19,059 research outputs found
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain
Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks performed to probe and identify potential ML-trained modelsâ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area
Dehumanization, Disability, and Eugenics
This paper explores the relationship between eugenics, disability, and dehumanization, with a focus on forms of eugenics beyond Nazi eugenics
Machine Learning and Knowledge: Why Robustness Matters
Trusting machine learning algorithms requires having confidence in their
outputs. Confidence is typically interpreted in terms of model reliability,
where a model is reliable if it produces a high proportion of correct outputs.
However, model reliability does not address concerns about the robustness of
machine learning models, such as models relying on the wrong features or
variations in performance based on context. I argue that the epistemic
dimension of trust can instead be understood through the concept of knowledge,
where the trustworthiness of an algorithm depends on whether its users are in
the position to know that its outputs are correct. Knowledge requires beliefs
to be formed for the right reasons and to be robust to error, so machine
learning algorithms can only provide knowledge if they work well across
counterfactual scenarios and if they make decisions based on the right
features. This, I argue, can explain why we should care about model properties
like interpretability, causal shortcut independence, and distribution shift
robustness even if such properties are not required for model reliability.Comment: Comments are welcom
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline
From medical charts to national census, healthcare has traditionally operated
under a paper-based paradigm. However, the past decade has marked a long and
arduous transformation bringing healthcare into the digital age. Ranging from
electronic health records, to digitized imaging and laboratory reports, to
public health datasets, today, healthcare now generates an incredible amount of
digital information. Such a wealth of data presents an exciting opportunity for
integrated machine learning solutions to address problems across multiple
facets of healthcare practice and administration. Unfortunately, the ability to
derive accurate and informative insights requires more than the ability to
execute machine learning models. Rather, a deeper understanding of the data on
which the models are run is imperative for their success. While a significant
effort has been undertaken to develop models able to process the volume of data
obtained during the analysis of millions of digitalized patient records, it is
important to remember that volume represents only one aspect of the data. In
fact, drawing on data from an increasingly diverse set of sources, healthcare
data presents an incredibly complex set of attributes that must be accounted
for throughout the machine learning pipeline. This chapter focuses on
highlighting such challenges, and is broken down into three distinct
components, each representing a phase of the pipeline. We begin with attributes
of the data accounted for during preprocessing, then move to considerations
during model building, and end with challenges to the interpretation of model
output. For each component, we present a discussion around data as it relates
to the healthcare domain and offer insight into the challenges each may impose
on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20
Pages, 1 Figur
When and How to Fool Explainable Models (and Humans) with Adversarial Examples
Reliable deployment of machine learning models such as neural networks
continues to be challenging due to several limitations. Some of the main
shortcomings are the lack of interpretability and the lack of robustness
against adversarial examples or out-of-distribution inputs. In this paper, we
explore the possibilities and limits of adversarial attacks for explainable
machine learning models. First, we extend the notion of adversarial examples to
fit in explainable machine learning scenarios, in which the inputs, the output
classifications and the explanations of the model's decisions are assessed by
humans. Next, we propose a comprehensive framework to study whether (and how)
adversarial examples can be generated for explainable models under human
assessment, introducing novel attack paradigms. In particular, our framework
considers a wide range of relevant (yet often ignored) factors such as the type
of problem, the user expertise or the objective of the explanations in order to
identify the attack strategies that should be adopted in each scenario to
successfully deceive the model (and the human). These contributions intend to
serve as a basis for a more rigorous and realistic study of adversarial
examples in the field of explainable machine learning.Comment: 12 pages, 1 figur
Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room
Background: Diagnostic errors occur frequently, especially in the emergency room. Estimates about the
consequences of diagnostic error vary widely and little is known about the factors predicting error. Our
objectives thus was to determine the rate of discrepancy between diagnoses at hospital admission and
discharge in patients presenting through the emergency room, the discrepanciesâ consequences, and factors
predicting them.
Methods: Prospective observational clinical study combined with a survey in a University-affiliated tertiary
care hospital. Patientsâ hospital discharge diagnosis was compared with the diagnosis at hospital admittance
through the emergency room and classified as similar or discrepant according to a predefined scheme by
two independent expert raters. Generalized linear mixed-effects models were used to estimate the effect of
diagnostic discrepancy on mortality and length of hospital stay and to determine whether characteristics of
patients, diagnosing physicians, and context predicted diagnostic discrepancy.
Results: 755 consecutive patients (322 [42.7%] female; mean age 65.14 years) were included.
The discharge diagnosis differed substantially from the admittance diagnosis in 12.3% of cases. Diagnostic
discrepancy was associated with a longer hospital stay (mean 10.29 vs. 6.90 days; Cohenâs d 0.47; 95%
confidence interval 0.26 to 0.70; P = 0.002) and increased patient mortality (8 (8.60%) vs. 25(3.78%); OR 2.40; 95% CI 1.05
to 5.5 P = 0.038). A factor available at admittance that predicted diagnostic discrepancy was the diagnosing physicianâs
assessment that the patient presented atypically for the diagnosis assigned (OR 3.04; 95% CI 1.33â6.96; P = 0.009).
Conclusions: Diagnostic discrepancies are a relevant healthcare problem in patients admitted through the
emergency room because they occur in every ninth patient and are associated with increased in-hospital
mortality. Discrepancies are not readily predictable by fixed patient or physician characteristics; attention
should focus on context
Domain-independent exception handling services that increase robustness in open multi-agent systems
Title from cover. "May 2000."Includes bibliographical references (p. 17-23).Mark Klein and Chrysanthos Dellarocas
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