Conversational Search and Analysis of Collections of Letters and Comments


Today, public-sector personnel can, increasingly, utilize conversational search engines over large collections of letters and comments sent to elected representatives or in response to regulatory rulemaking processes. As the two publications shared below indicate, delivering these entirely realizable technologies to the public sector would greatly benefit democracy.

Brainstorming, in the not-too-distant future, conversational AI agents could run scripts and interact with conversational search engines, on behalf of human personnel, to accelerate repetitive procedures involved with producing reports or dashboards.

That is, beyond engaging in multi-step man-machine dialogue about collections of letters and comments, public-sector personnel could dispatch conversational AI agents which would interact on their behalf, in a procedural manner, with other conversational AI systems, e.g., conversational search engines, about large collections of letters and comments while producing valuable reports and dashboards.

Interestingly, generated reports and dashboards could be open and transparent, public-facing and Web-based, so that citizens could also explore them.

Below are two publications - with selected quotes - about how AI could be of use for empowering public-sector personnel to process bulk letters and comments sent to elected representatives or in response to regulatory rulemaking processes.

AI Could Shore Up Democracy – Here’s One Way (link)

"Consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

"For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes.

"In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, ‘who donated the most to our campaign’ or ‘which company employs the most of my former staffers’ become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

“If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals.”

Implementing Federal-wide Comment Analysis Tools (link)

"The federal government publishes tens of thousands of documents each year in the Federal Register, with over 800,000 total documents since 1994, which garner millions of submissions from the public (comments and other matter presented).

"Agencies have a legal obligation to consider all relevant submissions and response to those which, significantly, would require a change to the proposed rule. To discern relevance, significance, and disposition, human review is needed.

"The capacity for human review often can’t meet the demand for high-volume comment events. Initial screening and classification allow regulator officials to focus on relevant submissions and response to groups of significant comments address the same topic. Some agencies perform independent, tailored analyses to assist with this initial screening.

“The CDO Council recognized an opportunity to leverage recently advanced Natural Language Processing (NLP), which would be more efficient than these independent analyses. A generalizable toolset could provide effective comment grouping with less upfront effort, and this toolset could be shared and reused by rule makers across government to aid and expedite their comment analysis.”