NLP in the Public Sector

NLP (Natural Language Processing) is a subfield of AI that concentrates on creating the algorithms and computational models needed for machines to comprehend, analyze, and produce natural language. The goal of NLP is to make it possible for computers to communicate with people in the same way that people do through language.

There are many applications for natural language processing, including ones in business, the public domain, and research. In this article, we'll discuss how governments and the general public are using natural language processing to enhance the infrastructure and its components as a whole.

Introduction to NLP in the Public Sector

Nowadays, artificial intelligence is becoming a must for every industry, whether it is the public or commercial sector. Considering how different businesses have adapted, it appears that the private sector is embracing artificial intelligence with open arms while the public sector is still in the early stages of embracing the technology for its practical applications. 

Yet, over the past few years, the government sector has accelerated its use of technology and artificial intelligence, which have now become a necessity. Additionally, it aids in the transition of government to electronic governance. 

For example, an NLP tool can search through tens of thousands of emails and social media posts to identify the most frequently asked queries concerning government services. By automating the responses to frequently requested queries, NLP frees up staff time to work on more challenging issues. This may result in quicker and more accurate responses to citizen questions, making people happier.

Apart from this NLP can also be used to figure out how someone feels. This means that it can find out how people feel about a government policy by looking at what they say about it on social media or other online platforms. With this information, governments can change their policies and the way they talk to people to better meet their needs and expectations.

Applications of NLP in the Public Sector

NLP has various applications in the public sector, including interacting with the public, researching historical records, and passing orders, acts, and ordinances. NLP technology, when used properly, can aid in the processing of these texts, hence increasing the possibility of speed and effectiveness of several public agencies. Here are some ways NLP is being used in the public sector right now:

Answering public inquiries

Governments are using NLP to manage the enormous amount of emails, texts, letters, calls, and social media posts from the general public. Countries already have started using natural language processing to sort through thousands of emergency reports and determine which instances are the most urgent.

Effective call routing could reduce the workload for help desk workers and even assist in resolving simple informational inquiries. Expanding the use of more intelligent virtual assistants can lower costs by 30% in mature contact centers while increasing customer satisfaction.

In order to process public comments on proposed regulations, which can take over 1,000 hours simply to categorize, HHS (United States Department of Health and Human Services) has tested the use of natural language processing. The agency was able to show millions of dollars in cost savings thanks to the tool's ability to meet quality standards and boost personnel satisfaction.

Improved Policing

A more effective, prevention-focused, and non-confrontational policing strategy may now be implemented at scale because of Natural Language Processing (NLP).

NLP can increase accuracy and transparency in reporting crimes. Police reports are normally hurriedly produced, and there is fear of repercussions, if the crime witnesses and victims' statements of what happened may be inaccurately covered.

According to researchers at Claremont Graduate University, NLP technology can precisely identify key components including weapons, cars, time, individuals, and locations in witness and police reports as well as linked news articles.

Referred to as problem-oriented policing (POP), this system can be a potential replacement for traditional policing methods. POP aims to deter crime by influencing the factors that contribute to it. Unstructured free text data that is frequently gathered by police for administration or investigational purposes is one potential source of information regarding the crime. NLP has the ability to make these unstructured data accessible, enabling law enforcement to carry out more POP initiatives.

Obtaining weather and traffic information from social media 

The detection of disruptive occurrences becomes possible if a given government controls a population that is sufficiently well-represented on social media. A system that collects tweets and accurately identifies earthquakes was created at the University of Tokyo in Japan. Their study discovered that it detected 96% of earthquakes with a seismic intensity of 3 or higher, outperforming the Japan Meteorological Agency's detection rate. Also, the technology showed quicker response times when alerting people to a hazard.

Examining government policies

By analyzing presidential addresses from 10 Latin American nations and Spain between 1819 and 2016, the Poverty and Equity Global Practice Group of the World Bank utilized LDA (Latent Dirichlet Allocation) topic modeling to gauge changes in policy goals. The authors were able to determine the key themes for each text using LDA and show how their importance varied across nations and across time.

Identifying bias and fake news

The World Economic Forum has identified digital disinformation and false news as the most significant technological and sociopolitical danger. Intentionally misleading readers, unwarranted polarization of opposing groups, and increased news traffic are all goals of fake news, which is produced with these goals in mind.

