of a ‘smart’ homecare setting. Specifically, we focus on how a disabled person and their personal assistant (PA) manage and distribute their work during homecare routine activities of daily living such as eating, getting out of bed, and getting dressed. We studied ~100 hours of video recorded by participants using a voice-controlled camera system to examine how they adapted their activities to accommodate the affordances and constraints of working with a CUI and a range of connected smart home devices. Our analyses focus on how participants encounter and resolve practical and interactional troubles that emerge when using the CUI as a functional component of the homecare environment. By examining these moments in detail, this paper invites designers, engineers, and researchers to adopt a more interactional model of disability, agency, and environment that can describe the interdependent relationships between human users, virtual agents, and the smart homecare setting.

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.

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

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/10.1177/1044207309350561Post navigation

An artificial turn in social interaction research?

August 21, 2021

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

August 21, 2021

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

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

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

Putting wake words to bed

August 2, 2021

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

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
Alexa Hepburn and Galina B Bolden. 2017. Transcribing for social research. Sage, London.
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
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
Seung-Hee Lee. 2006. Second summonings in Korean telephone conversation openings. Language in Society. 35, 02. DOI:https://doi.org/10.1017/S0047404506060118
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
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
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.
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
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
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
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
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
Harvey Sacks. 1995. Lectures on conversation. Wiley-Blackwell, London.
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
Emanuel A Schegloff. 1988. Presequences and indirection: Applying speech act theory to ordinary conversation. Journal of Pragmatics 12, 1 (1988), 55–62.
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

July 25, 2021

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.

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.
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.

CLAN’s interface (left) and ELAN’s (right) with transcripts of the same bit of audiovisual data

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 a nd 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

December 22, 2020

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

June 29, 2020

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

May 25, 2020

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.

How do people with dementia and their carers use Alexa-type devices in the home?

February 27, 2020

We have a a fully funded PhD position available (deadline 6th March 2020) to work with myself, Prof. Charles Antaki and Prof. Liz Peel in collaboration with The Alzheimer’s Society to explore the opportunities, risks and wider issues surrounding the use of AI-based voice technologies such as the Amazon Echo and home automation systems in the lives of people with dementia.

Voice technologies are often marketed as enabling people’s independence. For example, Amazon’s “Sharing is Caring” advert for its AI-based voice assistant Alexa shows an elderly man being taught to use the ‘remind me’ function of an Amazon Echo smart speaker by his young carer. But how accessible are these technologies in practice? How are people with dementia and carers using them in creative ways to solve everyday access issues? And what are the implications for policy given the consent and privacy issues?

The project will combine micro and macro-levels of analysis and research. On the micro-level, the successful applicant will be trained and/or supported to use video analysis to study how people with dementia collaborate with their assistants to adapt and use voice technologies to solve everyday access issues. On the macro-level, the project will involve working on larger scale operations and policy issues with Ian Mcreath and Hannah Brayford at The Alzheimer’s Society and within the wider Dementia Choices Action Network (#DCAN).

Through this collaboration, the research will influence how new technologies are used, interpreted and integrated into personalised care planning across health, social care and voluntary, community and social enterprise sectors.

The deadline is the 6th March 2020 (see the job ad for application details). All you need to submit for a first round application is a CV and a short form, with a brief personal statement. We welcome applications from people from all backgrounds and levels of research experience (training in specific research methods will be provided where necessary). We especially welcome applications from people with first hand experience of disability and dementia, or with experience of working as a formal or informal carer/personal assistant.

This research will form part of the Adept at Adaptation project, looking at how disabled people adapt consumer AI-based voice technologies to support their independence across a wide range of impairment groups and applied settings.

The successful applicant will be supported through the ESRC Midlands Doctoral Training Partnership, and will have access to a range of highly relevant supervision and training through the Centre for Research in Communication and Culture at Loughborough University.

Feel free to contact me on s.b.albert@lboro.ac.uk with any informal inquiries about the post.

Adept at Adaptation

February 27, 2020

There is a crisis in social care for disabled people, and care providers are turning to AI for high-tech solutions. However, research often focuses on medical interventions rather than on how disabled people adapt technologies and work with their carers to enhance their independence.

This project explores how disabled people adapt consumer voice technologies such as the Amazon Alexa to enhance their personal independence, and the wider opportunities and risks that AI-based voice technologies may present for future social care services.

We are using a Social Action research method to involve disabled people and carers in shaping the research from the outset, and conversation analysis to examine how participants work together using technology (in the broadest sense – including language and social interaction), to solve everyday access issues.

The project team includes myself, Elizabeth Stokoe, Thorsten Gruber, Crispin Coombs , Donald Hislop, and Mark Harrison.

Background

Voice technologies are often marketed as enabling people’s independence.

For example, a 2019 Amazon ad entitled “Morning Ritual” features a young woman with a visual impairment waking up, making coffee, then standing in front of a rain-spattered window while asking Alexa what the weather is like.

Many such adverts, policy reports and human-computer interaction studies suggest that new technologies and the ‘Internet of Things’ will help disabled people gain independence. However, technology-centred approaches often take a medicalized approach to ‘fixing’ individual disabled people, which can stigmatize disabled people by presenting them as ‘broken’, offering high-tech, lab-based solutions over more realistic adaptations.

This project explores how voice technologies are used and understood by elderly and disabled people and their carers in practice. We will use applied conversation analysis – a method designed to show, in procedural detail, how people achieve routine tasks together via language and social interaction.

A simple example: turning off a heater

Here’s a simple example of the kind of process we are interested in.

In the illustration below, Ted, who is about to be hosted out of his bed, gives a command to Alexa to turn off his heater (named ‘blue’) while his carer, Ann moves around his bed, unclipping the wheel locks so she can move it underneath the hoist’s ceiling track. Before Ann can move the bed, she has to put away the heater. Before she can put it away, it must be switched off.

Ann leaves time and space for Ted to use *Alexa* to participate in their shared activity.

While Ann could easily have switched off the heater herself before moving it out of the way and starting to push the bed towards the hoist, she pauses her activity while Ted re-does his command to Alexa – this time successfully. You can see this sequence of events as it unfolds in the video below.

Here are a few initial observations we can make about this interaction.

Firstly, Ann is clearly working with Ted, waiting for him to finish his part of the collaborative task before continuing with hers. By pausing her action, she supports the independence of his part of their interdependent activity.

Secondly, using conversation analytic transcriptions and the automated activity log of the Amazon device, we can study the sequences of events that lead up to such coordination problems. For example, we can see that when Ted says the ‘wake word’ Alexa, it successfully pauses the music and waits for a command. We can see how Alexa mishears the reference to the heater name ‘blue’, then in lines 7 and 8, it apologizes and gives an account for simply giving up on fulfilling the request and unpausing the music.

*Alexa* mishears Ted’s reference to the heater ‘blue’, then terminates the request sequence

These moments give us insight both into the conversation design of voice and home automation systems, and also into how interactional practices and jointly coordinated care activities can support people’s independence.

Thanks to

The British Academy/Leverhulme Small Research Grants scheme for funding the pilot project.

Abstract

References

References

AI and voice technologies in disability and social care

Background

A simple example: turning off a heater

Thanks to