U.S. Department of Labor: Apply AI and automation technologies to unlock the value of data

Government is awash with data. To gain insights into this data to better serve citizens, agencies are applying technologies such as automation, RPA (robotic process automation), ML (machine learning) and AI (artificial intelligence) to better manage data and improve methods and workflows . The DOL (U.S. Department of Labor) is one of these agencies that are developing unique ways to use emerging technologies in their data-rich environments.

Sanjay Koyani, Chief Technology Officer of the U.S. Department of Labor, and his team are working hard to integrate various innovative technologies such as responsible AI, RPA, and chatbots, and plan to create an enterprise-level data platform at the Department of Labor. At an upcoming AI in Government event on September 15, 2022, Sanjay will explore the sector’s AI, automation and data journey, what needs to be done to explore cultural change considerations, and how to best Good at identifying problems and customer needs and then developing solutions to truly identify and solve those problems.

In a preview interview with Forbes, Sanjay shares how the Department of Labor is applying AI and ML in data-rich environments, some of the challenges in adopting transformative technologies in the public sector, and how the U.S. Department of Labor is looking at Trustworthy and responsible AI.

What innovative ways are you using data and AI to help the Department of Labor (DOL)?

Sanjay Koyani: All IT modernization initiatives work toward our goal of being the best in federal IT solutions, which supports our Department of Labor mission to enhance service to the American public and provide better Good customer service to support a more digital workplace.

A little more than a year ago, we created a new branch within the Technology, Innovation and Engineering (TIE) division that specializes in emerging technologies and creating human-centered design approaches for the Department of Labor’s future technologies. The first emerging technology capability we launched and are working to expand across the enterprise revolves around the use of automation – Robotic Process Automation (RPA). Over the past year, we have launched five RPA bots—software applications used to automate repetitive, rules-based administrative tasks—and are piloting six more. We are currently developing several RPAs for future use and exploring additional opportunities across all departments within the Department of Labor. The overall goal is to allow employees to focus their capabilities on mission-critical work rather than administrative-based tasks, and to lay the foundation for other advanced technologies such as machine learning and artificial intelligence.

In TIE, we are also exploring how to use AI as a service more responsibly to improve performance and add value. We have multiple AI pilots underway and we are innovating in the cloud by using native AI support capabilities to assess program needs such as speech-to-text, text-to-speech, translation services, and extraction of text and structured documents for faster decision-making form recognition service. At the same time, we are also beginning to explore practices for designing and evaluating AI ethically and responsibly so that we can scale it with greater confidence.

To advance our AI and automation efforts, our team is also enhancing our analytics capabilities by creating the Enterprise Data Platform to support data-based decision-making in innovative ways. Data is the foundation of AI and machine learning, so we are investing in data management and analysis tools. Using Technology Modernization Funding allocated to this program, the Department of Labor can enhance data management and advanced analytics capabilities, enhance data sharing and sharing across departments, and make faster and better decisions. We can also advance elements of the Executive Order on Worker Empowerment to provide investigators and policy teams with better intelligence, high-quality and timely worker protection data that makes jobs safer.

In what areas did you start your data and cognitive technology projects?

Sanjay Koyani: We have started identifying projects through our innovation incubator, which helps evaluate proofs of concept – demonstrating the risks and evaluating them against existing tools. This allows us to expand our current pilot programs to see if they can address additional problems and explore innovative solutions.

Another tactic we have used recently is an organization-wide Bot-a-Thon, which helps inform employees about the use of bots and understand how they can help employees with administrative tasks such as reporting, filling out forms, or research . The results involve nine different robotic processes being developed starting in FY21, with five robots already in use saving thousands of hours of work.

What are the unique opportunities for the public sector when it comes to data and artificial intelligence?

Sanjay Koyani: We have greater visibility and more focus on the importance of modernizing IT in government and how IT impacts multiple government services. The current presidential administration has made IT modernization, including data and AI, a priority. Congress continues its focus on IT with the Federal IT Acquisition Reform Act (FITARA), which puts agency CIOs in control of IT investments and rates agencies in seven key IT areas. Cybersecurity breaches have also refocused attention on how AI can help the public sector mitigate threats and respond more quickly to potential risks.

