By Anahita Bilimoria, Decision Lab Senior Machine-Learning Engineer
An Essential Framework for Responsible AI Deployment
The promise of Artificial Intelligence is immense, offering solutions to humanity’s most pressing challenges. With the recent boom of Language Models and AI penetrating every domain, we are confronted with a fundamental truth: global adoption and subsequent progress rely solely on trust. While the performance of AI in every aspect of automated decision-making is phenomenal, there has been a rising concern for trust in AI, fueled by opaque decision-making and perceived biases. This challenge has given rise to stalled innovation, public apprehension and the risk of deploying technologies without adequate oversight. The urgency to build and maintain trust in AI has surpassed being simply a matter of ethics and has given rise to safety concerns as well.
Despite adaptive speed and response, autonomous AI’s unchecked deployment can lead to instability. The absence of clear dependability metrics and inadequate interpretability methods raise trust questions across diverse AI. Although some transparent AI exists, rapid critical adoption in a volatile environment, coupled with regulation and public concern, necessitates urgent trust-building. AI TRiSM provides a repeatable framework (trust, security, privacy, transparency) to address these risks.
What is AI TRiSM?
Gartner, a leading research and advisory company, defines AI TRiSM (AI Trust, Risk and Security Management) as a framework that ‘ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection’. This ensures that models that follow this framework are not unethical, unfair or biased. While most AI solutions focus on model performance, AI TRiSM adds a layer of model responsibility, urging developers to strike a balance between the two.
Despite being a framework of individual principles, AI TRiSM enables the fulfillment of each principle through conscious and targeted steps.
Trust
Entailing the concepts of transparency, fairness, reliability, privacy and safety; this pillar ensures that models offer accountability and build trust. This component of AI TRiSM requires models to offer explainability, either by using Explainable AI (xAI), which are models designed to be interpretable, or artificially inducing explainability in their decision-making processes. Models trained on data are prone to biases in the data itself, leading to discriminatory model outcomes. Techniques like a thorough Exploratory Data Analysis (EDA) – which involves visualizing and summarizing data to spot imbalances – and bias detection methods can help gain insight into the biases in data. Regulations like GDPR and CCPA ensure data privacy and security. While models can’t follow these regulations directly, you can ensure your AI solution does by implementing appropriate data handling and storage practices! Autonomous solutions can build trust by introducing kill switches, pathways for human intervention, and safe operation mechanisms to ensure safe execution even in unexpected situations.
Risk
This pillar involves identifying and managing risks associated with your AI solution throughout its lifecycle. You can map up the risks associated with your solution. Some key aspects can include:
- Performance risks (e.g., model drift, accuracy degradation)
- Ethical risks (e.g., bias, lack of fairness)
- Security risks (e.g., adversarial attacks, data breaches)
- Operational risks (e.g., deployment failures, integration issues)
The solution lifecycle must include allocated resources for identifying, evaluating, and mitigating risks proactively throughout the solution’s development and deployment. Identifying these risks proactively helps stakeholders make informed decisions about the deployment and ongoing use of the AI solution. Ensuring the risk register includes all potential risks will highlight the potential gaps in your solution and enable mitigation strategies. Your team can have established roles, responsibilities, and policies for managing AI risks effectively.
Security
Each type of AI comes with its own security issues, like adversarial attacks (subtly changing input data to fool the AI), data poisoning (injecting malicious data to corrupt training), and model stealing (recreating a proprietary model). Achieving security in the model performance involves achieving security throughout the solution lifecycle, following a ‘Security by design’ approach to developing your solution. One must secure their data (incoming and outgoing), adopt techniques to protect the AI models, infrastructure, and APIs. While this principle shares its goal with standard security for software solutions, in AI TRiSM this also means making sure your model is not ‘hackable’ in ways specific to AI vulnerabilities.
How can you adopt AI TRiSM as a company?
While developers can tackle components individually, companies can also embrace it as a complete framework. The following steps outline how your company can introduce AI TRiSM to your organisation:
- Adopt AI TRiSM across your entire solution lifecycle (discovery to evaluation), documenting observations and decisions in a final report. A company-wide template standardizes AI TRiSM implementation for all projects.
- Ensure company-wide awareness of AI TRiSM through clear communication channels. Document model audits and communicate identified risks with proposed mitigation strategies to relevant stakeholders.
- Provide thorough training and education on AI TRiSM principles and practices across the organization, perhaps through workshops or online modules.
- Establish partnerships with entities that have a strong focus on AI TRiSM to leverage their expertise and insights.
Employing TRiSM in AI offers several key benefits, including building trust in AI systems, mitigating potential risks through pre-emptive resolution, ensuring compliance with evolving regulations, and fostering sustainable and transparent AI growth.
This post has provided an overview of AI TRiSM and its critical role in the responsible development and deployment of AI. In upcoming articles, we will delve deeper into each of the core pillars – Trust, Risk, Security, and Transparency – exploring the specific challenges, techniques, and best practices associated with building trustworthy AI systems.
Investing in AI TRiSM is an investment in the long-term value and viability of AI. By embedding these principles into our processes, we build a foundation of trust that will be crucial for the continued adoption and positive impact of artificial intelligence!
Next Steps
Navigating the complexities of AI TRiSM – ensuring trust, managing risk, and maintaining security – is crucial for successful AI adoption. At Decision Lab, we are committed to developing AI solutions that inherently incorporate these principles from design to deployment.
Our deep expertise in Explainable AI (xAI) provides the transparency needed for user confidence and regulatory compliance, directly addressing the ‘Trust’ pillar. We specialise in creating effective Human-AI Teaming paradigms, designing systems where human insight complements automated decision-making, ensuring robust operation and essential oversight to mitigate ‘Risk’.
Furthermore, our development processes are underpinned by strict adherence to rigorous ISO standards, demonstrating our commitment to ‘Security’, reliability, and quality across all our AI solutions. Partner with Decision Lab to build AI systems that are not only high-performing but also fundamentally trustworthy, secure, and aligned with responsible innovation principles.
To explore how Decision Lab’s AI solutions can benefit your organisation, get in touch. Let’s unlock the full potential of AI.
Author: Anahita Bilimoria, Decision Lab Senior Machine Learning Engineer
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