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Civic AI

We’ve tracked civic, democratic, and ethical AI projects for you since 2016. Explore our wide-ranging collection of examples of people using AI for public interest goals (including work to prevent harms from AI itself).

Explore AI tools

Filter by open-source tools only or browse all AI projects and organizations

AI for Participatory Democracy

AI features on digital participation platforms: How engagement platforms are introducing AI features to support participants and institutions.

Including AI features introduced by digital participation platforms:

  • “Several participation platforms already use Natural Language Processing (NLP) to summarize large volumes of text submitted by participants and identify topical clusters in conversations. As more people participate on digital platforms, these solutions will become more necessary, since few institutions have the capacity to read millions of submissions. IBM Watson’s Project Debater is an example of how multiple AI models can be integrated to summarize political arguments.
  • NLP can also be used to categorize the emotional tone of feedback through sentiment analysis, although this technology still isn’t reliably accurate.
  • A hybrid model that combines AI, crowdsourced filtering, and expert human intelligence could help ensure the success of large-scale engagements.
  • AI also can flag efforts to subvert a vote with fake levels of support by identifying identically phrased language.
  • Speech-to-text technology is rapidly evolving to produce more accurate transcripts of phone conversations and meetings.
  • People are already able to use AI to generate significant volumes of text with a simple prompt. For example, today’s AI software can instantly generate a large volume of content to support a simple, initial statement like, “We shouldn’t build a park here.” Such bot-generated text can be used to pollute the participatory process and, in turn, diminish the value of contributions from actual humans.
  • “Deepfake” technology allows people to generate realistic-seeming images, video, and audio of people who don’t really exist. As this technology becomes more effective and readily available, it will be easier to generate authentic-seeming audio and video clips from fictional personas.”

It is important to note, however, that AI could also have negative effects on participation:

(Quotes by Matt Stempeck, Guide to Digital Participation Platforms with People Powered)

Govtech AI

For example, AI for preventing fraud and improving customer service.

Including:

  • AI Constituent / Customer Service: Automating or semi-automating constituent services, usually on behalf of government, and usually in the name of efficiency and cost-savings.
  • Registers of Government AI: Official or watchdog inventories of all the AI tools, algorithms, models, and applications deployed by government(s).
  • Organizational policies on AI: How organizations are strategically responding to the increasing availability and power of AI.
  • Government AI Strategies: “[A] set of coordinated government policies that have a clear objective of maximizing the potential benefits and minimizing the potential costs of AI for the economy and society” – Building an AI World, by CIFAR.

Plus:

AI and Open DataFor example, automating large-scale public interest data projects and mapping disparate open data sets together.
Journalism AIFor example, tools for journalists to conduct investigative reporting.
AI and DisinformationAI to help in the fight against disinformation, and AI for…spotting AI.
Campaign AIAI to help you talk to more voters.
Nonprofit AI

Including Organizational policies on AI: How organizations are strategically responding to the increasing availability and power of AI.

And: AI and the humanitarian sector: hype or hope?

AI for Watchdogging & Transparency

For example, to monitor government by analyzing procurement records at scale.

Including AI Auditing: Projects, resources, and groups that audit public, democratic, or civic AI to ensure equitable and ethical outputs.

Open source AI Artificial Intelligence tech (like LLMs) whose source code, training data, models, or algorithms are made publicly available, or which are distributed with open licenses, (sometimes) allowing anyone to view, modify, and distribute the code.
AI governance Projects that seek to establish, improve, and/or democratize societal governance over AI technologies in order to protect people and democratic values.
Generative AI The new generation of user-friendly generative AI tools that allow people to create entire applications, essays, videos, images, songs, and more from simple prompts.
Bots Autonomous or semi-autonomous chat agents that interact with people for the purpose of achieving a civic goal, like registering a vote or capturing citizen feedback.
Machine Learning “Learn without explicit programming, using examples, to develop a model that can make decisions.” – Google.org
Deep Learning “Using multiple layers of artificial neurons to create a network that can make a decision based on raw input. Applications of deep learning include computer vision and speech recognition.” -Google.org
Machine Vision “See, recognize, and process images, videos, and other visual inputs.” -Google.org
Deepfakes Also known as synthetic media. Including “deepfakes for good”.
Audio processing and Speech recognition “Hear, recognize, and process sound files and other auditory inputs” and “Using audio processing to translate human speech to text” -Google.org
Machine learning analytics “Process and understand large volumes of data to identify patterns and make predictions.” -Google.org (We haven’t tagged many projects here because we have an entire Data Science for Social Good category)
Ethical & Responsible AI While the first generations of tech-for-good work took a solutionist approach to addressing existing problems with new technology, scholars and activists are driving growing awareness of the problems with technology itself. By exposing the negative consequences, intended or otherwise, of tech, these communities draw attention to issues with tech-centric approaches.
Digital security and privacy People will not (and should not) use tech that’s untrustworthy. Too many tech projects, including civic tech projects, have abused user privacy and/or failed users in providing a secure user experience. Anyone advocating for increasing digitization of government, social benefits, or public conversation should invest in strong digital security and privacy-respecting approach from the onset and on an ongoing basis, as this area evolves rapidly.
Algorithmic Transparency These projects open “black box” algorithms up to expert and/or public audit by sharing inputs, rules, and other components of how the system makes its decisions.
Auditing Civic AI Our collaboration with Eticas to test civic and democratic AI models to ensure that their algorithmic outcomes and impacts do not further discriminate or erase individuals and groups.
AI Literacy The ability to navigate technology and complicated AI contexts is an increasingly necessary skillset for citizens.
Assistive and Accessible AI Trustworthy AI should be accessible to all people. The ability to use technology regardless of disability or status is a fundamental precursor to that technology serving civic goals.
AI for public good Includes AI addressing related fields beyond civics and democracy, including humanitarian and peacetech.
     

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