Events & Conferences
Recent honors and awards for Amazon scientists
Ruomeng Cui won Management Science best paper award
Ruomeng Cui, an Amazon Visiting Academic with Amazon’s Supply Chain Optimization Technologies (SCOT) team, won the 2023 Management Science Best Paper Award in Operations Management.
Cui, who is on leave from her role as an associate professor in the department of Information System and Operations Management at the Goizueta Business School, Emory University, won the award along with her co-authors Jun Li and Dennis Zhang for their 2020 paper, “Reducing discrimination with reviews in the sharing economy: Evidence from field experiments on Airbnb.”
Their paper explored ways to reduce “widespread discrimination by hosts against guests of certain races in online marketplaces” by using peer-generated online reviews. Their work has influenced sharing platforms’ strategies to fight discrimination.
The award is given “to the manuscript judged to be most deserving for its contribution to the theory and practice of operations management among all operations papers published in the past 3 years at Management Science.”
Cui earned her PhD in operations management from the Kellogg School of Management, Northwestern University in 2014. In June 2022, she joined Amazon as a Visiting Academic. In that role, she is building and implementing cutting-edge causal inference, machine learning, optimization, and economic models to make supply chain decisions.
Christos Faloutsos won Donald G. Fink Overview Paper Award
Christos Faloutsos, an Amazon Scholar and the Fredkin Professor of Computer Science at Carnegie Mellon University, was part of a team that received the 2023 IEEE Signal Processing Society Donald G. Fink Overview Paper Award by the IEE Signal Processing Society for “Tensor Decomposition for Signal Processing and Machine Learning.”
In their 2016 overview paper, Faloutsos and his coauthors — Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, and Evangelos E. Papalexakis — noted that while tensors, which are a higher-dimensional analogue of a matrix, already had “a rich history, stretching over almost a century, and touching upon numerous disciplines” their usage had only then “become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining and machine learning.” Their overview aimed “to provide a good starting point for researchers and practitioners interested in learning about and working with tensors.”
The IEEE Signal Processing Society Overview Paper Award honors the authors “of a journal article of broad interest that has had substantial impact over several years on a subject related to the Society’s technical scope.”
Faloutsos said he believes the paper’s impact can be attributed to the fact that tensors are powerful tools. “They can handle static graphs, time evolving graphs, knowledge graphs which consist of triplets such as subject, verb, object, e.g., who plays in what team, who lives in, what city, who is friends with whom.”
Faloutsos, who joined Amazon as a Scholar in 2018, researches large-scale data mining with emphasis on graphs and time sequences, anomaly detection, tensors, and fractals.
Nicholas Kullman won 2023 Transportation Science Journal Paper of the Year
Nicholas Kullman, a senior research scientist with Amazon Line Haul, won the 2023 Transportation Science Journal Paper of the Year. Kullman and his coauthors — Martin Cousineau, Justin C. Goodson, and Jorge E. Mendoza — were awarded for their 2021 paper, “Dynamic Ride-Hailing with Electric Vehicles”.
In the paper, the authors “consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests.”
“As autonomous vehicles become more common, fleets of taxis may become more centrally coordinated,” Kullman explained. “We wanted to consider this case where there’s a central authority that controls whether or not requests are accepted or rejected.
“We wanted to look at good policies for figuring out which vehicles should serve which requests and what do you do with your vehicles when they’re not serving requests so that they are better positioned to be able to serve future requests — a sort of dynamic stochastic vehicle routing problem.”
The team utilized deep reinforcement learning to develop new policies. Those policies were compared “against some more classical operations research approaches” and “and against dual bounds on the value of an optimal policy.”
“I think one of the other reasons why the paper was well received was that we had dual bounds,” Kullman explained. “We built out a benchmark where we knew we could not have done better than that standard. Basically, if you’re the taxi authority and you know exactly where and when these requests are going to pop up, what would you do?”
The team found its “best policy trained with deep reinforcement learning outperforms the reoptimization approach.” Kullman, who joined Amazon in 2021, earned a PhD in operations research from Université de Tours. At Amazon, he researches optimization of middle-mile linehaul operations.
