Events & Conferences
Responsible AI in the wild: Lessons learned at AWS
When we first joined AWS AI/ML as Amazon Scholars over three years ago, we had already been doing scientific research in the area now known as responsible AI for a while. We had authored a number of papers proposing mathematical definitions of fairness and machine learning (ML) training algorithms enforcing them, as well as methods for ensuring strong notions of privacy in trained models. We were well versed in adjacent subjects like explainability and robustness and were generally denizens of the emerging responsible-AI research community. We even wrote a general-audience book on these topics to try to explain their importance to a broader audience.
So we were excited to come to AWS in 2020 to apply our expertise and methodologies to the ongoing responsible-AI efforts here — or at least, that was our mindset on arrival. But our journey has taken us somewhere quite different, somewhere more consequential and interesting than we expected. It’s not that the definitions and algorithms we knew from the research world aren’t relevant — they are — but rather that they are only one component of a complex AI workstream comprising data, models, services, enterprise customers, and end-users. It’s also a workstream in which AWS is uniquely situated due to its pioneering role in cloud computing generally and cloud AI services specifically.
Our time here has revealed to us some practical challenges of which we were previously unaware. These include diverse data modalities, “last mile” effects with customers and end-users, and the recent emergence of AI activism. Like many good interactions between industry and academia, what we’ve learned at AWS has altered our research agenda in healthy ways. In case it’s useful to anyone else trying to parse the burgeoning responsible-AI landscape (especially in the generative-AI era), we thought we’d detail some of our experiences here.
Modality matters
One of our first important practical lessons might be paraphrased as “modality matters”. By this we mean that the particular medium in which an AI service operates (such as visual images or spoken or written language) matters greatly in how we analyze and understand it from both performance and responsible-AI perspectives.
Consider specifically the desire for trained models be “fair”, or free of significant demographic bias. Much of the scientific literature on ML fairness assumes that the features used to compare performance across groups (which might include gender, race, age, and other attributes) are readily available, or can be accurately estimated, in both training and test datasets.
If this is indeed the case (as it might be for some spreadsheet-like “tabular” datasets recording things like medical or financial records, in which a person’s age and gender might be explicit columns), we can more easily test a trained model for bias. For instance, in a medical diagnosis application we might evaluate the model to make sure the error rates are approximately the same across genders. If these rates aren’t close enough, we can augment our data or retrain the model in various ways until the evaluation is passed to satisfaction.
But many cloud AI/ML services operate on data that simply does not contain explicit demographic information. Rather, these services live in entirely different modalities such as speech, natural language, and vision. Applications such as our speech recognition and transcription services take as input time series of frequencies that capture spoken utterances. Consequently, there are not direct annotations in the data of things like gender, race, or age.
But what can be more readily detected from speech data, and are also more directly related to performance, are regional dialects and accents — of which there are dozens in North American English alone. English-language speech can also feature non-native accents, influenced more by the first languages of the speakers than by the regions in which they currently live. This presents an even more diverse landscape, given the large number of first languages and the international mobility of speakers. And while spoken accents may be weakly correlated or associated with one or more ancestry groups, they are usually uninformative on things like age and gender (speakers with a Philadelphia accent may be young or old; male, female or nonbinary; etc.). Finally, the speech of even a particular person may exhibit many other sources of variation, such as situational stress and fatigue.
What is the responsible-AI practitioner to do when confronted with so many different accents and other moving parts, in a task as complex as speech transcription? At AWS, our answer is to meet the task and data on their own terms, which in this case involves some heavy lifting: meticulously gathering samples from large populations of representative speakers with different accents and carefully transcribing each word. The “representative” is important here: while it might be more expedient to (for instance) gather this data from professional actors trained in diction, such data would not be typical of spoken language in the wild.
