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.
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.
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.
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.
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.
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/