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Algorithmic Accountability.pdf

Data_Society_Algorithmic_Accountability_Primer_FINAL.pdf
Preview of Algorithmic Accountability
🔗 Source: datasociety.net
📊 Size: 1010 KB
📄 Pages: 12 pages
⬇️ Downloads: 142

Summary

Algorithms are sets of instructions for computers to accomplish tasks, used by organizations to make decisions and allocate resources based on large datasets. However, algorithms can perpetuate racial bias and inequality due to biased data, technical constraints, and designer values. Algorithmic decision-making can result in unfair outcomes, such as "technological redlining," which systematically excludes certain groups from opportunities and services. Lack of transparency, auditing, and accountability mechanisms exacerbates the issue, making it difficult to assess and address algorithmic bias. Experts argue that algorithms are not neutral, but rather opinions embedded in mathematics, and that external auditing systems and regulations are necessary to ensure fairness and accountability.

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Description

Algorithms are sets of instructions for computers to accomplish tasks, used by organizations to make decisions and allocate resources based on large datasets.

Technical Information

  • File Format: PDF
  • File Size: 1010 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 142
  • Last Updated: 4 weeks ago

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This PDF document about Algorithmic Accountability provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Algorithmic Accountability.

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