Skip to content
footer (1)

Building Proper Guardrails for Responsible AI

Less than 1 minute Minutes
by Nirmal Ranganathan, VP of Engg, AI, Rackspace Technology

In our recently published 2024 Global AI Report, we gathered insights from 1,420 IT decision-makers spanning diverse industry verticals. The findings indicate a pivotal shift: More than 60% of respondents reported that their organizations’ AI initiatives have progressed from ideation to implementation in the past year. As AI moves from theoretical planning to real-world application, our study also uncovered current perspectives on responsible AI usage and evaluated how prepared organizations are for implementation of ethical AI principles.

The results reveal an uncertain landscape: 75% of respondents say they would unconditionally trust AI-generated answers, but only 20% believe these outputs should always involve human validation. Conversely, nearly 40% expressed concern that their organizations lack adequate safeguards to ensure responsible AI use. These disparities underscore a critical gap between confidence in AI capabilities and the implementation of necessary precautions against potential misuse.

Below are some essential guardrails you can establish to help ensure that your organization’s AI solutions are managed appropriately and leveraged responsibly.

Maintain human oversight

Despite the advancements in AI, particularly in large language models (LLMs), challenges remain, especially with complex, industry-specific use cases and scenarios. In sectors like healthcare and finance, where accuracy and compliance are paramount, substantial human oversight is indispensable. While specialized models can be tuned for specific tasks, fully autonomous AI systems are still a rarity, highlighting the necessity of human intervention.

Promote transparency

While the first wave of AI development was largely focused on the IT and technology departments, the second wave has been very business-oriented. As AI deployment extends into core business areas — including marketing, sales, HR, finance and engineering — transparency becomes essential.

AI operations must be guided by clear, well-defined policies, comply with regulatory standards, and maintain openness about data usage and privacy implications. These measures are fundamental to managing AI responsibly across all facets of the organization. Accountability goes hand-in-hand with transparency, requiring a proper mechanism to audit model decisions and impacts on stakeholders, and a process to report issues and complaints.

Ensure accountability and data integrity

We are seeing issues related to potential copyright violations and the use of data without permissions. When models generate similar outputs to content that’s been created by others, it should naturally raise concerns.

To rebuild trust, AI systems must incorporate rigorous data validation processes. This includes verifying data sources, maintaining data provenance transparency, and ensuring data used in training and inferencing is accurately tagged and managed.

Address hallucinations, data validation and security

By now, we’ve all heard of AI ‘hallucinations’ where large language models (LLMs) generate responses based on nonexistent data. These incidents call into question the protocols of companies that build these models, and we believe that the solution lies in strengthening validation processes.

Many LLMs now provide options to trace the lineage and sources of their data. As new AI projects are deployed, the integration of rigorous data validation into every solution becomes crucial to ensure the accuracy of results. This includes implementing robust guardrails, tagging datasets with appropriate metadata for use during inferencing, or within the review, adjust and create processes for generative AI workloads.

Our 2024 Global AI Research Report sheds further light on some related challenges:

  • Security concerns: Highlighted by 38% of respondents as the principal challenge in AI adoption, security issues arise from the complexity of AI models and the extensive data they handle. These concerns necessitate advanced security protocols and enhanced threat detection mechanisms. The scarcity of skilled personnel exacerbates these challenges, underscoring the urgent need for upskilling initiatives to close the knowledge gap in AI technologies.
  • Accuracy challenges: As reported by 30% of respondents, ensuring reliable performance and accurate outputs from AI models presents significant challenges. Inaccuracies can lead to a wide range of negative outcomes — from minor disruptions to major errors with far-reaching consequences. This underscores the necessity for exhaustive testing and meticulous validation processes to mitigate such risks.

Securing the future of AI with robust guardrails

As organizations everywhere continue to embrace AI, the need for robust, well-defined guardrails has never been more critical. Our comprehensive approach, underscored by the insights from the 2024 Global AI Research Report, emphasizes the importance of maintaining human oversight, promoting transparency and ensuring accountability and data integrity across all AI deployments. These measures are not just best practices; they are essential safeguards that protect both the organizations and the public from the potential pitfalls of unchecked AI.

Download the 2024 Global AI Report now