We also gather speech data that exhibits variability along other important dimensions, including the acoustic conditions during recording (varying amounts and types of background noise, recordings made via different mobile-phone handsets, whose microphones may vary in quality, etc.). The sheer number of combinations makes obtaining sufficient coverage challenging. (In some domains such as computer vision, coverage issues that are similar — variability across visual properties such as skin tone, lighting conditions, indoor vs. outdoor settings, and so on — have led to increased interest in synthetic data to augment human-generated data, including for fairness testing here at AWS.)
Once curated, such datasets can be used for training a transcription model that is not only good overall but also roughly equally performant across accents. And “performant” here means something more complex than in a simple prediction task; speech recognition typically uses a measure like the word error rate. On top of all the curation and annotations above, we also annotate some data by self-reported speaker demographics to make sure we’re fair not just by accent but by race and gender as well, as detailed in the service’s accompanying service card.
Our overarching point here is twofold. First, while as a society we tend to focus on dimensions such as race and gender when speaking about and assessing fairness, sometimes the data simply doesn’t permit such assessments, and it may not be a good idea to impute such dimensions to the data (for instance, by trying to infer race from speech signals). And second, in such cases the data may lead us toward alternative notions of fairness that might be more task-relevant, as with word error rates across dialects and accents.
The last mile of responsible AI
The specific properties of individuals that can or cannot (or should not) be gleaned from a particular dataset or modality are not the only things that may be out of the direct control of AI developers — especially in the era of cloud computing. As we have seen above, it’s challenging work to get coverage of everything you can anticipate. It’s even harder to anticipate everything.
The supply chain phrase “the last mile” refers to the fact that “upstream” providers of goods and products may have limited control over the “downstream” suppliers that directly connect to end-users or consumers. The emergence of cloud providers like AWS has created an AI service supply chain with its own last-mile challenges.
AWS AI/ML provides enterprise customers with API access to services like speech transcription because many want to integrate such services into their own workflows but don’t have the resources, expertise, or interest to build them from scratch. These enterprise customers sit between the general-purpose services of a cloud provider like AWS and the final end-users of the technology. For example, a health care system might want to provide cloud speech transcription services optimized for medical vocabulary to allow doctors to take verbal notes during their patient rounds.
As diligent as we are at AWS at battle-testing our services and underlying models for state-of-the-art performance, fairness, and other responsible-AI dimensions, it is obviously impossible to anticipate all possible downstream use cases and conditions. Continuing our health care example, perhaps there is a floor of a particular hospital that has new and specialized imaging equipment that emits background noise at a specific regularity and acoustic frequency. In the likely event that these exact conditions were not represented in either the training or test data, it’s possible that overall word error rates will not only be higher but may be so differentially across accents and dialects.
Such last-mile effects can be as diverse as the enterprise customers themselves. With time and awareness of such conditions, we can use targeted training data and customer-side testing to improve downstream performance. But due to the proliferation of new use cases, it is an ever-evolving process, not one that is ever “finished”.
AI activism: from bugs to bias
It’s not only cloud customers whose last miles may present conditions that differ from those during training and testing. We live in a (healthy) era of what might be called AI activism, in which not only enterprises but individual citizens — including scientists, journalists, and members of nonprofit organizations — can obtain API or open-source access to ML services and models and perform their own evaluations on their own curated datasets. Such tests are often done to highlight weaknesses of the technology, including shortfalls in overall performance and fairness but also potential security and privacy vulnerabilities. As such, they are typically performed without the AI developer’s knowledge and may be first publicized in both research and mainstream media outlets. Indeed, we have been on the receiving end of such critical publicity in the past.
To date, the dynamic between AI developers and activists has been somewhat adversarial: activists design and conduct a private experimental evaluation of a deployed AI model and report their findings in open forums, and developers are left to evaluate the claims and make any needed improvements to their technology. It is a dynamic that is somewhat reminiscent of the historical tensions between more traditional software and security developers and the ethical and unethical hacker communities, in which external parties probe software, operating systems, and other platforms for vulnerabilities and either expose them for the public good or exploit them privately for profit.
