Digital Transformation in Government: The Role of Generative AI
Unlocking Value from Data
In 2024, the ongoing process of digitalization further enhances the efficiency of government programs and the effectiveness of policies, as detailed in a previous white paper. Two critical elements driving this digital transformation are data and artificial intelligence (AI). AI plays a pivotal role in unlocking value from data and gaining deeper insights into the extensive information that governments collect to serve their citizens.
The Growing Demand for Generative AI
As the demand for generative AI is expected to grow this year, it becomes imperative for the public sector to embrace responsible use of this technology. Only by doing so can governments establish themselves as trustworthy stewards.
Traditional AI vs. Generative AI
To understand the unique challenges that are posed by generative AI compared to traditional AI, it helps to understand their fundamental differences.
Traditional AI
Traditional AI primarily relies on algorithms and extensive labeled data sets to train models through machine learning. These models can provide recommendations or identify certain behaviors by recognizing patterns and adhering to predefined rules. For instance, traditional AI is used to improve the effectiveness of spam email filtering, enhance movie or product recommendations for consumers, and enable virtual assistants to help individuals in seeking information.
Generative AI
Generative AI is emerging as a valuable solution for automating and improving routine administrative and repetitive tasks. This technology excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks. It can effectively identify, summarize, convert, predict, and generate content from large data sets. Implementing this technology in the public sector can significantly improve efficiency, enabling organizations to complete their daily tasks with a fraction of the resources.
The Potential of Generative AI in Government
Generative AI presents an unprecedented opportunity to enhance various aspects of government operations and improve services for citizens. It can empower government workers with more powerful tools for answering questions and conducting research. Tasks such as contract writing and management, which are both time-consuming and crucial, might greatly benefit from the application of generative AI.
The US Department of State’s Approach
Last year, the US Department of State sought feedback on the challenges and security considerations of introducing generative and natural language processing AI into its network. A June request for information from the State Department revealed their aim to improve worker efficiency and accuracy in repetitive tasks related to market research and acquisition planning for contract writing. Machine learning-trained generative AI might help in drafting new contracts based on this research.
Implementing Generative AI Responsibly
The remarkable generative capabilities of this emerging AI technology raise questions about its responsible use in the public sector. For example, contract managers need to know that the original research is faithfully converted into a legally binding contract for two or more parties.
Key Pillars for Responsible AI in Government
IBM’s AI development centers around 5 fundamental pillars to help ensure trustworthy AI. Government leaders should prioritize these pillars when considering the responsible development, training, and deployment of AI:
Fairness
Fairness in an AI system refers to its ability to treat individuals or groups equitably, depending on the context in which the AI system is used. That means countering biases and preventing discrimination that is related to protected characteristics, such as gender, race, age, and veteran status.
Privacy
Privacy pertains to an AI system’s ability to prioritize and safeguard consumers’ privacy and data rights while complying with existing regulations related to data collection, storage, access, and disclosure.
Explainability
Explainability is important because an AI system must be able to provide a human-interpretable explanation for its predictions and insights in a way that does not hide behind technical jargon.
Transparency
Transparency means that an AI system must include and share information on how it was designed and developed and the data or data sources used to feed the system.
Robustness
Robustness is an AI system’s ability to effectively handle exceptional conditions, such as abnormalities in input. It helps to ensure consistent outputs.
Conclusion
As the public sector continues to embrace AI and automation to solve problems and improve efficiency, it is crucial to maintain trust and transparency in any AI solution. Teams should have the ability to comprehend and manage the AI lifecycle effectively. Proactively adopting responsible AI practices is an opportunity for all of us to improve.
FAQs
Q: What is generative AI, and how does it differ from traditional AI?
A: Generative AI is a type of AI that excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks. It can effectively identify, summarize, convert, predict, and generate content from large data sets.
Q: What are the key pillars for responsible AI in government?
A: IBM’s AI development centers around 5 fundamental pillars: fairness, privacy, explainability, transparency, and robustness.
Q: How can governments ensure the responsible use of generative AI?
A: Governments can ensure the responsible use of generative AI by prioritizing the 5 key pillars, maintaining human oversight, and providing clear processes for AI development and deployment.
Q: What are the benefits of implementing generative AI in government?
A: Generative AI can significantly improve efficiency, enabling organizations to complete their daily tasks with a fraction of the resources. It can also empower government workers with more powerful tools for answering questions and conducting research.
