AI Robustness Report
A.I. Robustness: a Human-Centered Perspective on Technological Challenges
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption. Besides, robustness is interpreted differently across domains and contexts of AI. In this work, we systematically survey recent progress to provide a reconciled terminology of concepts around AI robustness.
Quantifying the Imperative for Robust AI
Our analysis reveals critical statistics underscoring the urgency and strategic value of investing in robust AI solutions for enterprise stability and growth.
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Papers Analyzed
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Peer-Reviewed Publications
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Projected Growth by
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section covers fundamental approaches to improve the robustness of AI models across their lifecycle: training data augmentation with malicious samples, ad-hoc training procedures and architectures, and post-training pruning and model fusion. Key strategies include generating adversarial attacks, augmenting data for both adversarial and non-adversarial robustness, and designing in-model robustness strategies through training and architecture design, and leveraging model post-processing opportunities like identifying unstable attributes and fusing models.
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Papers on Robust AI Analyzed in this Survey
AI Robustness Improvement Pipeline
Processing Training Data
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Designing In-Model Strategies
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Model Post-Processing
| Aspect | Adversarial Robustness | Natural Robustness |
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This theme explores progress in improving robustness for specific model architectures (Graph Neural Networks, Bayesian Neural Networks), specific application areas (Natural Language Processing, Cybersecurity), and specific concepts within Trustworthy AI (Explainability, Fairness). These approaches are tailored to domain-specific needs, bridging the gap with non-functional requirements and addressing less explored settings.
Robustness in NLP: Combating Adversarial Misspellings
Pruthi et al. [172] highlight the fragility of NLP models against word-level adversarial attacks, where altering just two characters per sentence can degrade classification performance to random guessing. They propose attaching a word recognition model to the classification model to combat adversarial misspellings.
Impact: Significantly improves model robustness against text-based adversarial attacks while maintaining classification accuracy, crucial for reliable NLP systems.
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Increase in GNN Robustness with Novel Architectures
This section focuses on procedures, benchmarks, and empirical studies to evaluate AI model robustness. It covers methodologies for computing safe radii or error regions, and the use of abstract interpretation for certified robustness. It also highlights benchmarks for adversarial attacks and common corruptions, and metrics for assessing model robustness, attack efficacy, and computational costs. Furthermore, it discusses trade-offs with other Trustworthy AI concepts like accuracy, fairness, and explainability.
| Approach | Key Features | Benefits | Limitations |
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Trade-offs: Robustness vs. Explainability
Woods et al. [252] demonstrate that the fidelity of explanations can be negatively impacted by adversarial attacks. They propose a regularization method to increase robustness and improve model explanations, termed Adversarial Explanations, highlighting the complex relationship between these two desirable AI properties.
Impact: Reveals the necessity for methods that not only improve robustness but also preserve or enhance the interpretability and trustworthiness of AI models, emphasizing a multi-objective optimization challenge.
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Your Robust AI Implementation Roadmap
A phased approach to integrating AI robustness into your enterprise, ensuring a secure and reliable transition.
Phase 1: Robustness Audit & Strategy
Conduct a comprehensive audit of existing AI systems for vulnerabilities. Define clear robustness objectives, identifying potential adversarial and natural attack vectors specific to your operational context. Develop a tailored strategy for integrating human-centered approaches and knowledge into AI development workflows. Establish initial benchmarks for desired robustness levels and explore multidisciplinary insights for optimal solution design.
Phase 2: Data & Model Hardening
Implement advanced data augmentation techniques, including the generation of diverse adversarial and natural perturbations to strengthen training datasets. Apply in-model robustness strategies, focusing on adaptive training procedures and architecting resilient neural networks (e.g., GNNs, BNNs). Prioritize explainability and fairness considerations, ensuring that robustness enhancements do not compromise other trustworthiness properties. Develop methods to incorporate human knowledge into feature alignment and data labeling processes.
Phase 3: Continuous Evaluation & Human-in-the-Loop Integration
Establish robust evaluation frameworks with certified robustness metrics and continuous benchmarking. Integrate human-in-the-loop mechanisms for ongoing monitoring, diagnosis of unknown unknowns, and validation of model behavior under novel perturbations. Develop tools and workflows to support ML practitioners in identifying and mitigating robustness-related issues, fostering collaboration with domain experts. Refine models based on real-world feedback and emerging adversarial tactics to ensure sustained, trustworthy AI performance.
Phase 4: Scaling & Governance
Scale robust AI solutions across the enterprise, ensuring seamless integration with existing IT infrastructure and business processes. Implement governance frameworks that define clear responsibilities for AI robustness, accountability, and continuous improvement. Establish regular human-centered reviews to assess the long-term impact of robust AI on organizational objectives and ethical guidelines. Leverage collected human insights to inform future AI development and policy-making, driving a culture of trustworthy AI innovation.
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