Lab
Turning curiosity into experiments, prototypes, and new ideas.
Introduction
Tools, Experiments & Interventions
The Lab is where questions become things we can explore, test and learn from. We build tools and experiment with new approaches to exploring and shaping how people interact with technology.
Some experiments lead to practical interventions. Others open up new questions. All are part of figuring out what we can do differently.
BE Cue Detector
Making the Invisible Visible.
Conversational AI can sound knowledgeable, attentive and remarkably human. But the language that makes it believable isn't necessarily a reflection of its capabilities.
The BE Cue Detector uses AI to identify and highlight three kinds of textual cues: social, expertise and mirroring. By making these cues visible, it creates opportunities to notice, question and discuss how AI shapes our perceptions.
Developed from exploratory research into the Believability Effect (BE), the tool is an experiment in designing for human judgement.
The BE Cue Detector highlights social, expertise and mirroring cues in conversational AI responses.
BE Diagnostic
Examining AI Beyond the Surface.
The BE Diagnostic is an experimental research tool for systematically identifying and examining textual cues that contribute to the believability of conversational AI.
Built to support my exploratory research into the Believability Effect, it uses AI to classify social and expertise cues, provides explanations for its classifications, and allows human reviewers to accept, reject or revise the results.
The tool also produces structured data for further analysis, making it possible to examine patterns across AI-generated responses.
Beyond research, the approach opens up possibilities for independent AI evaluation. Organizations could examine how vendor systems communicate, signal expertise and respond to users, rather than relying exclusively on vendor-provided assessments.
The BE Diagnostic identifies and classifies textual cues in conversational AI responses, providing AI-generated explanations and allowing human reviewers to evaluate the results. Note: Colour coding is being aligned with the BE Cue Detector.
Earlier Explorations
Different technologies, different questions, and a shared approach: bring together diverse perspectives, experiment with what's possible, and discover what happens when technology meets the real world.
Where’s Janneke? | 2018
Can an interdisciplinary team go from idea to working prototype in one week?
Three students, three disciplines and one challenge. Observe how people work, find a problem worth solving and build something useful.
They noticed colleagues were always looking for me. Their solution? An Amazon Alexa skill called "Where's Janneke?"
One week later, it worked.
And when they proposed tracking my whereabouts, I had one condition: they had to experience being tracked too.
HestaHub | 2019
What if your home knew when to ask for help?
Developed as a concept for Collision 2019, HestaHub explored how existing smart-home technologies could help people living independently.
The idea was simple: keep personal data inside the home and notify a trusted person when something needed attention. No constant surveillance. No unnecessary sharing of personal information.
Built around a principle carried forward from earlier robotics experiments: know when to ping a human.
Saffi & Elly | 2018-2019
What happens when social robots meet the real world?
Exploring what robot co-workers might mean for banking, we took Saffi and Elly out into the wild, from corporate events to TIFF and a Hermès pop-up.
We discovered that people had strong reactions to the robots, often wanting to interact or take photographs. Those encounters inspired new possibilities, including a social media partner and a talent scout to help connect young people with recruiters.
The experiments also exposed the limitations of the technology, particularly around data privacy, control and the unpredictable nature of human interaction.
The experience eventually led to a presentation at the National University of Singapore.
Big questions. Small experiments. Better ways forward.
AI is changing faster than our ways of working. Giving human judgement a chance means experimenting, learning and adapting as we go.
At Orange Gate Labs, we build small, purposeful interventions that put people at the centre. We use what already exists, create what doesn't, and move quickly from ideas to something we can test.
Have a challenge worth tackling together?