THE KALEIDOSCOPE PROJECT
AI · Philanthropy · Policy

Holding
"AI for Good" Accountable

The current “AI for Good” movement (i.e., the push to fund or deploy LLMs as social impact solutions) should be treated as a danger to society. These same tools power automated weapons and surveillance tech, formed via an environment-destroying training process over non-consensually collected data, and most notably, lack any quality evidence that they are even wanted, let alone helpful.

The Kaleidoscope Project was established to provide a counterbalance. Our work is grounded in the belief that “AI for Good” requires high levels of community authority, robust measurement of both benefits and harms, and a reorientation towards AI tools that aren’t LLMs or otherwise generative.

OUR FIVE GUIDING PRINCIPLES

01

Evidence & Transparency

02

Big Tech Independence

03

Agency & Ownership

04

Surface Collective Input

05

Invest in Foundations

01

Evidence & Transparency

Net Effect

Transparent (policies, data retention)

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02

Big Tech Independence

Biggest harm is conflation. Not only good marketing/PR but making communities dependent on tech that has ethical concerns + financial concerns - Predatory

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03

Community Agency & Ownership

Communities need a say of how to interact.'

Data soveriegnty, Access

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04

Surface Collective Input

Lived experience is not a replacement. Understand the unique contexts someone is in to build effective solutions. Methodology

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05

Invest in Foundations

AI can exacerbate harms, especially if capital is prioritized their. Close foundational gaps like literacy, connectivity

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How we work

Diligence

Evidence-based assessment of AI investments for philanthropies, using the Net Effect framework. Gains and harms measured where tools are actually deployed — before more capital follows. Real funding decisions, not demos.

Policy & Education

Evidence-based assessment of AI investments for philanthropies, using the Net Effect framework. Gains and harms measured where tools are actually deployed — before more capital follows. Real funding decisions, not demos.

Micro-Grants

Evidence-based assessment of AI investments for philanthropies, using the Net Effect framework. Gains and harms measured where tools are actually deployed — before more capital follows. Real funding decisions, not demos.

Evaluations

Evidence-based assessment of AI investments for philanthropies, using the Net Effect framework. Gains and harms measured where tools are actually deployed — before more capital follows. Real funding decisions, not demos.

Our Impact

>$10M

Capital de-risked

Feel something is missing? We would love your feedback!

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