What use cases can you share of successful applications of AI?

Sanjay Koyani: We developed a new user-inspired website for the Department of Labor’s Employment and Training Administration (ETA) based on customer-centered design and enhanced customer experience by incorporating AI. As a result, AI helps improve candidate access/opportunity matching on Apprenticeship.gov.

Another example is our use of AI-powered form recognition services to speed up beneficiary determination. Our team evaluated how AI-powered cloud technology could assist claims examiners in evaluating benefit forms for accuracy and fraud to make decisions faster. Using existing cloud technology, we train AI models to extract and organize data from multiple claim forms so that examiners get comprehensive information faster. Before this, reviewers spent a lot of time manually sorting and comparing forms instead of focusing entirely on beneficiary support and faster decision-making.

Can you share some of the challenges the public sector faces with AI and ML?

Sanjay Koyani: I will touch on some of the challenges. One is data management, which is a big focus for the Department of Labor. While having lots of data is a good thing, you need to know what information is available and know how it is used. To use AI and ML correctly, you need to understand what data exists, classify it, and align agency stakeholders on how the Department of Labor uses the data to make faster and better decisions. This requires ongoing education and investment in our data strategy.

Human-centered design is also key to AI/ML. Therefore, you must ensure that you communicate with all relevant stakeholders to understand the process and how they will use the technology. This is an important moment to decide whether AI/ML can solve the problem. Not all problems can be solved with technology.

Another key challenge is cultural acceptance. Culture change can be difficult, so be sure to demonstrate the benefits at work, how to use new technology responsibly, and how it can be used throughout the organization.

Ultimately, for the Department of Labor, department-wide scalability is the long-term goal. So we're looking at cultural and technical considerations, assessing effectiveness, and then building on our successes.

How do you address the privacy, trust and security issues surrounding artificial intelligence?

Sanjay Koyani: We are using the Responsible AI Framework to ensure that AI is used in a trustworthy manner. The Department of Labor is working with nonprofit practitioners and government subject matter experts to end bias in AI algorithm development and help us navigate the complex landscape of creating safe AI.

In addition, we currently have a number of policies and procedures in place to help address safety issues. These include sound governance policies and an overall strategy that considers security from the outset.

In the "Executive Order on Responsible AI" (Executive Order on Responsible AI), OSTP (White House Office of Science and Technology Policy) outlined 10 principles for responsible implementation of AI systems. Additionally, privacy is an important consideration when considering the use of AI systems. Not only do we want to ensure that we are not introducing bias, but we also want to ensure that the privacy of those whose information is included in the data is protected. We comply with federal regulations and employ specialized privacy assessments in this regard.

How do you train artificial intelligence technical talents?

Sanjay Koyani: We are building enterprise architecture and IT governance processes to support the use of all emerging technology solutions. This will help ensure consistency of tools to support the organization's business needs and standardized processes. Another way we develop AI technical talent is through education, training and hiring subject matter experts. For example, we recently had a Presidential Innovation Fellow (PIF) evaluate our Trustworthy AI pilot use cases that support the Administration’s Executive Order on Promoting the Use of Trustworthy AI in the Federal Government. Our PIF allows us to work with agency experts to design and test new models to evaluate how we design, develop and deploy AI in a more responsible way, which helps increase transparency and gives people confidence in AI scaling.

What artificial intelligence technologies are you most looking forward to in the next few years?

Sanjay Koyani: I look forward to seeing more responsible AI testing initiatives that will help fill the gaps as we modernize legacy IT systems and use more automation to enable transformation. Each initiative will allow us to mature our enterprise architecture and use emerging technologies.

Another area I’m excited to see AI assist in is cybersecurity. Given the changing environment and continued pressure on resources to protect systems and network solutions, I think there will be more solutions that help automate responses to cyber threats and reduce risk for organizations.

In an upcoming talk in September 2022, Sanjay will delve into some of the topics discussed above and share his team’s work on integrating innovative technologies such as responsible AI, RPA, and chatbots Highlights.

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