Generative AI and the Future of Government Services: Promise and Prudence

Generative AI

Generative AI refers to a type of artificial intelligence that can create new, original content such as text, images, music and more, rather than just analyzing data. The key capability that has captured significant attention recently is the ability to automatically generate natural language.

  • This capability has sparked interest among several governments because of the potential applications of generative AI within the public sector.

Chat conversational AI tools like ChatGPT launched in 2022 have brought widespread public awareness to this technology. Other examples include Dall-E for generating images and Claude for text.

  • These systems are trained on massive datasets to learn patterns and produce outputs highly similar to the examples used in training. When given a user prompt, they can respond by predicting the most likely next sequence of words based on their learned data. This allows for contextual responses tailored to the supplied input.

In essence, advances in machine learning now enable AI to produce remarkably fluent, human-like language and other creative content on demand versus just following pre-configured rules.

Potential Government Applications

Despite current limitations, large language models (LLMs) and other forms of generative AI hold promise for enhancing public sector productivity in several areas:

  1. Accelerating citizen services by rapidly retrieving information to power digital self-service platforms and route queries appropriately using natural language generation tools.
  2. Reducing staff workloads; for example, by using generative AI to draft routine communications to allow refocusing on complex tasks requiring human judgment.
  3. Using LLMs to analyze and summarize large volumes of data to uncover insights.
  4. Leveraging machine translation and natural language generation models to improve accessibility and localization of public-facing information.
  5. Performing specialized processing like financial analysis, contract review, regulatory compliance checks through cost-effective, scalable, and accurate generative AI systems.

Limitations of generative AI and LLMs

LLMs are trained to predict probable next words without deeper understanding of meaning or truthfulness. This allows fluent generation, but responses may not always be accurate or appropriate.

When deploying generative AI systems, organizations should implement responsible practices to address key limitations:

  1. Hallucination risk: Because these models prioritize plausible outputs over accuracy, they may generate convincing yet incorrect or nonsensical text.
  2. Lack of reasoning: LLMs predict sequences rather than truly understand content and logic.
  3. Potential for bias: Since these models learn from patterns in data, they risk perpetuating offensive assumptions or stereotypes if the training process does not proactively address bias.
  4. Lack of expertise: Without specialized training, LLMs do not possess true contextual competence in sensitive domains like legal or medical advice. Their capacities are broad but shallow – they can articulate ideas fluently but cannot replace years of professional nuance.
  5. Lack of lived experience: LLMs have no personal context or emotions. Their outputs may appear convincingly human-like, but they do not have true consciousness or understanding.
  6. Accessing current information: Earlier LLMs could not retrieve real-time external data, constrained by their training sets. Now some models can incorporate dynamic internet access. However, they still lack broader frames of reference that humans accumulate over time.
  7. Limited memory: LLMs track limited conversational context in their neural networks. They may lose coherence in long discussions.
  8. Lack of explainability: The inner workings of generative AI models can be opaque, operating like “black boxes” that produce outputs without revealing their reasoning.

Given such uncertainties in explainability along with other limitations, autonomous generative AI currently seems unsuitable for scenarios directly impacting human health, safety, or civil rights without human oversight.

However, capabilities are evolving rapidly. As providers address limitations and risks, responsible use cases may expand.

The UK Framework for Government

In the UK, an initial guidance on generative AI released in June 2023 encouraged civil servants to gain fluency with the technology while staying cognizant of risks. The Central Digital and Data Office (UK) recently published an expanded framework that provides practical considerations for anyone developing or implementing a generative AI solution, building on that early guidance.

These principles and this framework set out a consistent approach for the use of generative AI tools for UK government.

Principle 1: You know what generative AI is and what its limitations are

Principle 2: You use generative AI lawfully, ethically, and responsibly

Principle 3: You know how to keep generative AI tools secure

Principle 4: You have meaningful human control at the right stage

Principle 5: You understand how to manage the full generative AI lifecycle

Principle 6: You use the right tool for the job

Principle 7: You are open and collaborative

Principle 8: You work with commercial colleagues from the start

Principle 9: You have the skills and expertise that you need to build and use generative AI

Principle 10: You use these principles alongside your organisation’s policies and have the right assurance in place

Generative AI promises notable gains in productivity. The framework seeks to aid readers in understanding generative AI, guide developers building generative AI systems, and most crucially, outline vital considerations for utilizing generative AI safely and ethically.

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