Over time the software community has developed mechanisms to alter these dynamics to be more productive than adversarial, in particular in the form of bug bounty programs. These are formal events or competitions in which software developers invite the hacker community to deliberately find vulnerabilities in their technology and offer financial or other rewards for reporting and describing them to the developers.
In the last couple of years, the ideas and motivations behind bug bounties have been adopted and adapted by the AI development community, in the form of “bias bounties”. Rather than finding bugs in traditional software, participants are invited to help identify demographic or other biases in trained ML models and systems. Early versions of this idea were informal hackathons of short duration focused on finding subsets of a dataset on which a model underperformed. But more recent proposals incubated at AWS and elsewhere include variants that are more formal and algorithmic in nature. The explosion of models, interest in, and concerns about generative AI have also led to more codified and institutionalized responsible-AI methodologies such as the HELM framework for evaluating large language models.
We view these recent developments — AI developers opening up their technology and its evaluation to a wider community of stakeholders than just enterprise customers, and those stakeholders playing an active role in identifying necessary improvements in both technical and nontechnical ways — as healthy and organic, a natural outcome of the complex and evolving AI industry. Indeed, such collaborations are in keeping with our recent White House commitments to external testing and model red-teaming.
Responsible AI is neither a problem to be “solved” once and for all, nor a problem that can be isolated to a single location in the pipeline stretching from developers to their customers to end-users and society at large. Developers are certainly the first line where best practices must be established and implemented and responsible-AI principles defended. But the keys to the long-term success of the AI industry lie in community, communication, and cooperation among all those affected by it.
Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?
Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.
They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’
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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
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Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.
This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.
Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.
“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”
“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”
ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.
The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.
AI for Information Security
| Recipient | University | Research title |
| Christopher Amato | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Bernd Bischl | Ludwig Maximilian University of Munich | Improving Generative and Foundation Models Reliability via Uncertainty-awareness |
| Shiqing Ma | University Of Massachusetts Amherst | LLM and Domain Adaptation for Attack Detection |
| Alina Oprea | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
| Roberto Perdisci | University of Georgia | ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection |
Automated Reasoning
| Recipient | University | Research title |
| Nada Amin | Harvard University | LLM-Augmented Semi-Automated Proofs for Interactive Verification |
| Suguman Bansal | Georgia Institute of Technology | Certified Inductive Generalization in Reinforcement Learning |
| Ioana Boureanu | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Omar Haider Chowdhury | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Stefan Ciobaca | Alexandru Ioan Cuza University | An Interactive Proof Mode for Dafny |
| João Ferreira | INESC-ID | Polyglot Automated Program Repair for Infrastructure as Code |
| Sicun Gao | University Of California, San Diego | Monte Carlo Trees with Conflict Models for Proof Search |
| Mirco Giacobbe | University of Birmingham | Neural Software Verification |
| Tobias Grosser | University of Cambridge | Synthesis-based Symbolic BitVector Simplification for Lean |
| Ronghui Gu | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Alexey Ignatiev | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Kenneth McMillan | University of Texas At Austin | Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification |
| Alexandra Mendes | University of Porto | Overcoming Barriers to the Adoption of Verification-Aware Languages |
| Jason Nieh | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
| Rohan Padhye | Carnegie Mellon University | Automated Synthesis and Evaluation of Property-Based Tests |
| Nadia Polikarpova | University Of California, San Diego | Discovering and Proving Critical System Properties with LLMs |
| Fortunat Rajaona | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
| Subhajit Roy | Indian Institute of Technology Kanpur | Theorem Proving Modulo LLM |
| Gagandeep Singh | University of Illinois At Urbana–Champaign | Trustworthy LLM Systems using Formal Contracts |
| Scott Stoller | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
| Peter Stuckey | Monash University | Huub: Next-Gen Lazy Clause Generation |
