Polimill Inc., a leader in the social implementation of generative AI, added a function to its generative AI for government ministries and local governments, “QommonsAI,” on February 4, 2025, that allows the system to explain the data sources and reasoning processes that underpin its policies. This function can be used in “QommonsAI Talk” within Commons AI.
This function, which can explain the reasoning process, is expected to provide reassurance to citizens when using generative AI in administrative decisions and responses to residents, addressing concerns that a lack of transparency in the decision-making process may lead to doubts and distrust among citizens.
Commons AI is a collection of generative AI with various specialized functions
Commons AI is a collection of generative AI with various specialized functions. As of February 2025, the main specialized generative AIs are as follows:
- Congressional AI
- Public Service Support AI
- Social Welfare AI (Edition I is “Overcoming Social Vulnerability and Inclusion”)
- Administrative documents (e-Gov Laws and Regulations)
- AI to support resident voices
- Private Knowledge
- QommonsAI Talk
Also Read: Deep Tech Forum NYC Highlights Japan-U.S. Innovation Synergies
What is particularly important about generative AI for government officials
At Polimill, we believe that the following points are particularly important when it comes to generative AI for government officials compared to generative AI for businesses, and we develop and provide Commons AI with these points in mind.
Ensuring public interest and accountability
Unlike profit-making companies, government organizations are public institutions whose purpose is to promote the welfare and interests of all residents, and therefore accountability and transparency are required. Therefore, when using generative AI for policy-making or information provision, it is extremely important that the output meets the standards of public service, is fair and accurate, and does not unfairly exclude the socially vulnerable or minorities. It is necessary to develop a mechanism to detect and correct biases (such as regional discrimination, prejudice against gender, race, disabilities, etc.) in generative AI1 prompts and LLM models.
Handling and security of personal and confidential information
Governments handle highly confidential data, such as basic resident registers, tax information, health insurance information, etc. Trade secrets and customer information are also important for business generative AI, but the leak of public personal information has a huge social impact and can easily lead to a loss of credibility for local governments and legal issues.
Impact on the digital divide and regional disparities
In a corporate environment, the focus is on improving profits and streamlining operations, and disparities are often limited to differences in competitiveness within the company. On the other hand, in local governments, disparities in resident services can directly cause regional inequality and social problems, so from the perspective of corporate social responsibility (CSR), it is necessary to provide generative AI free of charge and provide support and training free of charge to local governments that have difficulty securing the resources to introduce it.
Transparency, explainability, and ethical considerations
When generative AI is involved in administrative decisions or responses to residents, citizens will have doubts and distrust if the decision-making process is not transparent. The government is governed democratically and has a strong obligation to explain. While explainability is important for companies as well, the public sector needs to ensure even stricter transparency from the perspective of democratic legitimacy.
Public Responsibility for Human Resource Development and Correcting Internal Disparities
Digital divides within government can affect services to residents, so as an organization, it is imperative to urgently promote ICT education and skills as a public responsibility. Companies develop human resources as part of their management strategy, but local governments must also explain the process to taxpayers and proceed in a way that contributes to improving resident welfare.
SOURCE: PR TIMES
