skip to main content
10.1145/3126858.3133314acmotherconferencesArticle/Chapter ViewAbstractPublication PageswebmediaConference Proceedingsconference-collections
keynote

Fairness, Accountability, and Transparency while Mining Data from the Web and Social Networks

Published: 17 October 2017 Publication History

Abstract

Digital media have been changing fundamentally our society, as a consequence of easier access to contents as well as better and cheaper generation and dissemination through the internet, as witnessed by services such as online videos, games and social networks. More recently, there has been an increasing availability of "smart" services that, among other tasks, help users to locate, understand and analyze automatically media of interest. Smart services are often based on algorithms from data mining and related areas such as machine learning and artificial intelligence. Beyond the efficiency and effectiveness of theses services, there is a growing concern about the fairness, accountability and transparency associated with them, which is the subject of this talk. Fairness comprises guarantees that algorithms are neither biased nor discriminatory, even when they are mathematically and computationally correct. Accountability means the identification of entities, human or not, that should be held responsible for the algorithms' consequences. Transparency is the property of generating understandable explanations on the algorithms' outcomes. In this talk we are going to discuss and characterize data mining algorithms, in particular when applied to web and social networks, with respect to fairness, accountability and transparency, and present strategies that assure these properties while satisfying other usual requirements such as precision, effectiveness, and privacy preservation.

Cited By

View all
  • (2021)Big Data, Classification, Clustering and Generate Rules: An inevitably intertwined for Prediction2021 International Conference on Information Technology (ICIT)10.1109/ICIT52682.2021.9491733(149-155)Online publication date: 14-Jul-2021
  • (2020)Social Media Representations of Law Enforcement within Four Diverse Chicago NeighborhoodsJournal of Contemporary Ethnography10.1177/089124162094329149:6(832-852)Online publication date: 22-Jul-2020
  • (2020)Images of Women on Social Media: A Comparison of Four Diverse Chicago NeighborhoodsViolence and Gender10.1089/vio.2019.0064Online publication date: 28-Jul-2020

Index Terms

  1. Fairness, Accountability, and Transparency while Mining Data from the Web and Social Networks

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
      October 2017
      522 pages
      ISBN:9781450350969
      DOI:10.1145/3126858
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      • SBC: Brazilian Computer Society
      • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
      • CGIBR: Comite Gestor da Internet no Brazil
      • CAPES: Brazilian Higher Education Funding Council

      In-Cooperation

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 October 2017

      Check for updates

      Author Tags

      1. data mining
      2. distributed processing
      3. parallel

      Qualifiers

      • Keynote

      Conference

      Webmedia '17
      Sponsor:
      • SBC
      • CNPq
      • CGIBR
      • CAPES
      Webmedia '17: Brazilian Symposium on Multimedia and the Web
      October 17 - 20, 2017
      RS, Gramado, Brazil

      Acceptance Rates

      WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
      Overall Acceptance Rate 270 of 873 submissions, 31%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)3
      • Downloads (Last 6 weeks)2
      Reflects downloads up to 17 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2021)Big Data, Classification, Clustering and Generate Rules: An inevitably intertwined for Prediction2021 International Conference on Information Technology (ICIT)10.1109/ICIT52682.2021.9491733(149-155)Online publication date: 14-Jul-2021
      • (2020)Social Media Representations of Law Enforcement within Four Diverse Chicago NeighborhoodsJournal of Contemporary Ethnography10.1177/089124162094329149:6(832-852)Online publication date: 22-Jul-2020
      • (2020)Images of Women on Social Media: A Comparison of Four Diverse Chicago NeighborhoodsViolence and Gender10.1089/vio.2019.0064Online publication date: 28-Jul-2020

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media