Ethical AI Starts Here: Understanding Guardrail Technology

The headlines say it all: “Hiring tech giant prevented people over 40 from getting hired” or “AI firm faces lawsuit over son’s suicide” or even “When an algorithm can send you to prison.” Clearly, with all the benefits of AI, the downsides can be catastrophic for a business. When an AI algorithm fails, the results can lead to a loss of customer trust, significant regulatory scrutiny, and legal altercations. Incidents like these present hurdles for companies moving AI workloads from proof of concept to production. Avoid these headlines by implementing guardrail technology.

By implementing guardrail technology, reputable enterprises can begin to address the challenges of compliance, trust, and ethics.  Read on to find out how you can start on the right path.

What is guardrail technology?

Guardrail technology provides for the implementation of safety boundaries. These prevent harmful outputs and ensure responsible operation. They are analogous to an autopilot system. They ensure an AI stays on course by preventing unintended behavior, biases, security risks, and compliance violations.

Guardrails broadly fall into two critical categories: input guardrails and output guardrails.

• Input Guardrails examine data fed into an AI model before it processes a request. They aim to prevent problematic, biased, or malicious inputs. Examples include:
  • Data Validation and Sanitization: Ensuring input data is clean, complete, and free from anomalies. These could lead to erroneous or biased outputs.
  • Bias Detection and Mitigation: Identifying and correcting demographic or systemic biases present in the input data. This prevents their propagation through the model.
  • Prompt Injection Prevention: Detecting and blocking malicious prompts designed to elicit harmful responses.
• Output Guardrails evaluate the AI system’s generated responses before they are delivered to the user. They filter out undesirable or harmful outputs. Examples include:
  • Harmful Content Detection: Flagging outputs containing hate speech, abusive language, profanity, or other harmful content.
  • Bias Detection in Output: Continuously monitoring for any emergent biases in the generated content.
  • Hallucination Detection: Verifying factual accuracy and identifying “hallucinations” before they reach users.

The first step toward responsible AI

Implementing guardrails is a first step towards Responsible AI. Responsible AI is an overarching approach to designing, developing, and deploying AI systems. It is fair, accountable, transparent, and beneficial to society, encompasses the ethical, legal, and societal implications of AI and it adheres to key principles such as:

• Fairness and non-discrimination
• Transparency and explainability
• Accountability
• Privacy and security
• Robustness and reliability
• Human oversight

Get started with Guardrails

For enterprises looking to implement guardrails,  the Guardrails AI Hub provide modular components called “validators.” These probe GenAI models for behavioral, compliance, and performance metrics. These validators can be mixed and matched to build custom guardrails for critical production paths. The hub is available free on GitHub.

SUSE also provides a primer for jumpstarting development of guardrails with a Greendoc. The “GreenDoc – Use Guardrails with SUSE AI” provides a step-by-step guide to implementing guardrails in SUSE AI.

SUSE AI is a comprehensive, cloud-native lifecycle management platform. It is engineered to deploy, run, and manage AI applications across your enterprise infrastructure. SUSE AI provides zero trust security and observability for AI workloads. It gives actionable insights and ensures a secure supply chain with curated and sanitized AI components. The platform is designed with a modular architecture that fosters faster innovation. It is adaptable to new business demands, including guardrails technology.

Infosys and SUSE: Partnering for Responsible AI

A truly comprehensive approach requires combining robust technology with ethical frameworks. SUSE recently announced its expanded partnership with Infosys. This partnership focuses on their Infosys Responsible AI offerings. These include an advanced, open source Responsible AI Suite.  The Responsible AI Suite is built around a “Scan, Shield, and Steer” framework. This suite provides advanced defensive technical guardrails. These detect and mitigate issues like privacy breaches and security attacks. In addition, SUSE AI offers enhanced model transparency for insights into AI-generated output. It provides customization and scalability across industries. This partnership between SUSE and Infosys tackles some of the key blockers of ethics and transparency. By implementing these tools and methodologies on the SUSE AI platform, you can now move AI PoCs out of purgatory and into production with confidence.

Secure your AI; Safeguard your business

Building safer and transparent AI systems demands a comprehensive strategy. Guardrails technology is an essential component for robust protection. By proactively implementing both input and output guardrails, you can ensure output aligns with user intent. You can prevent the generation of harmful or biased content and maintain user privacy and data security. You can ensure transparency and explainability. Guardrails, combined with integrating ethical considerations and robust processes throughout the entire AI lifecycle and fostering a culture of responsibility, are vital. They foster trust, ensure compliance, and ultimately unlock the full transformative potential of artificial intelligence.