The Challenges of Machine Learning and Their Economic Implications

Borrellas, Pol; Unceta, Irene

doi:10.3390/e23030275

Cited by 9 publications

(4 citation statements)

References 80 publications

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“…The performance of the created models was then evaluated using several metrics in the evaluation phase of the CRISP-DM methodology. When applying some data mining approaches, the problem of overtraining may occur (Andrés et al, 2021;Borrellas & Unceta, 2021). To avoid this problem and create models with good generalizability, we used a combination of simple and five-fold cross-validation technique.…”

Section: H2: the Combination Of Selected Individual Models Optimizes ...mentioning

confidence: 99%

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Durica,

Frnda,

Svabova

2023

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Research background: Financial distress or non-prosperity prediction has been a widely discussed topic for several decades. Early detection of impending financial problems of the company is crucial for effective risk management and important for all entities involved in the company’s business activities. In this way, it is possible to take the actions in the management of the company and eliminate possible undesirable consequences of these problems. Purpose of the article: This article aims to innovate financial distress prediction through the creation of individual models and ensembles, combining machine learning techniques such as decision trees and neural networks. These models are developed using real data. Beyond serving as an autonomous and universal tool especially useful in the Slovak economic conditions, these models can also represent a benchmark for Central European economies confronting similar economic dynamics. Methods: The prediction models are created using a dataset consisting of more than 20 financial ratios of more than 19 thousand real companies. Partial models are created employing machine learning algorithms, namely decision trees and neural networks. Finally, all models are compared based on a wide range of selected performance metrics. During this process, we strictly use a data mining methodology CRISP-DM. Findings & value added: The research contributes to the evolution of financial prediction and reveals the effectiveness of ensemble modelling in predicting financial distress, achieving an overall predictive ability of nearly 90 percent. Beyond its Slovak origins, this study provides a framework for early financial distress prediction. Although the models are created for diverse industries within the Slovak economy, they could also be useful beyond national borders. Moreover, the CRISP-DM methodological framework enables its adaptability for companies in other countries.

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Section: H2: the Combination Of Selected Individual Models Optimizes ...mentioning

confidence: 99%

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Durica,

Frnda,

Svabova

2023

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“…Finally, tort and antidiscriminatory laws should be expanded to cover AI and ML. 121 Doing so may assist in outlining boundaries and settling disputes regarding liability and accountability in cases where its use is improper.…”

Section: Postoperative Outcomes and Complicationsmentioning

confidence: 99%

Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

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“…The FairFace dataset [65] is a collection of ≈100 thousand facial images extracted from the YFCC-100M Flickr dataset [165]. Automated models trained on FairFace can exploit age group (age ranges of [0-2], [3][4][5][6][7][8][9], [10][11][12][13][14][15][16][17][18][19], [20][21][22][23][24][25][26][27][28][29], [30][31][32][33][34][35][36][37][38][39], [40][41][42][43][44][45][46][47][48][49], [50]…”

Section: The Datasetmentioning

confidence: 99%

“…The resulting benefits are numerous and multifaceted. For example, it can contribute to increasing well-being both on a collective and an individual level, for example by generating wealth [5] or taking care of tedious or dangerous tasks [6]. Moreover, it can promote fairer behaviors toward social and political equality [4].…”

Section: Introductionmentioning

confidence: 99%

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Franco

Oneto

Navarin

et al. 2021

Entropy

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In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.

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The Challenges of Machine Learning and Their Economic Implications

Cited by 9 publications

References 80 publications

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Artificial neural network and decision tree-based modelling of non-prosperity of companies

Machine Learning in Spine Surgery: A Narrative Review

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

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