Rapidly prototyping ideas around the future of artificial intelligence and data privacy with Meta.
Source: TTC Labs
MY ROLE
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Facilitation
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Leading
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Ideation
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Prototyping
OUTLINE
TTC Labs brings together policymakers, lawyers, communications professionals, privacy experts and product designers to tackle challenging questions about the future of technology.
Co-facilitating two design jams in Singapore with TTC Labs, we worked with startups to design and prototype ideas around the topics of artificial intelligence (AI) and data privacy & consent.
Client / Facebook
Studio / Craig Walker
Source: TTC Labs
AI EXPLAINABILITY
AI is rapidly developing, however there is a lack of trust with this new technology. We asked ourselves how we could develop trust in AI by demystifying how it works and giving users control over how their data is used.
DATA PRIVACY & CONSENT
Data use notices are lengthy and hard to digest, causing users to skip over important content which informs their consent. Redesigning these notices with clear UI, UX and content design can help resolve this.
Source: TTC Labs
FRAMING THE PROBLEM
After listening to subject matter experts, we developed how might we's and a problem statement to frame the challenge of the day.
PROTOTYPING AI EXPLAINABILITY
After an exciting morning listening to AI subject-matter experts and developing an area of focus, we split into groups. The teams were multidisplinary which opened us up to interesting ways of designing.
Leading one group, we worked with the start-up Jumper to consider how they could integrate AI explainability into their product. Jumper is an AI-driven chatbot that operates within existing social media apps, such as Messenger. New products are surfaced to customers based on their previous transaction history. The design was therefore conversational.
We began first by developing a persona and sketching ideas on paper, exploring potential narratives containing AI explainability. The final outcome was a prototype that explores progressively disclosing how AI is used in the app.
Algorithmic driven preferences are often opaque. The proposed design disclosed how AI is being used by surfacing previous choices and how they are informing the algorithm. The design educated users on how AI is used in the product, while also giving them control over their data.