Niklas Karlsson named IEEE Fellow
Niklas Karlsson, a senior principal research scientist in Amazon Advertising Engineering, was recently named an IEEE Fellow for “technical leadership to vSLAM and online advertising.” The designation took effect on Jan. 1. Karlsson leads a team within Amazon DSP (ADSP) engineering, where he oversees research pertaining to ADSP bidding and optimization.
Karlsson earned a master’s in engineering physics from Lund University and then earned both a master’s in statistics and applied probability and a PhD in control, dynamic systems, and robotics, from UC Santa Barbara. After graduating he joined Evolution Robotics as senior navigation and control engineer. While there, he and his colleagues developed and patented vSLAM (visual simultaneous localization and mapping), an odometry- and vision-based SLAM system.
In 2005, Karlsson transitioned to a role as principal control engineer with Advertising.com. There he leveraged his expertise in feedback control and systems engineering to develop a next generation of scalable and adaptive bidding solutions for ad campaign optimization. By way of acquisitions and mergers, he ended up with Yahoo, where, after 17 years in online advertising, he departed as the chief scientist and vice president of research and development for Yahoo’s Demand Side Platform.
The IEEE Fellow designation is conferred by the IEEE board of directors upon individuals with outstanding records of accomplishment in any of the IEEE fields of interest. The total number selected in any one year cannot exceed 0.1% of the total voting membership. IEEE Fellow is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement.
Joan Feigenbaum named IEEE Fellow
Joan Feigenbaum, an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale University, will be elevated to IEEE Fellow grade in 2024. The grade of IEEE Fellow “recognizes exceptional distinction in the engineering profession.”
Feigenbaum, who works in the AWS Cryptographic Algorithms group on privacy-preserving computation, was awarded “for contributions to trust-management systems and Internet algorithmics.”
Hugo Krawczyk named IACR Distinguished speaker
Hugo Krawczyk, senior principal scientist, Amazon Web Services, was selected to present the 2023 IACR Distinguished Lecture.
The International Association for Cryptologic Research (IACR) Distinguished Lectures are awarded “to people who have made important contributions to cryptology research.”
Krawczyk, who is also an IACR Fellow, has made fundamental contributions to the cryptographic design of Internet standards like IPsec, IKE, and TLS. He also co-invented numerous cryptographic algorithms including the HMAC message authentication algorithm.
Prior to joining Amazon in July 2023, he was a principal researcher at the Algorand Foundation and part of its founding team. Prior to that, he was an IBM Fellow and Distinguished Research Staff Member at the IBM T.J. Watson Research Center as a member of the Cryptography Research group from 1992 to 1997, and again from 2004 to 2019. He was an associate professor at the Department of Electrical Engineering at the Technion in Israel from 1997 until 2004.
Aaditya Ramdas won Peter Gavin Hall IMS Early Career Prize
Aaditya Ramdas, an Amazon Visiting Academic who is also an assistant professor of statistics and machine learning at Carnegie Mellon University (CMU), won the Peter Gavin Hall Institute of Mathematical Statistics (IMS) Early Career Prize. Ramdas was recognized “for significant contributions in the areas of reproducibility in science and technology; active, sequential decision-making; and assumption-light uncertainty quantification.”
The prize “recognizes one researcher annually who is within the first eight years of completing their doctoral degree.” Ramdas has a bacehlor’s degree in computer science and engineering from IIT-Bombay and earned both a master’s and a PhD in statistics and machine learning from CMU.
Ramdas researches selective and simultaneous inference, game-theoretic statistics, and black-box predictive inference. His areas of applied interest include neuroscience, genetics and auditing.
Aaron Roth named CyLab’s 2023 Distinguished Alumni Award winner
Aaron Roth, an Amazon Scholar who is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, was named Distinguished Alumni Award winner by CyLab, Carnegie Mellon University’s security and privacy research institute. The award recognizes “Roth’s excellence in algorithms and machine learning, leadership in the field, and commitment to his students.”
Roth, who joined Amazon as a Scholar in 2020, researches the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning.
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.’
Download or listen to the episode below:
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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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And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.
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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