5,909 research outputs found
Individual Fairness in Pipelines
It is well understood that a system built from individually fair components
may not itself be individually fair. In this work, we investigate individual
fairness under pipeline composition. Pipelines differ from ordinary sequential
or repeated composition in that individuals may drop out at any stage, and
classification in subsequent stages may depend on the remaining "cohort" of
individuals. As an example, a company might hire a team for a new project and
at a later point promote the highest performer on the team. Unlike other
repeated classification settings, where the degree of unfairness degrades
gracefully over multiple fair steps, the degree of unfairness in pipelines can
be arbitrary, even in a pipeline with just two stages.
Guided by a panoply of real-world examples, we provide a rigorous framework
for evaluating different types of fairness guarantees for pipelines. We show
that na\"{i}ve auditing is unable to uncover systematic unfairness and that, in
order to ensure fairness, some form of dependence must exist between the design
of algorithms at different stages in the pipeline. Finally, we provide
constructions that permit flexibility at later stages, meaning that there is no
need to lock in the entire pipeline at the time that the early stage is
constructed
Responsible Design Patterns for Machine Learning Pipelines
Integrating ethical practices into the AI development process for artificial
intelligence (AI) is essential to ensure safe, fair, and responsible operation.
AI ethics involves applying ethical principles to the entire life cycle of AI
systems. This is essential to mitigate potential risks and harms associated
with AI, such as algorithm biases. To achieve this goal, responsible design
patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee
ethical and fair outcomes. In this paper, we propose a comprehensive framework
incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical
development of AI systems. Our framework comprises new responsible AI design
patterns for ML pipelines identified through a survey of AI ethics and data
management experts and validated through real-world scenarios with expert
feedback. The framework guides AI developers, data scientists, and
policy-makers to implement ethical practices in AI development and deploy
responsible AI systems in production.Comment: 20 pages, 4 figures, 5 table
The SPATIAL Architecture:Design and Development Experiences from Gauging and Monitoring the AI Inference Capabilities of Modern Applications
Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of-concept architecture that analyzes AI models in a human-in-the-loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in realworld industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight
Carbon Responder: Coordinating Demand Response for the Datacenter Fleet
The increasing integration of renewable energy sources results in
fluctuations in carbon intensity throughout the day. To mitigate their carbon
footprint, datacenters can implement demand response (DR) by adjusting their
load based on grid signals. However, this presents challenges for private
datacenters with diverse workloads and services. One of the key challenges is
efficiently and fairly allocating power curtailment across different workloads.
In response to these challenges, we propose the Carbon Responder framework.
The Carbon Responder framework aims to reduce the carbon footprint of
heterogeneous workloads in datacenters by modulating their power usage. Unlike
previous studies, Carbon Responder considers both online and batch workloads
with different service level objectives and develops accurate performance
models to achieve performance-aware power allocation. The framework supports
three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and
Decentralized DR. We evaluate Carbon Responder polices using production
workload traces from a private hyperscale datacenter. Our experimental results
demonstrate that the efficient Carbon Responder policy reduces the carbon
footprint by around 2x as much compared to baseline approaches adapted from
existing methods. The fair Carbon Responder policies distribute the performance
penalties and carbon reduction responsibility fairly among workloads
Connecting Fairness in Machine Learning with Public Health Equity
Machine learning (ML) has become a critical tool in public health, offering
the potential to improve population health, diagnosis, treatment selection, and
health system efficiency. However, biases in data and model design can result
in disparities for certain protected groups and amplify existing inequalities
in healthcare. To address this challenge, this study summarizes seminal
literature on ML fairness and presents a framework for identifying and
mitigating biases in the data and model. The framework provides guidance on
incorporating fairness into different stages of the typical ML pipeline, such
as data processing, model design, deployment, and evaluation. To illustrate the
impact of biases in data on ML models, we present examples that demonstrate how
systematic biases can be amplified through model predictions. These case
studies suggest how the framework can be used to prevent these biases and
highlight the need for fair and equitable ML models in public health. This work
aims to inform and guide the use of ML in public health towards a more ethical
and equitable outcome for all populations
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