ConsumeWise

Consumer Awareness Platform for AI Risks

Project Details

We are building an open-source consumer awareness platform that helps the general public understand, evaluate, and navigate products and services powered by advanced AI. As AI systems become more capable and pervasive, consumers face growing risks from manipulation, bias, privacy violations, and opaque automation—yet have almost no accessible infrastructure to make informed choices or hold providers accountable.

This project addresses that gap with a mobile-first web application covering four critical domains:

Product Transparency — Scanning and decoding AI-generated content, deepfakes, and algorithmically curated products to reveal hidden AI involvement and potential harms.
Financial Literacy — Educating users about AI-driven financial products, robo-advisors, predictive scoring, and scam vectors enabled by generative AI.
Health & Nutrition Awareness — Evaluating AI-driven wellness products, diagnostic chatbots, and personalized medicine claims for safety and evidence quality.
Digital Privacy Awareness — Mapping data harvesting by AI services, explaining consent architectures, and providing actionable privacy hygiene.

By raising baseline consumer literacy about AI risks, we aim to reduce societal vulnerability to AI-enabled manipulation, improve market pressure for safer AI products, and create a more informed citizenry capable of supporting meaningful AI governance.

Theory of Impact

Primary Pathway: Reducing Catastrophic and Existential Risk from Advanced AI

Existential and catastrophic AI risk does not emerge only from superintelligent systems. It accumulates through thousands of smaller failures that normalize over-reliance on opaque AI, erode human judgment, concentrate power, and weaken social resilience. A consumer base that cannot distinguish safe from unsafe AI, or recognize manipulation, creates fertile ground for both accidental and intentional catastrophic harm.

This project reduces x-risk by:

Building epistemic resilience. Informed consumers are harder to manipulate by AI-generated misinformation, deepfakes, and algorithmic radicalization—all of which are force multipliers in instability and conflict scenarios.
Creating market feedback loops. Transparent product labeling and crowd-sourced risk signals push AI companies toward safer design by making harms visible and costly.
Generating policy-relevant data. Aggregated user reports and platform analytics produce real-world evidence of AI harms, informing regulatory frameworks before crises occur.

People

Niranjan Anand

Team Member

Grants Received– no grants recorded

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Funding Asks

Funding ask

Open

Minimum

$25,000

Ideal

$80,000

How the money will be spent

Engineering (1.5 FTE × 12 months) $30,000+

Content & Research (part-time domain experts) $8,000+

Infrastructure & Tooling $4,000

Community Management & Moderation $3,000

Contingency / Unforeseen Costs $5,000 +

Total $50,000+

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