| Yulei Sui | University of New South Wales | Path-Sensitive Typestate Analysis through Sparse Abstract Execution |
| Nikos Vasilakis | Brown University | Semantics-Driven Static Analysis for the Unix/Linux Shell |
| Ping Wang | Stevens Institute of Technology | Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database |
| John Wawrzynek | University of California, Berkeley | GPU-Accelerated High-Throughput SAT Sampling |
AWS AI
| Recipient | University | Research title |
| Panagiotis Adamopoulos | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Vikram Adve | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Frances Arnold | California Institute of Technology | Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization |
| Yonatan Bisk | Carnegie Mellon University | Useful, Safe, and Robust Multiturn Interactions with LLMs |
| Shiyu Chang | University of California, Santa Barbara | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Yuxin Chen | University of Pennsylvania | Provable Acceleration of Diffusion Models for Modern Generative AI |
| Tianlong Chen | University of North Carolina at Chapel Hill | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
| Mingyu Ding | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Nikhil Garg | Cornell University | Market Design for Responsible Multi-agent LLMs |
| Jessica Hullman | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Christopher Jermaine | Rice University | Fast, Trusted AI Using the EINSUMMABLE Compiler |
| Yunzhu Li | Columbia University | Physics-Informed Foundation Models Through Embodied Interactions |
| Pattie Maes | Massachusetts Institute of Technology | Understanding How LLM Agents Deviate from Human Choices |
| Sasa Misailovic | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
| Kristina Monakhova | Cornell University | Trustworthy extreme imaging for science using interpretable uncertainty quantification |
| Todd Mowry | Carnegie Mellon University | Efficient LLM Serving on Trainium via Kernel Generation |
| Min-hwan Oh | Seoul National University | Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits |
| Patrick Rebeschini | University of Oxford | Optimal Regularization for LLM Alignment |
| Jose Renau | University of California, Santa Cruz | Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming |
| Vilma Todri | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
| Aravindan Vijayaraghavan | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
| Wei Yang | University of Texas at Dallas | Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing |
| Huaxiu Yao | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
| Amy Zhang | University of Washington | Tools for Governing AI Agent Autonomy |
| Ruqi Zhang | Purdue University | Efficient Test-time Alignment for Large Language Models and Large Multimodal Models |
| Zheng Zhang | Rutgers University-New Brunswick | AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser |
AWS Cryptography
| Recipient | University | Research title |
| Alexandra Boldyreva | Georgia Institute of Technology | Quantifying Information Leakage in Searchable Encryption Protocols |
| Maria Eichlseder | Graz University of Technology, Austria | SALAD – Systematic Analysis of Lightweight Ascon-based Designs |
| Venkatesan Guruswami | University of California, Berkeley | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| Joseph Jaeger | Georgia Institute of Technology | Analyzing Chat Encryption for Group Messaging |
| Aayush Jain | Carnegie Mellon | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Huijia Lin | University of Washington | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
| Hamed Nemati | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Karl Palmskog | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
| Chris Peikert | University of Michigan, Ann Arbor | Practical Third-Generation FHE and Bootstrapping |
| Dimitrios Skarlatos | Carnegie Mellon University | Scale-Out FHE LLMs on GPUs |
| Vinod Vaikuntanathan | Massachusetts Institute of Technology | Can Quantum Computers (Really) Factor? |
| Daniel Wichs | Northeastern University | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
| David Wu | University Of Texas At Austin | Fast Private Information Retrieval and More using Homomorphic Encryption |
Sustainability
| Recipient | University | Research title |
| Meeyoung Cha | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
| Jingrui He | University of Illinois at Urbana–Champaign | Foundation Model Enabled Earth’s Ecosystem Monitoring |
| Pedro Lopes | University of Chicago | AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware |
| Cheng Yaw Low | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.
During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.
Evaluation of Nova Premier against PRISM AI
PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.
Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.
The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.
Manual red teaming with ActiveFence
The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.
| Model | 3P Flag Rate [↓ is better] |
| Nova Premier | 12.0% |
| Sonnet 3.7 (non-reasoning) | 20.6% |
| GPT4.1 API | 22.4% |
“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”
These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.
Acknowledgments: Vincent Ponzo, Elyssa Vincent
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