Holding
"AI for Good" Accountable
The “AI for Good” movement is going unchecked. The tools at its center (LLMs) are trained on data collected without consent, power surveillance tools and automated weapons, and come with a significant environmental cost.
Most notably, it forces vulnerable communities to interact AI, and assume its risks, with little evidence that these solutions are even wanted, let alone helpful.
The Kaleidoscope Project was established to provide that check. We hope to push “AI for Good” towards community authority and governance, honest measurement of both benefits and harms, and the minimization of social impact's dependence on LLMs and Big Tech.
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
Honest measurement should move on from vague metrics like “Lives Impacted” towards quantifying the “Net Effect” (benefits minus harms) of an AI’s deployment. Numbers can’t fully measure impact so they must be paired with longitudinal, depth-based interviews. Transparency in this measurement and other aspects (e.g., policies, data retention) helps to ensure accountability, lift others and move towards field-wide standards.
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02
Big Tech Independence
Partnerships between Big Tech (especially LLM developers) and CSOs require scrutiny. Big Tech gets increased usage from embedding their tools in core social services and an improved reputation from associating with impact, while CSOs risk theirs on widely distrusted tools. That distrust is warranted given opaque data practices and model interpretability, contracts with surveillance/"defense" tech and the unilateral power to change core functionality or pricing at any time.
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03
Community Agency & Ownership
There’s an enormous gap between those who decide to build, and especially fund, “AI for Good” and those who interact with it. When AI replaces a person in a service that someone depends on, a choice is forced onto them. While the lack of consent is problematic alone, it’s compounded by undisclosed use, retained data, and the lack of standards around quality. Even our own attempts at comprehensive evidence frameworks can’t encapsulate the nuanced benefits of human interaction. Regardless of how good any builder, funder, or evaluator thinks an AI tool is, the people who will use the tool should have governance power to decide how and whether it’s deployed and clear ownership of their data.
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04
Surface Collective Input
A founder’s “lived experience” is important, but it doesn't reflect the diversity within a community. What’s more critical is community input at scale. “Community design” is an established principle in social impact but is often treated as a checkbox or afterthought. True community input should happen before prototyping so they can shape the foundations. This community involvement should be codified into governance mechanisms and evaluation methods so that it can’t be overlooked when inconvenient.
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05
Invest in Foundations
While AI is sold as a leapfrog technology, no AI application stands alone. Most require devices, internet access, and digital literacy. Without these foundations, AI can exacerbate divides within a single community. Before deploying AI, first audit and, if needed, fund the foundations. This includes education about what happens to people’s data and where these tools fail. Investing in these foundations also avoids the “saviorism” trap of deploying a culturally ignorant tool or creating unforeseen maintenance burdens. The right investment should give communities power and capacity to innovate on their own.
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How we work
Diligence
Evidence-based assessment of AI investments for philanthropies, using our in-house Net Effect framework.
Policy & Education
Strategy sessions or long-term engagements focused on codifying stick governance mechanisms and/or educating on the biggest opportunities and risks of AI.
Micro-Grants
Reinvested revenue towards foundational infrastructure for resource-limited communities.
Evaluations
Quantifiable tests to assess an AI tool's performance over a knowledge domain or set of tasks
Our Impact
>$10M
Capital de-risked