Before 2022, software development primarily focused on reliability and functionality testing, given the predictable nature of traditional systems and apps. With the rise of generative AI (genAI) models, however, the concept of model and AI alignment has become increasingly crucial. I had the privilege of contributing to a groundbreaking report titled Align By Design (Or Risk Decline) that explores this necessity and outlines essential strategies for AI alignment.
Generative AI models are progressing from simple knowledge recall to handling complex tasks requiring advanced planning and reasoning. Developers can now equip these models with tools that allow them to interact with digital environments, such as APIs for databases, websites, and software. As these intelligent systems learn, adapt, and make decisions in unforeseen ways, their unpredictability renders traditional testing insufficient. Instead, aligning these models with corporate and customer values through technical and governance mechanisms is essential.
The “Align By Design” Imperative
Stuart Russell, in his book “Human Compatible,” observes, “Until recently, we were shielded from potentially catastrophic consequences by the limited capabilities of intelligent machines and the limited scope they have to affect the world.” This shield is weakening as AI capabilities continue to advance rapidly.
The report defines “align by design” as a proactive approach to developing AI systems, ensuring that they meet business goals while adhering to company values, standards, and guidelines throughout the AI development lifecycle. This involves technical adjustments and governance guardrails to align AI models with human values and goals. Our findings indicate that alignment is best when it is integrated into the design process, rather than just being something you do at the end of development.
First, Understand Technical Adjustments For AI Alignment
In addition to providing a framework for ensuring that alignment is part of your overall AI application design, understand the specific alignment techniques for emerging generative models, such as:
- Fine-tuning. Methods such as supervised fine-tuning and reinforcement learning from human feedback are essential for aligning AI outputs with desired outcomes. Techniques like low-rank adaptation and direct preference optimization tailor AI models for specific tasks.
- Prompt enrichment. Beyond grounding models with business data, techniques like metaprompts offer higher-level instructions and examples. These techniques can guide AI behavior, reducing errors and minimizing the risk of generating deceptive responses. “Guard-railing” models can insert statements into prompts to keep model responses within a bounded set of acceptable outputs.
- Controlled generation. These techniques take prompting a step further. For example, chain of thought involves prompting AI models to articulate their reasoning process step by step before arriving at a final conclusion or answer, while ReAct is a prompting framework that combines both reasoning and action in a single prompt to guide AI models in generating more accurate and contextually relevant responses.
Learn To Balance Model Helpfulness And Harmlessness
Our research highlights the critical need to balance model helpfulness with harmlessness. Overloading models with guardrails and tuning can diminish their effectiveness, while insufficient alignment may lead to harmful outputs or unintended actions. Extreme cases could result in agentic models becoming deceptive or pursuing unforeseen goals.
Governance gates are vital for maintaining this balance. Intent and output gates are essential alignment components: Intent gates govern user input by, for example, applying guardrails, while output gates assess model responses and attempt to redirect those that may cause harm. Some more advanced firms such as LivePerson are experimenting with language models for governance, while Microsoft Azure AI Content Safety filters unsafe content.
Addressing emerging risks is also crucial. AI systems might develop deceptive behaviors such as falsifying maintenance needs or hoarding resources, making detection difficult. Additionally, AI could exacerbate cybersecurity threats and societal divides through persuasive but manipulative content.
As AI’s reasoning and autonomy evolve, aligning these systems with corporate and human values to implement AI responsibly means blending technical alignment with strong governance. For further guidance, schedule an inquiry or guidance session with Brandon, Enza, and me as you explore next steps. I will also be at Forrester’s Technology & Innovation Summit in Austin, Texas, September 9–12. If you’re a technology, data, or analytics leader grappling with AI adoption, I hope to see you there.
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