How tech can make sure every voice is heard in civil discourse

The loudest voice in the room is the one that is heard. The person who can earn the attention of the greatest number of people holds the most power. And this is a serious problem for civil discourse. The loudest voice in a room is not always representative of the majority, and a single voice cannot express the nuances of a diverse community.

Technology has emerged as an early-stage solution to this challenge of representation. Artificial intelligence (AI), for example, is already used to gauge voter sentiment and predict results through election polling. Some AI technology even pulls from public social media posting to craft a sample of individuals who represent the United States. The power of these systems is that real and accurate results are predicted and revealed without direction from humans (beyond writing the algorithm).

These technologies are clear steps toward creating systems to measure and report public feedback. With more representative data available, we will be able to create more equitable communities. Here’s what it takes to build, adopt and implement technology that ensures more equitable facilitation of civil discourse.

AI must be built with equality and representation in mind.
To leverage the power of technology and ensure that every voice is heard in civil discourse, we must first build systems that can accurately represent our diverse communities. It is true that artificial intelligence can inherit the biases of its creators. The National Institute of Standards and Technology (NIST) published guidelines for determining and addressing these inherited biases in AI technology.

Building AI with equality and representation in mind first requires clear and accurate data that is representative of a diverse population. In practice, when AI is employed in the context of civil discourse, it should be used to pull a significant number of responses from a large network of respondents. Additionally, AI systems and algorithms must also be adapted and adjusted regularly to remain relevant and representative.

The report from NIST also indicated that evaluating the algorithm alone for bias is not sufficient. AI must also be viewed through a critical lens that considers the social and community factors that could affect responses, like how access to social media (or access to the internet) may be limited in some communities, preventing those communities from partaking in activities such as posting online. Within the context of the initial example of AI used to determine voter sentiment, this context is critical.

AI can remove subjectivity and ensure fair and accurate data reporting.
Traditionally, town meetings were the primary places where community feedback was gathered, but data shows that these in-person meetings are not always representative of the true demographics of the area (for straightforward reasons like limited attendance at meetings or even administrative bias). AI can stand in the gap here for modern communities and make it possible to collect a greater volume of community feedback and then draw essential information from that feedback. AI is a valuable tool for leaders, legislators, and those managing large capital projects.

So how does AI deal with the loudest voice in the room and ensure that every voice is heard in civil discourse? Algorithms can be trained to conduct semantic analysis and even detect the severity of speech. These components make it possible for the algorithm to filter out any noise so that the final data set represents the community accurately. For example, in the context of capital projects, gathering public feedback is built into the planning process and is mandatory for the project to move forward. Capital projects include roads, water systems, government buildings, and even some commercial, education, and health care projects—all of which affect a diverse group of people dispersed across cities and towns. Community feedback is essential if we want to actualize the inclusive, equitable public spaces we so often discuss. With the use of AI, community feedback can be gathered, analyzed and reported free from administrative bias.

If we are to build more equitable communities, we must leverage new technologies like AI to conduct more thorough, expansive and fair assessments of feedback. Robust and accurate data about public sentiment as it relates to capital planning projects and community investments will help leaders determine if the infrastructure they are building serves the community it was intended to serve. This is just one example of the important role AI can play in civil discourse, but the value can extend further to topics like legislation, education, and public systems. Artificial intelligence can help leaders filter out the noise that is so prevalent in civil discourse to get to the core trends of public feedback so that all voices are heard.

Balaji Sreenivasan is the founder and CEO of Aurigo Software Technologies, and has played a critical role in shaping Aurigo to be a modern enterprise cloud software business that helps capital owners plan with confidence, build with quality and maintain their assets efficiently. With more than 40,000 projects and more than $300 billion under management, Aurigo has earned several industry accolades under Sreenivasan’s leadership, including Builtworld’s Infrastructure 50, GovTech 100, Constructech Top Products, and Inc. 5000. Sreenivasan is a mechanical and aerospace engineer with a deep understanding of enterprise software, blockchain and AI-ML technologies. He is an alumnus of the National Institute of Technology (NIT), Trichy, the University of Florida, Gainesville, and the Harvard Business School.