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The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics.

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Algorithmic accountability.

Hetan Shah1,2

  • 1Royal Statistical Society, London, UK h.shah@rss.org.uk.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|August 8, 2018
PubMed
Summary
This summary is machine-generated.

Algorithms and machine learning offer social benefits but require public trust. This paper proposes solutions like transparency and better governance for algorithmic accountability, addressing wider societal impacts.

Keywords:
accountabilityalgorithmsbiasdata ethicsgovernance

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Area of Science:

  • Computer Science
  • Social Science
  • Public Policy

Background:

  • The increasing integration of algorithms and machine learning (ML) into society presents significant opportunities for social impact.
  • Public trust and a 'license to operate' are crucial for the ethical deployment of these technologies.
  • Concerns exist regarding the accountability of algorithms in public-facing applications.

Purpose of the Study:

  • To outline approaches for ensuring algorithmic accountability and trustworthiness.
  • To advocate for public sector confidence in negotiating with the private sector regarding data.
  • To highlight the need for regulators to address technological challenges.

Main Methods:

  • Review of current concerns and proposed solutions for algorithmic governance.
  • Analysis of the implications of algorithms in the public sphere.
  • Discussion of broader societal challenges posed by ubiquitous algorithms.

Main Results:

  • Key strategies include enhanced transparency, outcome monitoring, and improved governance frameworks.
  • Public sector bodies should be empowered to negotiate data terms with private entities.
  • Recommendations encompass workforce diversity, ethics training, and deliberative public bodies.

Conclusions:

  • Addressing algorithmic accountability requires a multi-faceted approach, including technical, organizational, and regulatory measures.
  • Wider concerns such as data monopolies, democratic challenges, and public interest must be considered.
  • Proactive adaptation by regulators and stakeholders is essential for harnessing the benefits of algorithms responsibly.