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Simplifying public services administration with law as code technology

März 7, 2024 / Mona Hillenbrand | Christian Schieb

Short on time? Read the key takeaways:

  • The retirement of the baby boomer generation leads to a smaller workforce facing increasing workloads and outdated IT systems in public services.
  • AI technology transforms complex, unstructured legal texts into automated, structured rules, streamlining the management of policies and regulations.
  • A blend of data mining, aggregation, process automation, and AI, law as code creates clear, actionable legal rules, enhancing policy accessibility and efficiency.
  • To implement law as code effectively, focus on identifying specific problems, adhere to ethical AI practices, consult domain experts, and anticipate resistance, facilitating a smoother transition in the face of a shrinking public sector workforce.

The “doing more with less” phenomenon has come to the public sector. As the baby boomer generation retires, the workforce will be left with fewer people to handle the ever-hastening pace of work related to providing public services. That leaves the working population balancing a growing workload while, in many cases dealing with legacy IT systems that could hinder the need to accelerate change. 

The good news is that technology provides a solution. For example, most business processes are guided by established rules or standard procedures, providing a framework for consistent and efficient operation. Even when dealing with crucial information — especially laws and regulations — technology like AI can help transform unstructured text within policies and documents into rules that are automated. 

What were once tomes of unstructured data that were challenging to sift through can now help the public sector continue driving regulatory change with fewer people. Here’s how.

Navigating the maze of rules and regulations 

New tools and applications of proven technologies – such as data mining and extraction, data aggregation and process automation – can be combined with AI. Together, they can synthesize diverse and sometimes contradictory data elements into cohesive legal rules.

Properly deployed, these technologies can help eliminate repetitive work, free workers for more valuable tasks, reduce the number of broken processes and decrease human error. The combination of these technologies can manifest as “law as code” solutions. 

Other industries — especially those with fewer regulations — are already using AI to do more with less. But in the public sector, ensuring AI usage is ethical and transparent is critical. Here's an example of how law as code can help the talent pool achieve more with less.

A government’s finance office is working to extract the legal rules about family assistance programs. Instead of a legal expert, sifting through tomes of rules and no central source of information, law as code can search for a specific rule using AI and extract the pertinent information. Then, the legal expert can review the extract rules to ensure accuracy. With a significant portion of the public sector workforce approaching retirement, there's a looming risk of losing vast institutional knowledge. That's where technology can help. These challenges can be addressed with the right tools — such as law as code technologies — to make lives simpler and more efficient. 

What is law as code?

Law as code combines data mining, aggregation, process automation and AI. These technologies synthesize diverse and sometimes contradictory data elements into clear, actionable legal rules.

Here’s how they work together: 

  1. First, AI-driven tools extract targeted information from legal texts and integrate them, creating rules.
  2. Leaders then review and confirm each rule, providing expert verification.
  3. Once the rules are verified, they’re added to a rules-based engine, which is fully documented and auditable.
  4. Automation at the final step allows users to easily access information from the rules-based engine, enhancing their understanding of policies without the agency needing to build new software.  

One fundamental principle underpins this methodology: transparency. As the EU sorts out the AI Act, the public sector knows it must meet stringent requirements to use AI. The auditability of the law as code technology ensures that every piece of information can be traced back to its source, fulfilling legal requisites. Repeatability ensures the process can be consistently replicated, strengthening its reliability. Both characteristics help ensure public sector organizations use transparent and ethical AI practices. 

Getting started with law as code 

Technology, like law as code, can make the complex simple, the unclear clear and the inaccessible accessible. 

On your journey to simplifying legal access, keep these tips in mind:

  1. Identify the problem: Start by pinpointing the issue you want to address. Although vendors may dazzle you with advanced tools, focus on what would bring immediate value to your organization. 
  2. Use ethical and transparent AI practices: Meet the stringent requirements set for public organizations that use this technology. The full auditability of the technology promotes transparency as auditors and lawyers can understand the decision-making processes.
  3. Consult the experts: Partner with professionals with deep domain knowledge to expedite the implementation process. You don’t want to be a vendor’s guinea pig. 
  4. Expect resistance: New technologies often face opposition. However, naysayers can usually be converted into advocates by focusing on the greater public good and the impending shortage of public sector workers. 

As the public sector is teetering on the edge of a large portion of its workforce approaching retirement, technology — such as law as code — can help organizations maintain quality service while reducing institutional knowledge. Simplifying legal access can help both public servants and citizens alike.

Learn how Unisys Smart Rule can help your organization automate the implementation of legal texts and accelerate decision-making. 

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