Machine Intelligence
Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence. In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.
Machine Intelligence at Google raises deep scientific and engineering challenges, allowing us to contribute to the broader academic research community through technical talks and publications in major conferences and journals. Contrary to much of current theory and practice, the statistics of the data we observe shifts rapidly, the features of interest change as well, and the volume of data often requires enormous computation capacity. When learning systems are placed at the core of interactive services in a fast changing and sometimes adversarial environment, combinations of techniques including deep learning and statistical models need to be combined with ideas from control and game theory.
Recent Publications
Securing the AI Software Supply Chain
Isaac Hepworth
Kara Olive
Kingshuk Dasgupta
Michael Le
Mark Lodato
Mihai Maruseac
Sarah Meiklejohn
Shamik Chaudhuri
Tehila Minkus
Google, Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043(2024)
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As AI-powered features gain traction in software applications, we see many of the same problems we’ve faced with traditional software—but at an accelerated pace. The threat landscape continues to expand as AI is further integrated into everyday products, so we can expect more attacks. Given the expense of building models, there is a clear need for supply chain solutions.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
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Principal-Agent Reward Shaping in MDPs
Omer Ben-Porat
Yishay Mansour
Michal Moshkovitz
Boaz Taitler
AAAI 2024
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Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios such as Markov Decision Processes (MDPs). In this paper, we further explore this line of research by investigating how reward shaping under budget constraints can improve the principal's utility. We study a two-player Stackelberg game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players. The principal offers an additional reward to the agent, and the agent picks their policy selfishly to maximize their reward, which is the sum of the original and the offered reward. Our results establish the NP-hardness of the problem and offer polynomial approximation algorithms for two classes of instances: Stochastic trees and deterministic decision processes with a finite horizon.
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A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Heather Cole-Lewis
Rory Sayres
Nenad Tomašev
Liam McCoy
Leo Anthony Celi
Alanna Walton
Preeti Singh
Akeiylah DeWitt
Philip Mansfield
Sushant Prakash
Christopher Semturs
Joelle Barral
Ivor Horn
Karan Singhal
Arxiv(2024) (to appear)
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Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. In this work, we present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and then conduct an empirical case study with Med-PaLM 2, resulting in the largest human evaluation study in this area to date. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven newly-released datasets comprising both manually-curated and LLM-generated questions enriched for adversarial queries. Both our human assessment framework and dataset design process are grounded in an iterative participatory approach and review of possible biases in Med-PaLM 2 answers to adversarial queries. Through our empirical study, we find that the use of a collection of datasets curated through a variety of methodologies, coupled with a thorough evaluation protocol that leverages multiple assessment rubric designs and diverse rater groups, surfaces biases that may be missed via narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. We emphasize that while our framework can identify specific forms of bias, it is not sufficient to holistically assess whether the deployment of an AI system promotes equitable health outcomes. We hope the broader community leverages and builds on these tools and methods towards realizing a shared goal of LLMs that promote accessible and equitable healthcare for all.
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In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds. We propose a threat modeling approach to identify potential risks and countermeasures for product inclusion in security and privacy. We discuss various factors that can affect a user's ability to achieve a high level of security and privacy, including low-income demographics, poor connectivity, shared device usage, ML fairness, etc. We present results from a global security and privacy user experience survey and discuss the implications for product developers. Our work highlights the need for a more inclusive approach to security and privacy and provides a framework for researchers and practitioners to consider when designing products and services for a diverse range of users.
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ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
Somesh Jha
Transactions on Machine Learning Research (TMLR)(2024)
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Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. These predictions can then be deferred to humans for further evaluation. As an everlasting challenge for machine learning, in many real-world scenarios, the distribution of test data is different from the training data. This results in more inaccurate predictions, and often increased dependence on humans, which can be difficult and expensive. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. Selective prediction and active learning have been approached from different angles, with the connection between them missing. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage. For this new paradigm, we propose a simple yet effective approach, ASPEST, that utilizes ensembles of model snapshots with self-training with their aggregated outputs as pseudo labels. Extensive experiments on numerous image, text and structured datasets, which suffer from domain shifts, demonstrate that ASPEST can significantly outperform prior work on selective prediction and active learning (e.g. on the MNIST→SVHN benchmark with the labeling budget of 100, ASPEST improves the AUACC metric from 79.36% to 88.84%) and achieves more optimal utilization of humans in the loop.
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Federated Variational Inference: Towards Improved Personalization and Generalization
Elahe Vedadi
Josh Dillon
Philip Mansfield
Karan Singhal
Arash Afkanpour
Warren Morningstar
AAAI Federated Learning on the Edge Symposium(2024)
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Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or generalize to new clients. We study personalization and generalization in stateless cross-device federated learning setups assuming heterogeneity in client data distributions and predictive models. We first propose a hierarchical generative model and formalize it using Bayesian Inference. We then approximate this process using Variational Inference to train our model efficiently. We call this algorithm Federated Variational Inference (FedVI). We use PAC-Bayes analysis to provide generalization bounds for FedVI. We evaluate our model on FEMNIST and CIFAR-100 image classification and show that FedVI beats the state-of-the-art on both tasks.
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