【Session Theme: Trustworthy AI】
A summarized overview of the black box society and the concept of technological due process.
In an era where big data prevails, tech firms and financial sectors poured themselves into the maniac of mass data collection. No rigid reason needed – quantity is all that matters when the miraculous algorithms came to turn data into digital gold. However, as automatic systems took over from credit reporting, social media timeline arrangement, search engine result ranking to the extent of spotting anti-terrorism suspects, the tech industry found themselves impossible to shrug off responsibilities by claiming their results “machine learning magic.”
Despite the threat of mass data collection and “black box” algorithms sitting at the core of governmental and commercial activities, regulators are not underequipped to solve the problem. The talk would address human factors affecting different aspects of machine learning algorithms, the relevant social issues, and potential regulation directions.
About Poren Chiang
Poren Chiang (aka @RSChiang) is a LL.M. graduate at UCLA School of Law, specialized in Digital Law and Policy. His research agenda focuses on the challenges posed to administrative procedures by rapid changing technology.
Apart from writing FLOSS projects for fun, he also co-founded NTU Open Source Community and SITCON, a tech education initiative to support young programming self-starters.