Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.7
Journals
Entropy
Special Issues
Machine Learning Ecosystems: Opportunities and Threats
entropy-logo
Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Machine Learning Ecosystems: Opportunities and Threats

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 12940

Special Issue Editor

Dr. Jordi Nin

E-Mail Website1 Website2 Website3
Guest Editor
Ramon Llull University, ESADE, Av. Torre Blanca, 59, 08172 Sant Cugat, Barcelona, Spain
Interests: complex networks; financial networks; machine learning; ethics and machine learning

Special Issue Information

Dear Colleagues,

In many every-day examples, specific constraints hold that prevent optimal performance of machine learning models in the wild. Both their training data and the models themselves are usually subject to many internal and external restrictions. Internal restrictions include the design and maintenance of technological infrastructures, the alignment with business needs, technical debt, or the internal dysfunctionalities of companies. External constraints are related to the accessibility of the data or the legislation companies must obey, among others. Altogether, these restrictions have been studied from different perspectives in the machine learning literature, including accountability, privacy-preserving technologies, fairness, interpretability, data governance, etc., but also from not-so-technical perspectives, such as legal liability, human talent management, firms’ organizational structures, or economic dimensions of machine learning. This Special Issue wants to be a common forum for researchers working on these different machine learning dimensions that must interact in any real-life data centered application.

Dr. Jordi Nin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning accountability
  • interpretability
  • fairness
  • privacy
  • data products
  • data governance
  • data-driven transformation
  • data-driven organizations

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 1478 KiB  
Article
A Banking Platform to Leverage Data Driven Marketing with Machine Learning
by Marc Torrens and Amir Tabakovic
Entropy 2022, 24(3), 347; https://0-doi-org.brum.beds.ac.uk/10.3390/e24030347 - 28 Feb 2022
Cited by 5 | Viewed by 2625
Abstract
Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of [...] Read more.
Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank’s retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer. Full article
(This article belongs to the Special Issue Machine Learning Ecosystems: Opportunities and Threats)
►▼ Show Figures

Figure 1

17 pages, 2703 KiB  
Article
An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box
by Rosario Medina-Rodríguez, César Beltrán-Castañón and Ronaldo Fumio Hashimoto
Entropy 2021, 23(11), 1541; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111541 - 19 Nov 2021
Viewed by 1545
Abstract
Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between [...] Read more.
Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates. Full article
(This article belongs to the Special Issue Machine Learning Ecosystems: Opportunities and Threats)
►▼ Show Figures

Figure 1

21 pages, 1153 KiB  
Article
Toward a Comparison of Classical and New Privacy Mechanism
by Daniel Heredia-Ductram, Miguel Nunez-del-Prado and Hugo Alatrista-Salas
Entropy 2021, 23(4), 467; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040467 - 15 Apr 2021
Cited by 1 | Viewed by 2126
Abstract
In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather many data from different sources worldwide. This phenomenon has raised privacy concerns around the globe, compelling states to enforce data protection laws. In [...] Read more.
In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather many data from different sources worldwide. This phenomenon has raised privacy concerns around the globe, compelling states to enforce data protection laws. In parallel, privacy-enhancing techniques have emerged to meet regulation requirements allowing companies and researchers to exploit individual data in a privacy-aware way. Thus, data curators need to find the most suitable algorithms to meet a required trade-off between utility and privacy. This crucial task could take a lot of time since there is a lack of benchmarks on privacy techniques. To fill this gap, we compare classical approaches of privacy techniques like Statistical Disclosure Control and Differential Privacy techniques to more recent techniques such as Generative Adversarial Networks and Machine Learning Copies using an entire commercial database in the current effort. The obtained results allow us to show the evolution of privacy techniques and depict new uses of the privacy-aware Machine Learning techniques. Full article
(This article belongs to the Special Issue Machine Learning Ecosystems: Opportunities and Threats)
►▼ Show Figures

Figure 1

17 pages, 3563 KiB  
Article
Differential Replication for Credit Scoring in Regulated Environments
by Irene Unceta, Jordi Nin and Oriol Pujol
Entropy 2021, 23(4), 407; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040407 - 30 Mar 2021
Viewed by 1761
Abstract
Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new [...] Read more.
Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication by projecting a given classifier onto a new hypothesis space, in circumstances where access to both the original solution and its training data is limited. The resulting model replicates the original decision behavior while displaying new features and characteristics. In this paper, we apply this approach to a use case in the context of credit scoring. We use a private residential mortgage default dataset. We show that differential replication through copying can be exploited to adapt a given solution to the changing demands of a constrained environment such as that of the financial market. In particular, we show how copying can be used to replicate the decision behavior not only of a model, but also of a full pipeline. As a result, we can ensure the decomposability of the attributes used to provide explanations for credit scoring models and reduce the time-to-market delivery of these solutions. Full article
(This article belongs to the Special Issue Machine Learning Ecosystems: Opportunities and Threats)
►▼ Show Figures

Figure 1

23 pages, 320 KiB  
Article
The Challenges of Machine Learning and Their Economic Implications
by Pol Borrellas and Irene Unceta
Entropy 2021, 23(3), 275; https://0-doi-org.brum.beds.ac.uk/10.3390/e23030275 - 25 Feb 2021
Cited by 5 | Viewed by 3802
Abstract
The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, [...] Read more.
The deployment of machine learning models is expected to bring several benefits. Nevertheless, as a result of the complexity of the ecosystem in which models are generally trained and deployed, this technology also raises concerns regarding its (1) interpretability, (2) fairness, (3) safety, and (4) privacy. These issues can have substantial economic implications because they may hinder the development and mass adoption of machine learning. In light of this, the purpose of this paper was to determine, from a positive economics point of view, whether the free use of machine learning models maximizes aggregate social welfare or, alternatively, regulations are required. In cases in which restrictions should be enacted, policies are proposed. The adaptation of current tort and anti-discrimination laws is found to guarantee an optimal level of interpretability and fairness. Additionally, existing market solutions appear to incentivize machine learning operators to equip models with a degree of security and privacy that maximizes aggregate social welfare. These findings are expected to be valuable to inform the design of efficient public policies. Full article
(This article belongs to the Special Issue Machine Learning Ecosystems: Opportunities and Threats)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop