Eye on Surveillance
Sawt is an open source AI assistant skilled with recordings from New Orleans Metropolis Council conferences. In the present day, any resident can ask Sawt a query about what’s happening within the council. Our long-term purpose is to develop an moral, community-controlled giant language mannequin (LLM).
Should you stay in New Orleans, take into account attending our annual assembly. You possibly can RSVP here. Should you don’t stay in New Orleans, we settle for crypto donations. Our OpenAI invoice is beginning to develop and any assist can be significantly appreciated.
Outcomes to date
Though we have simply publicly launched the beta model of Sawt, a choose group of native residents have been testing it for the reason that summer season. To this point, 319 questions have been requested by New Orleanians. They’ve additionally offered suggestions on 201 responses generated by the system.
Retention
Regardless of focused outreach, we have observed that group members aren’t returning to make use of the instrument after their preliminary interplay. There was minimal engagement aside from a spike throughout a mid-November focus group. This means that Sawt isn’t useful sufficient but for folks to wish to come again.
We have approached Sawt’s improvement with warning, understanding that expertise alone is not a catalyst for change. Real grassroots engagement is paramount. Whereas we stay essential of the challenge, we’re additionally enthusiastic about its potential. Upcoming options aimed toward growing Sawt’s usefulness embody prompt information uploads for brand new council conferences, alerts when registered key phrases are mentioned throughout conferences, a bigger dataset, and extra exact citations.
Accuracy & bias
Enhancing accuracy and lowering bias are essential to creating Sawt a worthwhile instrument. By focus teams with organizers, non-profit employees, and group members from various areas, we have confirmed that Sawt’s responses aren’t all the time correct and generally include biases. When requested to offer suggestions on a scale of 1 – 5, folks at the moment charge Sawt round a 3.
These suggestions periods additionally surfaced some counterintuitive patterns about actual world utilization. For instance, folks really feel that generated responses are essentially the most correct when the variety of supply paperwork (okay) is lowest. In different phrases, folks discover that responses are most correct when there’s much less info being thought of.
In the end, the pattern measurement is small, so there’s restricted statistical significance to those outcomes. In case you are a New Orleans resident, you possibly can help by submitting some suggestions your self.
The way it’s made
The preliminary Sawt prototype was developed over a weekend by @ayyubibrahimi. The core logic has remained constant since then. We first ingest uncooked information, create embeddings, and setup FAISS. When somebody asks Sawt a query, we establish essentially the most related paperwork utilizing FAISS, mix them with the question, and ship them to OpenAI for a response.
Within the preprocessing/FAISS part, we applied the Hypothetical Doc Embeddings (HyDE) methodology from ‘Precise Zero-Shot Dense Retrieval without Relevance Labels to create our embedding house for Retrieval-Augmented Technology. RAG is a method that mixes the strengths of enormous language fashions with exterior information retrieval, aiming to reinforce the relevance and accuracy of the responses generated by the AI mannequin. The hypothetical doc for producing these embeddings was produced utilizing a zero-shot immediate. This immediate reads:
As an AI assistant, make the most of the New Orleans Metropolis Council transcript information out of your coaching to ship an in depth and neutral response to the next question: “{user_query}”.
This method ensures that the mannequin makes use of the particular dataset it was skilled on, aiming to offer a complete and neutral reply to the query.
The immediate despatched to OpenAI throughout the remaining part is extra advanced and detailed. It accommodates a number of directions emphasizing the necessity to format responses clearly. It is designed to stability the breadth of response with conciseness. A big a part of the immediate is devoted to explicitly investigating and addressing potential biases within the response. Moreover, the immediate instructs the mannequin to outline unusual phrases, making the responses extra accessible and informative for individuals who might not be accustomed to particular phrases associated to metropolis council actions.
Though Sawt is an Eye on Surveillance challenge, we labored with Dr Culotta and three undergraduate college students at Tulane College. Their work has centered on addressing bias they usually constructed out the present suggestions mechanism. Dr Culotta additionally generated the evaluation utilized in Determine 4.
Understanding our context
In December 2020, after the New Orleans Police Division (NOPD) had been caught lying for years about its use of facial recognition, Eye on Surveillance a coalition of native organizations united to divest from surveillance and spend money on communities, pushed the New Orleans City Council to adopt a data privacy ordinance which included a ban on facial recognition and different surveillance applied sciences. Nevertheless, merely a 12 months later, town repealed the ban on facial recognition and cell-site simulators. Furthermore, the council member who sponsored the ordinance is now piloting a predictive policing tech program as district legal professional, which is banned beneath the ordinance he handed.
Most members of Eye on Surveillance are volunteers with day jobs. It’s onerous to maintain up with all of the regressive steps the council is taking throughout their conferences. Whereas official council conferences are public and theoretically accessible, they typically happen throughout work hours, final all day, and have agendas that change last-minute. This makes it difficult for us to maintain up. Particularly when town strategically schedules bulletins of controversial selections/conferences the day earlier than a vacation.
All year long, there have been quite a few situations the place we solely grew to become knowledgeable about important surveillance initiatives months after they had been raised in metropolis council. We hope Sawt will likely be a useful gizmo for us and different New Orleanians transferring ahead.
- ShotSpotter: Council thought of utilizing it in January and we came upon in February.
- Facial Recognition: Reporting was accessible as of February, however the metropolis rejected a number of public data requests till July. Sawt itself was useful in offering info that led to our public data request lastly being accepted.
- Predictive Policing: Former council member who sponsored ban on predictive policing, is now DA and piloting the unlawful expertise. We came upon from the newspaper.
- Drones: NOPD seeks suggestions on drones they already bought.
Subsequent steps
As we transfer into 2024, we’re reflecting on the broader implications of Sawt. We’re questioning the extent to which we is likely to be perpetuating AI biases and contemplating the particular biases of Eye on Surveillance members that might hinder our efforts. However, we’re excited concerning the continued progress and the prospect of launching an official model in the summertime of 2024. We’re dedicated to creating community-owned and operated AI fashions.
If you wish to get entangled:
- RSVP to attend our annual assembly
- Sign up for our mailing record
- Contribute some code
- Donate
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