8 research outputs found
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Machine learning models are deployed as a central component in decision
making and policy operations with direct impact on individuals' lives. In order
to act ethically and comply with government regulations, these models need to
make fair decisions and protect the users' privacy. However, such requirements
can come with decrease in models' performance compared to their potentially
biased, privacy-leaking counterparts. Thus the trade-off between fairness,
privacy and performance of ML models emerges, and practitioners need a way of
quantifying this trade-off to enable deployment decisions. In this work we
interpret this trade-off as a multi-objective optimization problem, and propose
PFairDP, a pipeline that uses Bayesian optimization for discovery of
Pareto-optimal points between fairness, privacy and utility of ML models. We
show how PFairDP can be used to replicate known results that were achieved
through manual constraint setting process. We further demonstrate effectiveness
of PFairDP with experiments on multiple models and datasets.Comment: 3rd Workshop on Bias and Fairness in AI (BIAS), ECML 202
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
Machine Learning models are being deployed as parts of real-world systems
with the upsurge of interest in artificial intelligence. The design,
implementation, and maintenance of such systems are challenged by real-world
environments that produce larger amounts of heterogeneous data and users
requiring increasingly faster responses with efficient resource consumption.
These requirements push prevalent software architectures to the limit when
deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging
concept that equips systems better for integrating ML models. DOA extends
current architectures to create data-driven, loosely coupled, decentralised,
open systems. Even though papers on deployed ML-based systems do not mention
DOA, their authors made design decisions that implicitly follow DOA. The
reasons why, how, and the extent to which DOA is adopted in these systems are
unclear. Implicit design decisions limit the practitioners' knowledge of DOA to
design ML-based systems in the real world. This paper answers these questions
by surveying real-world deployments of ML-based systems. The survey shows the
design decisions of the systems and the requirements these satisfy. Based on
the survey findings, we also formulate practical advice to facilitate the
deployment of ML-based systems. Finally, we outline open challenges to
deploying DOA-based systems that integrate ML models.Comment: Under revie
Effectiveness and resource requirements of test, trace and isolate strategies for COVID in the UK.
We use an individual-level transmission and contact simulation model to explore the effectiveness and resource requirements of various test-trace-isolate (TTI) strategies for reducing the spread of SARS-CoV-2 in the UK, in the context of different scenarios with varying levels of stringency of non-pharmaceutical interventions. Based on modelling results, we show that self-isolation of symptomatic individuals and quarantine of their household contacts has a substantial impact on the number of new infections generated by each primary case. We further show that adding contact tracing of non-household contacts of confirmed cases to this broader package of interventions reduces the number of new infections otherwise generated by 5-15%. We also explore impact of key factors, such as tracing application adoption and testing delay, on overall effectiveness of TTI
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimization
and active learning benefiting from the scalability and efficiency of
TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based
models within sequential decision-making loops, e.g. Gaussian processes from
GPflow or GPflux, or neural networks from Keras. This modular mindset is
central to the package and extends to our acquisition functions and the
internal dynamics of the decision-making loop, both of which can be tailored
and extended by researchers or engineers when tackling custom use cases.
Trieste is a research-friendly and production-ready toolkit backed by a
comprehensive test suite, extensive documentation, and available at
https://github.com/secondmind-labs/trieste
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Towards Maintainable and Explainable AI Systems with Dataflow
Machine learning is enjoying rapid growth both as a thriving academic discipline and as a technology that has the potential to transform many aspects of our everyday lives. We have already witnessed breakthroughs in speech generation, drug discovery, recommendation algorithms, and more, all achieved with the help of machine learning. It is vital to realise that any practical application of machine learning is not limited to just creating an accurate model based on a sanitised dataset. Such real-life applications are complex software systems, in which the model is only one, albeit important, component. A significant effort is also spent on creating data collection and cleaning pipelines, quality assurance, model updating workflows, monitoring and operational maintenance of these systems. The experience of numerous practitioners shows that the translation of a well-performing machine learning model to a well-performing machine learning system is not easy. This thesis embarks on a quest to understand the pain points of this translation process and explore software architecture paradigms well suited for the needs of modern data-driven systems.
We begin by surveying existing reports on ML deployment and the difficulties they describe. The identified issues and concerns are matched against a typical ML deployment workflow, and we show that there is no single bottleneck, and the entire deployment pipeline is riddled with challenges. We argue that a lot of these challenges are caused by existing software infrastructure and that more data-oriented approaches to software architecture are needed to tackle them. This observation leads us to the second contribution of this thesis, in which we examine data-oriented architecture (DOA) as a promising software architecture paradigm that machine learning systems can benefit from. We focus on measuring the level of adoption of DOA in practical deployments of machine learning and show that even though the paradigm itself is relatively unknown, its principles widely permeate the modern engineering of ML systems. Specifically, we identify dataflow architecture as one of the patterns that realise all DOA principles.
We proceed to evaluate the benefits of the dataflow for the deployment of machine learning. The evaluation is presented in two parts. In the first part, we compare the process of deploying an ML model within the functionally equivalent codebases of applications implemented with dataflow and service-oriented approaches, the latter being used as a baseline. We identify some benefits of dataflow, such as higher discoverability and simpler data collection in the system. We also identify the limitations of the paradigm. We then present Seldon Core v2, an open-source model inference platform we designed following the dataflow architecture. We present a detailed discussion on how DOA principles can be implemented in practice, discuss the data observability features of the platform, and quantify the performance trade-offs involved.
The last contribution of the thesis points out another benefit of dataflow architecture for software development: a strong relationship between dataflow software and graphical causal models. We identify a connection between dataflow graphs and causal graphs and argue that this relationship allows a straightforward application of causal inference to dataflow software. We use fault localisation as a concrete example of this idea and showcase it in a variety of dataflow systems and scenarios.
The thesis closes with a discussion on research avenues that can further develop the community's understanding and adoption of Data-Oriented Architectures and dataflow for machine learning systems
Automatic Discovery of Privacy–Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can be provided in a particular application while maintaining acceptable utility. Analytical utility guarantees offer a rigorous tool to reason about this tradeoff, but are generally only available for relatively simple problems. For more complex tasks, such as training neural networks under differential privacy, the utility achieved by a given algorithm can only be measured empirically. This paper presents a Bayesian optimization methodology for efficiently characterizing the privacy– utility trade-off of any differentially private algorithm using only empirical measurements of its utility. The versatility of our method is illustrated on a number of machine learning tasks involving multiple models, optimizers, and datasets