skip to main content
research-article
Free Access

Accountability in Algorithmic Decision-making: A view from computational journalism

Published:28 November 2015Publication History
Skip Abstract Section

Abstract

Every fiscal quarter automated writing algorithms churn out thousands of corporate earnings articles for the AP (Associated Press) based on little more than structured data. Companies such as Automated Insights, which produces the articles for AP, and Narrative Science can now write straight news articles in almost any domain that has clean and well-structured data: finance, sure, but also sports, weather, and education, among others. The articles aren’t cardboard either; they have variability, tone, and style, and in some cases readers even have difficulty distinguishing the machine-produced articles from human-written ones.

References

  1. ACM. 2015. Software Engineering Code of Ethics and Professional Practice; https://www.acm.org/about/se-code#fullGoogle ScholarGoogle Scholar
  2. ACM Code of Ethics and Professional Conduct. 1992; https://www.acm.org/about/code-of-ethics.Google ScholarGoogle Scholar
  3. Citron, D., Pasquale, F. 2014. The scored society: due process for automated predictions. Washington Law Review 89.Google ScholarGoogle Scholar
  4. Clerwall, C. 2014. Enter the robot journalist. Journalism Practice 8(5): 519-531.Google ScholarGoogle ScholarCross RefCross Ref
  5. Diakopoulos, N. 2015. Algorithmic accountability: journalistic investigation of computational power structures. Digital Journalism 3(3): 398-415.Google ScholarGoogle ScholarCross RefCross Ref
  6. Diakopoulos, N. 2014. Algorithmic defamation: the case of the shameless autocomplete. Tow Center for Digital Journalism.Google ScholarGoogle Scholar
  7. Diakopoulos, N., et al. 2014. Data-driven rankings: the design and development of the IEEE Top Programming Languages news app. Proceedings of the Symposium on Computation + Journalism.Google ScholarGoogle Scholar
  8. Diakopoulos, N. 2015. How Uber surge pricing really works. Washington Post Wonkblog (April 17).Google ScholarGoogle Scholar
  9. Don Ray Drive-A-Way Co. v. Skinner, 785 F. Supp. 198 (D.D.C. 1992). 1992; http://law.justia.com/cases/federal/district-courts/FSupp/785/198/2144490/.Google ScholarGoogle Scholar
  10. Epstein, R., Robertson, R.E. 2015. The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences (PNAS) 112(33).Google ScholarGoogle ScholarCross RefCross Ref
  11. Eslami, M., et al. 2015. "I always assumed that I wasn't really that close to {her}": reasoning about invisible algorithms in the news feed. Proceedings of the 33rd Annual ACM SIGCHI Conference on Human Factors in Computing Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Feldman, M., et al. 2015. Certifying and removing disparate impact. Proceedings of the 21st ACM International Conference on Knowledge Discovery and Data Mining: 259-268. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Herlocker, J. L., et al. 2000. Explaining collaborative filtering recommendations. Proceedings of the ACM Conference on Computer Supported Cooperative Work: 241-250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kalhan, A. 2013. Immigration policing and federalism through the lens of technology, surveillance, and privacy. Ohio State Law Journal 74.Google ScholarGoogle Scholar
  15. Kashin, K., et al. 2015. Systematic bias and nontransparency in US Social Security Administration forecasts. Journal of Economic Perspectives 29(2).Google ScholarGoogle ScholarCross RefCross Ref
  16. Kraemer, F., et al. 2010. Is there an ethics of algorithms? Ethics and Information Technology 13(3): 251-260. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Letham, B., et al. 2015. Building interpretable classifiers with rules using Bayesian analysis. Annals of Applied Statistics.Google ScholarGoogle ScholarCross RefCross Ref
  18. Mitchell, A., et al. 2015. Millennials and Political News. Pew Research Center, Journalism and Media (June 1); http://www.journalism.org/2015/06/01/millennials-political-news/.Google ScholarGoogle Scholar
  19. Muckrock. 2011. Source code of HEAT SAFETY TOOL; https://www.muckrock.com/foi/united-states-of-america-10/source-code-of-heat-safety-tool-766/.Google ScholarGoogle Scholar
  20. Mühlbacher, T., et al. 2014. Opening the black box: strategies for increased user involvement in existing algorithm implementations. IEEE Transactions on Visualization and Computer Graphics 20(12): 1643-1652.Google ScholarGoogle ScholarCross RefCross Ref
  21. Nissenbaum, H. 1996. Accountability in a computerized society. Science and Engineering Ethics 2(1): 25-42.Google ScholarGoogle ScholarCross RefCross Ref
  22. Schaffer, J., et al. 2015. Getting the message?: a study of explanation interfaces for microblog data analysis. Proceedings of the 20th International Conference on Intelligent User Interfaces (IUI): 345-356. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Sen, S., et al. 2015. Turkers, Scholars, "Arafat" and "Peace": cultural communities and algorithmic gold standards. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing: 826- 838. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sifry, M. 2014. Facebook wants you to vote on Tuesday. Here's how it messed with your feed in 2012. Mother Jones (Oct. 31); http://www.motherjones.com/politics/2014/10/can-voting-facebook-button-improve-voter-turnout.Google ScholarGoogle Scholar
  25. Tintarev, N., Masthoff, J. 2007. A survey of explanations in recommender systems. Proceedings of the International Conference on Data Engineering: 801-810. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image Queue
    Queue  Volume 13, Issue 9
    Structured Data
    November-December 2015
    156 pages
    ISSN:1542-7730
    EISSN:1542-7749
    DOI:10.1145/2857274
    Issue’s Table of Contents

    Copyright © 2015 ACM

    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 November 2015

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Popular
    • Editor picked

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format