Ai+ Training

Is privacy dead? Or still, there are ways through which it can be ensured that individuals' privacy is maintained in this digital age of hyper-connectivity. In this talk Dr. Waseem Akhtar outlines ideas that employ the concept of Level of Abstraction (LoA) while finding meaning in information, to limit analysis of datasets to ensure privacy. LoA can be used to restrict people, organizations, and algorithms so that while analyzing datasets, information only at the permitted LoA is extracted; datasets are not analyzed and mined for the information that is beyond the permitted LoA.  It has widely been reported in the literature that datasets are regularly mined to extract information that is well beyond the original, transparently stated purpose of capturing data. This may include finding patterns and personality traits using digital footprints of unsuspecting and unaware individuals. Locking a dataset to a particular, clearly described, thoroughly understood, and mutely agreed LoA should ensure that the datasets are not used to extract information beyond the clearly stated purpose for which data is originally captured. An obvious challenge is how to make it transparent so that organizations don’t use long and hard to read and difficult to understand  “terms & policies of use” to avoid locking of datasets. Such terms and policies should be tied to clearly stated LoA and any further use must require getting further permissions. This presents a challenge that should be addressed by developing and establishing innovative ethical norms. The question is are we ready to reimagine privacy and discuss debate and establish new ethical norms. Organizations and systems that rely on the power they acquire, by mining datasets they hold, for any information and insights they deem beneficial will consider such attempts a major threat. This talk aims to trigger deep thinking on the topics, questions, and challenges mentioned above.