Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

Mobile Value Added Services: A Business Growth Opportunity for Women Entrepreneurs

Examines the potential for mobile value-added services adoption by women entrepreneurs in Egypt, Nigeria, and Indonesia in expanding their micro businesses; challenges, such as access to digital channels; and the need for services tailored to women

Monitoring food marketing to children: A joint Nordic monitoring protocol for marketing of foods and beverages high in fat, salt and sugar (HFSS) towards children and young people

The protocol describes methods for how to monitor marketing of foods and beverages high in fat, salt and sugar towards children and young people at a given time as cross-sectional studies, as well as allowing for monitoring of trends. The data provided could also be used for evaluation purposes, for instance providing relevant data for evaluating regulation practices and schemes in the respective countries; to study advertising and marketing practices, contents and forms over time. In addition to being a tool for monitoring purposes within each country, the protocol will also enable comparisons between the Nordic countries by establishing a joint understanding on how each marketing channel should be monitored. The protocol has been developed as a Nordic project between representatives and experts from Iceland, Finland, Sweden, Denmark and Norway together with international experts

Local Ranking Problem on the BrowseGraph

The "Local Ranking Problem" (LRP) is related to the computation of a centrality-like rank on a local graph, where the scores of the nodes could significantly differ from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many different tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing traffic performed on its pages (local BrowseGraph) and, as a consequence, the local computation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly overlooked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user browsing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from different domains. We show that the distance between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8

Cereal FACTS: Evaluating the Nutrition Quality and Marketing of Children's Cereals

Evaluates cereal companies' marketing practices in 2008-09, immediately before and after full implementation of their pledges to reduce unhealthy marketing to children. Ranks brands with combined scores for nutrition quality and marketing exposure

Systematic Review on Privacy Categorization

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people's decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorization, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorization involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorization has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorization as a research attempt is still meaningful and may have a future

Certain aspects of personal data protection in the social network: european experience and legislative regulation in Ukraine

The purpose of this study is to examine some aspects of personal data protection in the social network, a comparative analysis of the protection of personal data in the social network under Ukrainian and European legislation, namely the General Data Protection Regulation of the European Union. The methods used in this work are: dialectical, comparative-legal, formal-logical, analysis and dogmatic interpretation. Each of these methods was used in the study to understand and qualitatively explain to the audience categories the individual aspects of personal data protection on the social network. This article reveals the notion of: personal data in the social network, the features of their collection, storage and protection in accordance with European legislation and the development of proposals aimed at improving these processes in Ukraine. The research also addresses the following issues: Features of managing consent to the processing of personal data that have already been obtained; who can act as an "operator" under EU law and what actions he can take; who can act as "controller" and what functions it performs. The article concludes that there is an urgent need to streamline Ukrainian domestic legislation in line with EU law, which should result in a new law on personal data protection that complies with GDPR norms. As a result, a new law on personal data protection may soon emerge in Ukraine, replacing the outdated Law of Ukraine “On Personal Data Protection” of 01.06.2010, which is a “mirror” of   the repealed Directive 95/46/EC of the European Parliament and of the Council