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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Scaling up search engine audits: Practical insights for algorithm auditing.

Roberto Ulloa1, Mykola Makhortykh2, Aleksandra Urman3

  • 1Computational Social Science, GESIS - Leibniz-Institute for the Social Sciences, Germany.

Journal of Information Science
|April 15, 2024
PubMed
Summary
This summary is machine-generated.

Virtual agents enable systematic, cost-effective algorithm audits by simulating user behavior for search engines. This research details methods and lessons learned for reliable, transparent, and replicable algorithm performance monitoring.

Keywords:
Algorithm auditingdata collectionsearch engine auditsuser modelling

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Area of Science:

  • Computer Science
  • Information Science

Background:

  • Algorithm audits are crucial for assessing internet information services.
  • Virtual agents offer a scalable solution for simulating user behavior in audits.

Purpose of the Study:

  • To detail the methodology, challenges, and lessons learned from using virtual agents for algorithm audits.
  • To provide recommendations for improving the quality and replicability of virtual agent-based research.

Main Methods:

  • Systematic experiments were conducted using hundreds of virtual agents across eight search engines.
  • Diverse experimental designs and regional placements were employed to simulate real-world conditions.

Main Results:

  • The research infrastructure demonstrated successful performance across multiple data collections and experimental designs.
  • Specific strategies were identified to enhance the quality and reliability of virtual agent audits.

Conclusions:

  • Virtual agents are a promising tool for long-term monitoring of search engine algorithm performance.
  • This work provides a foundation for future research in automated algorithm auditing and transparency.