scholarly journals Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

2021 ◽  
Author(s):  
Michele Samorani ◽  
Shannon L. Harris ◽  
Linda Goler Blount ◽  
Haibing Lu ◽  
Michael A. Santoro
Keyword(s):  
Machine Learning ◽  
Racial Disparities ◽  
Real World ◽  
Racial Disparity ◽  
Racial Bias ◽  
State Of The Art ◽  
Waiting Times ◽  
Appointment Scheduling ◽  
Racial Group ◽  
Black Patients

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective.

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

2019 ◽  
Author(s):  
Michele Samorani ◽  
Shannon Harris ◽  
Linda Goler Blount ◽  
Haibing Lu ◽  
Michael A. Santoro
Keyword(s):  
Machine Learning ◽  
Racial Bias ◽  
Appointment Scheduling ◽  
Medical Appointment

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Lifelong Spectral Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5867-5874
Author(s):  
Gan Sun ◽  
Yang Cong ◽  
Qianqian Wang ◽  
Jun Li ◽  
Yun Fu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Spectral Clustering ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Orthogonal Basis ◽  
Learning Framework ◽  
The Past ◽  
Benchmark Datasets ◽  
Over Time

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.

scholarly journals A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study (Preprint)

2020 ◽  
Author(s):  
Angier Allen ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Hoyt Burdick ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Racial Disparities ◽  
Equal Opportunity ◽  
Racial Bias ◽  
Learning Algorithm ◽  
Early Warning Score ◽  
Machine Learning Algorithm ◽  
Saps Ii

BACKGROUND Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, <i>P</i>=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, <i>P</i>=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, <i>P</i>=.006 and equal opportunity difference 0.074, <i>P</i><.001, respectively). CONCLUSIONS This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

scholarly journals A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

10.2196/22400 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e22400
Author(s):  
Angier Allen ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Hoyt Burdick ◽  
Gregory Braden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Racial Disparities ◽  
Equal Opportunity ◽  
Racial Bias ◽  
Learning Algorithm ◽  
Early Warning Score ◽  
Machine Learning Algorithm ◽  
Saps Ii

Background Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. Objective The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. Methods Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). Results The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). Conclusions This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

scholarly journals A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection

2020 ◽  
Vol 34 (03) ◽  
pp. 2451-2458
Author(s):  
Akansha Bhardwaj ◽  
Jie Yang ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Event Detection ◽  
State Of The Art ◽  
Regularization Parameter ◽  
Learning Models ◽  
Training Process ◽  
Model Training ◽  
Real World Datasets ◽  
Machine Learning Models

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation of the distribution – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword-specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.

Smart Animation Tools

2018 ◽  
pp. 52-66
Author(s):  
Benjamin Kenwright
Keyword(s):  
Machine Learning ◽  
Parallel Processing ◽  
Real World ◽  
State Of The Art ◽  
Computational Cost ◽  
Massively Parallel ◽  
Learning Models ◽  
Processing Power ◽  
Massively Parallel Processing ◽  
Self Learning

This chapter discusses the inherent limitations in conventional animation techniques and possible solutions through optimisation and machine learning paradigms. For example, going beyond pre–recorded animation libraries towards more intelligent self-learning models. These models present a range of difficulties in real-world solutions, such as, computational cost, flexibility, and most importantly, artistic control. However, as we discuss in this chapter, advancements in massively parallel processing power and hybrid models provides a transitional medium for these solutions (best of both worlds). We review trends and state of the art techniques and their viability in industry. A particular area of active animation is self–driven characters (i.e., agents mimic the real-world through physics-based models). We discuss and debate each techniques practicality in solving and overcoming current and future limitations.

Benchmarking machine learning algorithms for Non-Intrusive Water Monitoring

2020 ◽  
Author(s):  
Andrea Cominola ◽  
Marie-Philine Becker ◽  
Riccardo Taormina
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Water Use ◽  
Real World ◽  
State Of The Art ◽  
Demand Management ◽  
Machine Learning Algorithms ◽  
Urban Water ◽  
Real World Data ◽  
End Use

&lt;p&gt;As several cities all over the world face the exacerbating challenges posed by climate change, population growth, and urbanization, it becomes clear how increased water security and more resilient urban water systems can be achieved by optimizing the use of water resources and minimize losses and inefficient usage. In the literature, there is growing evidence about the potential of demand management programs to complement supply-side interventions and foster more efficient water use behaviors. A new boost to demand management is offered by the ongoing digitalization of the water utility sector, which facilitates accurate measuring and estimation of urban water demands down to the scale of individual end-uses of residential water consumers (e.g., showering, watering). This high-resolution data can play a pivotal role in supporting demand-side management programs, fostering more efficient and sustainable water uses, and prompting the detection of anomalous behaviors (e.g., leakages, faulty meters). The problem of deriving individual end-use consumption traces from the composite signal recorded by single-point meters installed at the inlet of each household has been studied for nearly 30 years in the electricity field (Non-Intrusive Load Monitoring). Conversely, the similar disaggregation problem in the water sector - here called Non-Intrusive Water Monitoring (NIWM) - is still a very open research challenge. Most of the state-of-the-art end-use disaggregation algorithms still need an intrusive calibration or time- consuming expert-based manual processing. Moreover, the limited availability of large-scale open datasets with end- use ground truth data has so far greatly limited the development and benchmarking of NIWM methods.&lt;/p&gt;&lt;p&gt;In this work, we comparatively test the suitability of different machine learning algorithms to perform NIWM. First, we formulate the NIWM problem both as a regression problem, where water consumption traces are processed as continuous time-series, and a classification problem, where individual water use events are associated to one or more end use labels. Second, a number of algorithms based on the last trends in Artificial Intelligence and Machine Learning are tested both on synthetic and real-world data, including state-of-the-art tree-based and Deep Learning methods. Synthetic water end-use time series generated with the STREaM stochastic simulation model are considered for algorithm testing, along with labelled real-world data from the Residential End Uses of Water, Version 2, database by the Water Research Foundation. Finally, the performance of the different NIWM algorithms is comparatively assessed with metrics that include (i) NIWM accuracy, (ii) computational cost, and (iii) amount of needed training data.&lt;/p&gt;

Learning and Generalising Object Extraction Skill for Contact-rich Disassembly Tasks: An Introductory Study

2021 ◽  
Author(s):  
Antonio Serrano Muñoz ◽  
Nestor Arana-Arexolaleiba ◽  
Dimitrios Chrysostomou ◽  
Simon Bøgh
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Real World ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Robotic Manipulation ◽  
Object Extraction ◽  
Learning Agents ◽  
Key Concepts ◽  
Planning And Operation

Abstract Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the process's planning and operation. Machine learning, particularly reinforcement learning, methods are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the State-of-the-Art of contact-rich disassembly using reinforcement learning algorithms and a study about the object extraction skill's generalisation when applied to contact-rich disassembly tasks. The generalisation capabilities of two State-of-the-Art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation and real-world while perform a disassembly task. Results shows that, at least, one of the agents can generalise the contact-rich extraction skill. Also, this work identifies key concepts and gaps for the reinforcement learning algorithms' research and application on disassembly tasks.

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