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Quick Questions on Data and Algorithmic Transparency #APrIGF

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Quick Questions on Data and Algorithmic Transparency #APrIGF

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On prevention of data-driven discrimination.

@bact Arthit Suriyawongkul. Foundation for Internet and Civic Culture.
Presented at WS80 Algorithmic Transparency: Understanding why we are profiled in a certain manner, Asia Pacific Regional Internet Governance Forum 2017 Bangkok. 29 July 2017 http://apps.2017.rigf.asia/submission/proposaldetail?id=109

On prevention of data-driven discrimination.

@bact Arthit Suriyawongkul. Foundation for Internet and Civic Culture.
Presented at WS80 Algorithmic Transparency: Understanding why we are profiled in a certain manner, Asia Pacific Regional Internet Governance Forum 2017 Bangkok. 29 July 2017 http://apps.2017.rigf.asia/submission/proposaldetail?id=109

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Quick Questions on Data and Algorithmic Transparency #APrIGF

  1. 1. QUICK QUESTIONS ON DATA AND ALGORITHMIC TRANSPARENCY @bact
 Arthit Suriyawongkul
 Foundation for Internet
 and Civic Culture WS80 Algorithmic Transparency: Understanding why we are profiled in a certain manner
 
 Asia Pacific Regional Internet Governance Forum 2017 Bangkok — 29 July 2017
  2. 2. DATA AND ALGORITHMIC DISCRIMINATION ➤ Discrimination in credit, scholarship, labor, jobs, housing, or unfair pricing practices ➤ Machine recommendations or generated contents have influence on human decisions on a wider scale (like errors in earning reports generated from robot journalist) ➤ Over- and under-representation problems (Data rich vs Data poor)
  3. 3. ACM PRINCIPLES FOR ALGORITHMIC TRANSPARENCY AND ACCOUNTABILITY ➤ 1. Awareness ➤ 2. Access and redress ➤ 3. Accountability ➤ 4. Explanation ➤ 5. Data Provenance ➤ 6. Auditability ➤ 7. Validation and Testing
  4. 4. IT IS DIFFICULT ➤ Transparency in source code (open source) is not exactly equal to transparency in algorithm (code alone may not work without data/model) ➤ Apart from measures for input, monitoring of output is required ➤ FTC suggests to monitor the results, if it discriminates Input Processing (Decision-making) Output Where to audit? At input, algorithm, or output?
  5. 5. TECHNICAL/PRACTICAL QUESTION ➤ Does it possible to spot problematic set of data that leads to discrimination? ➤ If so, does it possible to remove the influence of that set of data from the model (that trained upon those data) ➤ How practical it is? If the training keeps iterating and the model keeps accumulating. How not to retrain from scratch?
  6. 6. CONTACT ➤ @bact ➤ Arthit Suriyawongkul ➤ Foundation for Internet and Civic Culture (Thai Netizen Network) ➤ arthit@thainetizen.org ➤ Links for this presentation
 https://bact.cc/2017/algorithmic-transparency-links/

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