AI guardrails: The importance of human in-on-out of the loop

The rapid ascent of AI has ushered in an era of unprecedented possibilities, transforming industries and redefining the way we work. Still, a critical question emerges: Just because AI can, does it mean it should? The future of work isn't a battle of humans vs. AI. What it should be is a symphony of humans and AI. However, without proper oversight and AI guardrails, this powerful collaboration can introduce significant risks: including bias, opacity, and failed compliance. 

Business AI use is dangerous without guardrails 

Untamed, unmonitored AI operation in critical sectors like finance or healthcare, connects to decisions that impact lives, in customer solutions handling unchecked AI stray far and wide from the standards set by your employees and company principals. Without clear AI regulation and robust AI risk management, the consequences can be dire.  

AI models, trained on historical data, can perpetuate and even amplify existing biases. These dangers extend to opaque decision-making processes, where the "why" behind an AI's output remains a mystery. Without proper frameworks, ensuring AI compliance with evolving legal and ethical standards becomes an uphill battle. This underscores the urgent need for proactive AI governance. 

Employees and AI: A collaborative paradigm 

Human-AI collaboration frameworks are essential. Instead of viewing AI as a replacement, we should see it as a powerful co-worker, but akin to a cross-departmental team for process resolution support and even process development. And just as your teams require clear processes, communication, and oversight to deliver quality work, AI agents demand similar environments.  This brings us to the key concepts of human involvement which should form the structure of AI business integrations: 

• Human in the loop (HITL): This means direct human intervention at key decision points. Humans review, validate, or override AI-generated outputs before execution. This is vital for high-stakes decisions. 

• Human on the loop (HOTL): Humans monitor the AI system's performance, intervening only when errors occur. This provides an oversight layer for more autonomous AI operations. 

• Human out of the loop (HOTL), or fully autonomous systems: This should only be considered for low-risk tasks. It still requires rigorous design and testing to ensure safety and reliability and, even then, an overarching human oversight mechanism should exist. 

Flowable: Your platform for responsible AI 

How can we ensure AI tools are used responsibly? The answer lies in establishing strong governance and control mechanisms. Flowable's platform offers the capabilities needed to design and implement these AI guardrails. It bridges AI innovation and responsible deployment by orchestrating human and AI tasks within visible, manageable, and auditable processes. Flowable provides the solutions to ensure: 

• Explainability: Understand why an AI made a particular decision. 

• Traceability: Track every AI-driven action and the data that influenced it. 

• Auditability: Reconstruct and verify the complete journey of a decision — crucial for compliance and scrutiny. 

Step-by-step guide: Designing reusable AI processes with Flowable 

Here's how to implement AI within your team using Flowable, focusing on building a system that fosters AI risk mitigation strategies and allows you to gain visibility of your AI agents' behavior. 

Step 1: Define the AI use case and risk profile Clearly articulate the AI function, data use, and the potential impact of its decisions. Categorize the risk level (low, medium, high) to determine the appropriate level of human intervention needed. For example, in a banking scenario, an AI flagging potential fraud might be considered high-risk, requiring HITL workflow integration. 

Step 2: Visually map the end-to-end process  

Identify AI tasks (e.g., data analysis) and insert human touchpoints for review (HITL) or monitoring (HOTL). Flowable ensures human involvement at critical junctions, structuring data flow for predictable AI results and avoiding undesired outcomes. 

Step 3: Refine AI prompts 

Use Flowable Design's built-in tools for testing and refining AI prompts. This streamlines development, ensuring optimal AI performance and transforming unstructured data into structured, actionable information. 

Step 4: Set up monitoring and alerting  

Configure Flowable to continuously monitor the performance of your AI agents, defining key performance indicators (KPIs) and thresholds. If an AI's accuracy drops, or unusual patterns emerge, Flowable can automatically trigger alerts to human operators.  

Step 5: Document and standardize  

Maintain comprehensive documentation within Flowable outlining an AI model's purpose, design, data sources, and human-AI interaction protocols. This ensures transparency and facilitates future audits, strengthening your AI governance framework. 

The path forward for responsible AI 

There’s no doubt the relationship between humans and AI holds immense promise. But realizing this potential requires a deliberate and structured approach to building AI guardrails. Flowable's platform empowers organizations to design auditable processes that are reusable across your business, and infuse explainability, traceability, and auditability into every AI-driven action. By embracing a customizable human-centric approach, you can ensure AI works intelligently, responsibly, and effectively, shaping a future where innovation thrives and remains practical to real business needs.