Special Issue "Machine Learning Ecosystems: Opportunities and Threads"
Deadline for manuscript submissions: 31 January 2021.
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
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 papers will be 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 1800 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.
- machine learning accountability
- data products
- data governance
- data-driven transformation
- data-driven organizations
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Differential Replication of Machine Learning Models for Highly Regulated Environments
Authors: Irene unceta, Jordi Nin, Oriol Pujol
Differential replication is a method to adapt existing machine learning solutions to the demands of highly-regulated environments by reusing knowledge from generation to generation of models. Copying is a differential replication technique to project 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 the financial industry. In particular, we shows how copies can be used to ensure the decomposability of attributes during pre-processing, as well as to reduce the time-to-market of machine learning solutions.
Title: Shadows from the past: a comparison of classical and new privacy mechanism
Authors: Daniel Heredia, Miguel Nunez-del-Prado, Hugo Alatrista-Salas
In the last decades, the development of interconnectivity, pervasive systems, citizen sensors, and Big Data technologies allowed us to gather a lot of 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 researches to exploit individual data in a privacy-aware way. Therefore, 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 privacy-aware machine learning techniques.
Title: The Challenges of Machine Learning and their Economic Implications
Authors: Pol Borrelles, Irene Unceta
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 is 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 the 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.