For instance, the Facebook AI team recently unveiled the RoBERTa, a natural language processing model, which was later modified to determine whether the news is true or fraudulent based on the localization and semantic context of the tokens it contains.

Enhance writing and reading abilities

NLP will soon develop the skills necessary to write government papers in addition to reading them. Several organizations are tasked with producing reports, usually using data tables. What if computers were able to read data and create phrases and paragraphs with a human-like structure?

Spell-checking apps like Grammarly have already cemented a position in the toolkits of copywriters and managers, proving that NLP is not just useful for academics. It offers practical suggestions on how to improve language clarity and eliminate errors.

Additionally, the software analyses text using NLP technology and offer ideas for enhancements. Moreover, NLP can provide more in-depth feedback, such as highlighting the absence of crucial details or adequate support for a claim, as well as identifying plagiarism.

There are additional possibly common examples of NLP and education working together. Increasing teachers' perceptions of what is happening with their students and their capabilities by providing recommendations on enhancing fundamental writing abilities.

Enhanced healthcare

Hospitals and other healthcare facilities produce a lot of unstructured data, both digital and paper-based. Governments can use  NLP technology to gain useful insights, allocate the proper funds, and make decisions that will improve a nation's overall healthcare system.

For example, NLP is used by the Lister Hill National Center for Biomedical Communications at the US National Library of Medicine to "de-identify" clinical information in narrative medical reports, maintaining clinical expertise while safeguarding patient privacy. Large biomedical datasets have been broken down using topic modeling for medical surveillance.

The National Center for Toxicological Research identified relevant drug groups from more than 60,000 drug adverse event pairs—that is, pairs of drugs and adverse events in which the adverse reaction is caused by the drug—using topic modeling on 10 years' worth of reports extracted from the FDA's Adverse Event Reporting System (FAERS).

Also by handling some of the front-line duties of interviewing and evaluating patients, the use of NLP in the form of virtual nurse assistants might save $20 billion yearly.

Improved Litigation

Researchers from Duke Law, the University of Southern California, and Stanford Law School recently compared an NLP-driven contract review platform against a group of attorneys in a study. Compared to the average of 85% for qualified lawyers, computers were 94% accurate on average in identifying dangers in Non Disclosure Agreements (NDAs), one of the most popular legal contracts used in business.

Even better: The computers finished the job in 26 seconds as opposed to the lawyers taking a 92-minute completion time. This demonstrates that NLP is capable of competing with humans in activities and raises the possibility of producing even more effective outcomes.

Ethical Concerns in NLP Applications in Public Sector

As discussed above NLP systems have many benefits for the public sector. But its use in the public sector also raises ethical concerns that need to be dealt with carefully to stop biases and discrimination from getting worse and spreading.

Training on biased data

When NLP systems are trained on data that is already biased, it can make the biases even stronger and lead to unfair or discriminatory results. This is especially scary in the public sector, where NLP systems are used to work on a large scale and little biases can make decisions that might have a negative impact on people's lives. So data originality and authenticity posses a big challenge.

Implementing safeguards

The appropriate safeguards must be in place for public sector entities that develop and use NLP systems. This involves thoroughly evaluating and validating NLP systems to make sure they are fair, transparent , and accountable. These systems have the potential to mislead and control a large population that relies on them and uses them regularly if they are not authorized and deployed with the necessary precautions.

Promoting diversity and inclusivity

Promoting diversity and inclusion in the development of NLP systems is an important part of addressing ethical concerns. This means that there is a need to engage different people in the training data and various stakeholders need to be involved in designing and deploying NLP applications.

Establishing accountability

There needs to be a clear way to find out who is responsible for what NLP systems decide. This could mean that the public sector needs to set up regulatory frameworks and standards to guide the creation and use of NLP applications. This will help in the long term when issues related to systems relying on NLP show up.

Informing Individuals

Lastly, public sector organizations should make sure that the people whose data is being used by NLP systems know exactly what these systems are for and how they might affect them. They should also be able to see the information about them and change it if they want to. By addressing these ethical concerns, public sector organizations should make sure that NLP systems are built and used in a fair, open, and accountable way.

Conclusion

NLP can help improve public services in most countries and can assist the general public in overcoming operational challenges and enhance overall functionality. NLP enables governments to gain valuable insights and become more effective and efficient, as well as cut back on bureaucracy-related expenditures while enhancing citizen services and ensuring safety.

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