scholarly journals On the ethics of algorithmic decision-making in healthcare

2019 ◽  
Vol 46 (3) ◽  
pp. 205-211 ◽  
Author(s):  
Thomas Grote ◽  
Philipp Berens
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Moral Responsibility ◽  
Medical Diagnosis ◽  
Social Epistemology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
High Profile ◽  
Trade Offs

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals Algorithmic Opacity: Making Algorithmic Processes Transparent through Abstraction Hierarchy

2018 ◽  
Vol 62 (1) ◽  
pp. 192-196
Author(s):  
Pragya Paudyal ◽  
B.L. William Wong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intelligence Analysis ◽  
Functional Relationships ◽  
Decomposition Space ◽  
Criminal Intelligence

In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethical decision-making in criminal intelligence analysis. Machine learning algorithms have played important roles in the decision-making process over the past decades. Intelligence analysts are increasingly being presented with smart black box automation that use machine learning algorithms to find patterns or interesting and unusual occurrences in big data sets. Algorithmic opacity is the lack visibility of computational processes such that humans are not able to inspect its inner workings to ascertain for themselves how the results and conclusions were computed. This is a problem that leads to several ethical issues. In the VALCRI project, we developed an abstraction hierarchy and abstraction decomposition space to identify important functional relationships and system invariants in relation to ethical goals. Such explanatory relationships can be valuable for making algorithmic process transparent during the criminal intelligence analysis process.

An Economic Perspective on Algorithmic Fairness

2020 ◽  
Vol 110 ◽  
pp. 91-95 ◽  
Author(s):  
Ashesh Rambachan ◽  
Jon Kleinberg ◽  
Jens Ludwig ◽  
Sendhil Mullainathan
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Decision Making Under Uncertainty ◽  
Economic Perspective ◽  
Optimal Regulation ◽  
Economic Framework ◽  
Computer Scientists ◽  
Algorithmic Bias

There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of “algorithmic bias” or “algorithmic fairness” has come from computer scientists and machine learning researchers. We argue that concerns about algorithmic fairness are at least as much about questions of how discrimination manifests itself in data, decision-making under uncertainty, and optimal regulation. To fully answer these questions, an economic framework is necessary--and as a result, economists have much to contribute.

Autonomous Interpretation Methods of Ultrasonic Data Through Machine Learning Facilitates Novel and Reliable Casing Annulus Characterization

2021 ◽  
Author(s):  
Ram Sunder Kalyanraman ◽  
Xiaoli Chen ◽  
Po-Yen Wu ◽  
Kevin Constable ◽  
Amit Govil ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Complex Task ◽  
Wireline Logs ◽  
Cement Sheath ◽  
Sonic Logs ◽  
Correct Processing

Abstract Ultrasonic and sonic logs are increasingly used to evaluate the quality of cement placement in the annulus behind the pipe and its potential to perform as a barrier. Wireline logs are carried out in widely varying conditions and attempt to evaluate a variety of cement formulations in the annulus. The annulus geometry is complex due to pipe standoff and often affects the behavior (properties) of the cement. The transformation of ultrasonic data to meaningful cement evaluation is also a complex task and requires expertise to ensure the processing is correctly carried out as well interpreted correctly. Cement formulations can vary from heavy weight cement to ultralight foamed cements. The ultrasonic log-based evaluation, using legacy practices, works well for cements that are well behaved and well bonded to casing. In such cases, a lightweight cement and heavyweight cement, when bonded, can be easily discriminated from gas or liquid (mud) through simple quantitative thresholds resulting in a Solid(S) - Liquid(L) - Gas(G) map. However, ultralight and foamed cements may overlap with mud in quantitative terms. Cements may debond from casing with a gap (that is either wet or dry), resulting in a very complex log response that may not be amenable to simple threshold-based discrimination of S-L-G. Cement sheath evaluation and the inference of the cement sheath to serve as a barrier is complex. It is therefore imperative that adequate processes mitigate errors in processing and interpretation and bring in reliability and consistency. Processing inconsistencies are caused when we are unable to correctly characterize the borehole properties either due to suboptimal measurements or assumptions of the borehole environment. Experts can and do recognize inconsistencies in processing and can advise appropriate resolution to ensure correct processing. The same decision-making criteria that experts follow can be implemented through autonomous workflows. The ability for software to autocorrect is not only possible but significantly enables the reliability of the product for wellsite decisions. In complex situations of debonded cements and ultralight cements, we may need to approach the interpretation from a data behavior-based approach, which can be explained by physics and modeling or through observations in the field by experts. This leads a novel seven-class annulus characterization [5S-L-G] which we expect will bring improved clarity on the annulus behavior. We explain the rationale for such an approach by providing a catalog of log response for the seven classes. In addition, we introduce the ability to carry out such analysis autonomously though machine learning. Such machine learning algorithms are best carried out after ensuring the data is correctly processed. We demonstrate the capability through a few field examples. The ability to emulate an "expert" through software can lead to an ability to autonomously correct processing inconsistencies prior to an autonomous interpretation, thereby significantly enhancing the reliability and consistency of cement evaluation, ruling out issues related to subjectivity, training, and competency.

Sentiment Analysis for Decision-Making Using Machine Learning Algorithms

Data Science ◽  
2019 ◽  
pp. 285-304
Author(s):  
Mohamed Alloghani ◽  
Thar Baker ◽  
Abir Hussain ◽  
Mohammed Al-Khafajiy ◽  
Mohammed Khalaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms
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