T M

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On April 14, 2021, T M commented on Extending People Analytics to K-Pop :

Carla, I agree that the output emphasizes revenue generation over art form. You know it’s significant when Forbes writes about you in those revenue and global growth terms: https://www.forbes.com/sites/tamarherman/2018/12/26/k-pop-claims-space-in-global-markets-while-looking-at-future-in-2018/?sh=499e18bc3998.

While most popular music is a revenue making business from the get-go, K-pop specifically may owe this label to its origin story. From what I know, the government of South Korea heavily invested in the promotion of Korean entertainment industry after the Asian financial crisis of 1997. Heavily in debt to IMF and other donors, South Korea looked for ways to generate more national income, and the then president saw great potential in exporting Korean culture. This article I found explains it in more detail: https://www.thedailyvox.co.za/how-a-financial-crisis-created-k-pop/. I had only vaguely heard of this before but the article filled in the gaps in my knowledge a bit.

Aliya, thank you for covering this article. It gives a detailed review of how a cultural background’s effect is easy to misinterpret. At the same time, it offers research based ways to take advantage of the cultural/language differences to foster innovation in an organization. A meta point that is not specifically mentioned in that article but one that must be named is that true diversity is important in organizations as well as in developing the algorithms that these organizations use in their people analytics. Without such diversity in building the tools, the biases and errors that often exist in homogenous teams will easily migrate to the models and algorithms they produce.

On April 14, 2021, T M commented on Why police using facial recognition technology is wrong :

Alfred, this is a great topic. With systems like facial recognition, both false positives and false negatives raise concerns that affect real human beings. An abundance of care must be shown in creating such systems, training the models, and, as you described so well, using them. It is true that the use of such systems is too alluring to police and law enforcement, and it is disappointing that meaningful action is not being put to place to address these legitimate concerns. I sometimes wonder if the way Massachusetts took (https://www.nytimes.com/2021/02/27/technology/Massachusetts-facial-recognition-rules.html) is the only viable solution so far. The article notes that other states and localities had to resort to “all or nothing” approach and ban the technology altogether. Unless we see tangible effort on the part of local police authorities that they are addressing these concerns, many more states may have to resort to policy making to deter abuse of facial recognition systems.

I share the sentiment that great care should be given to data protection, and to the ethical use of any collected data. Reading through the article you covered, I also thought that the “good data governance” that the author proposes is also a way of creating accountability on the part of the data collector, something that is missing in many countries not covered by GDPR. While I do not share the GDPR’s approach that each of us own the data about ourselves, I support its mission to hold all data collectors to much higher standards when it comes to responsible use of the power that data supplies them with.

On April 14, 2021, T M commented on Possible perils in people analytics that you need to be aware of :

I share the sentiment that great care should be given to data protection and the ethical use of any collected data. Reading through the article you covered, I also thought that the “good data governance” was also a way of creating accountability on the part of the collector, something that