Lauren McCarthy

Executive Summary

Lauren McCarthy creates participatory performance systems that examine how social and technological systems shape identity, relationships, and community. Rather than making traditional artworks, she describes her practice as creating situations she invites people into and then world-building through those situations together.

Her breakthrough work LAUREN (2017-present) involves literally replacing Amazon Alexa with herself. She installs cameras and smart devices in people's homes, then remotely watches and controls everything for approximately one week. She attempts to do everything Alexa can do, plus anticipate her participants' needs and act on them without being asked. Each week-long engagement develops its own character. Some participants relate to her functionally. Others bring her into their lives, asking what she thinks of an outfit, what to do about a relationship, what to cook for dinner.

Her work consistently insists on actual systems over speculation.

These are not speculative ideas or sci-fi ideas, but actual systems or actual situations that people can participate in.

This commitment to lived experience over hypothetical generates knowledge that theoretical work cannot produce. The understanding she pursues comes from watching and being watched, from inhabiting roles that automation pretends are seamless, and from discovering what those roles actually require when a human has to perform them.

LAUREN and the Smart Home Role

LAUREN began in 2017 as a response to Amazon's rollout of Alexa. At the time, smart speakers were being framed as functional appliances. Lauren felt the framing missed the point.

This is not just about a light that turns on automatically. This is bringing a different kind of intelligence and being into the home. It's giving up a lot of privacy and control and decision-making.

The project also emerged from personal circumstances. She had recently moved and moved in with someone, prompting questions about what home means and, more pointedly, what the difference is between a gaze of care and a gaze of surveillance.

She typically performs the role for about a week, which she describes as quite consuming, because watching people closely enough to anticipate them is genuinely demanding work. She deliberately creates the perception of being somewhere between a human and the system, using a cloned voice rather than speaking directly, so the experience feels a little more distributed. One of the most memorable moments involved orchestrating the ambience for a participant's date night, DJing the music and lighting in real time, while feeling uncertain about the goal. She did not want to ask too many questions at that point.

The project also makes visible the hidden human labour inside supposedly automated systems. Amazon Alexa has humans listening in for quality control, some of whom have had pretty traumatic experiences, including overhearing domestic violence and feeling unable to act. The point of LAUREN is not to expose Amazon, but to make the asymmetry felt. When the human in the system is named, present, and visibly working, the costs of the automated version become harder to ignore.

Consent as Aesthetic Position

Lauren's installation process modelled a different approach to technology deployment. She walked through homes with participants, asking permission for each camera placement, showing them how to turn things off, and establishing a shutdown keyword.

I was really thinking about the way that technology is often just forced on us without a lot of consent. I was trying to model a different pattern.

Participants could unplug cameras, turn them away, cover them, or speak the keyword to shut the whole system down. Some chose to keep cameras out of bedrooms, or to angle them away from beds when sleeping. The structure preserved their authority over their own space. The contrast with how smart devices typically arrive in homes, fully on by default with consent buried in terms of service, was the point. The work argues for an aesthetic of permission.

Speed, Homogenisation, and the Reflective Mode

Lauren identifies a fundamental problem with current AI tools.

I think one of the problems is just the speed at which these things work. It shuts down possibilities for creativity and imagination when you can get an answer so quickly.

She uses ChatGPT regularly for administrative tasks, drafting emails or assembling documents, and finds it genuinely useful for that. But for creative ideation, the outputs feel watered-down. She attributes this to LLMs aiming for a middle ground, an average, rather than the more unique or strange ways of seeing she pursues in her practice.

She also observes a homogenisation effect when AI is used for editing. Running text through a model makes it sound better in a generic way, and less like her. If everyone uses the same better filter, everything gets a little less interesting. Her vision for a more useful AI would build in deliberate slowness, a brainstorming partner that does not immediately spit out a page of ideas, but asks a question, pauses for a while, and builds time into the exchange.

Collaboration Versus Assistance

Lauren articulates a clear distinction between collaborative and assistive AI use.

Sometimes I prompt something and what I get back is not what I expected but that's interesting. Let's go. Let's do more.

That, for her, is collaboration. By contrast, when she is refining toward a set idea, asking the system to do the same thing again with minor variation, the work becomes assistance. The distinction turns on surprise and mutual adjustment versus refinement toward a predetermined goal.

She also finds AI useful for getting unstuck. When she cannot move forward, she sometimes asks the system to make a deliberately bad version of what she is trying to do. Even though the result is not what she wants, it gives her something to react to, and that reaction moves the work forward. AI in this mode is useful for carrying things through rather than generating new ideas.

Making as Generative

Lauren maintains a strong commitment to making as the primary site of creative discovery. When she feels stuck conceptually, the answer is not more thinking.

Instead of just thinking about it some more, I need to just make a version. And then that will be much more generative in getting to the next version.

She is equally clear about the value of time in creative development. Reaching a certain level of nuance, understanding, or resonance requires spending time with something. She is not trying to accelerate those processes. The making itself is what produces the ideas, not the application of ideas to making. This is part of why she is wary of AI's speed. Skipping the time of making skips the place where the thinking happens.

Pedagogy and Unique Voice

Teaching in an art programme, Lauren consistently returns to one question: what is the thing only that student can do that no one else can? Everyone has access to the same tools. The question is what each artist contributes that is uniquely theirs.

When reviewing student work made with AI, the question is not whether they used the tools but how they situate themselves inside the result. She sees AI enabling students to experiment and realise ideas at a level they could not reach before, offering glimmers of where their vision might go, while insisting that the tools do not substitute for the harder work of finding what is distinctively theirs.

Her position on technology and meaning is consistent.

There can be art in anything. It's really about how you decide to employ the tools rather than whether you're using the tool at all.

The tools themselves are neutral. The work of being an artist, in her framing, is the work of deciding what to do with them, where to slow them down, where to refuse them, and where to step into the role they are pretending to play. The week she spends watching someone's home is not a critique of automation in the abstract. It is the only way she has found to know what automation actually is.