In Maria Popova’s article, “In a new world of informational abundance, content curation is a new kind of authorship”, she brought to light an interesting point about twitter, “The point is that new tools in general, and Twitter in particular, greatly challenge the binary dichotomy of attention as something that is either given or taken away, distracted. Instead, these tools allow us to direct attention to destinations where it can be sustained with more concentration and immersion”. I found this quote particularly interesting because it challenges the negative reputation social media has for distracting people and making them obsessed with their phones. Popova rather explains that social media, specifically twitter in this example, are used to feed people information and further connect them to more information. I recently wrote an argumentative essay that distraction doesn’t exist because it is difficult to determine what is a distraction and what is a valuable use of time, and anything can be categorized as a distraction. This quote by Popova furthers my claim because she argues that social media isn’t a distraction but rather a different facet which one concentrates upon. What makes social media great is the diverse ways in which it can be used as Popova implies, social media links people to new sources and feeds curiosity. There are accounts made for educational, promotional, personal, and commercial purposes which creates many possibility for what can be gained from concentrating on social media. Maria Popova states in this quote that attention is neither given or taken away meaning that any use of time is of value so no-matter how your time is used you gain something from the experience. Popova’s view is truly insightful because it opens up the possibilities and understandings that can be gained from social media.
Weapons of Math Destruction Through The DIKW Framework
Cathy O’neil explores the ethics behind using algorithms to determine if specific people are adequate matches for certain jobs. Specifically she analyzes tests such as the Kronos test which determines if someone would be a good fit for a job, and the impact tool to weed out underperforming teachers. Both are computerized tests that lack human input, which leaves out many important factors in determining someone’s ability to do a job. Comparing the Kronos test to a human creates two very different processes, I’ll demonstrate through the DIKW framework. One data point the Kronos test may receive is “yes”. The information test has it that this is a response to the test question “Get mad easily?” which the Kronos test knowledge would understand this as a red flag. The wisdom in this situation would be that this person may not be good to hire since they may act irrationally due to their anger. The test doesn’t leave room for explanation or justification as a human might. An interviewer in the same situation may go through a different process. In this situation the interviewer would receive the same data point and information, but the knowledge and wisdom would be different since an interviewer can see things in shades of gray unlike the Kronos test. The knowledge an interviewer may have is “this person may have a mental illness” and then would be followed by the wisdom “I should ask them to elaborate further on why they may get angry easily to better understand the candidate”. There are many contributing factors to why someone responds the way they do on a test, using tests like these are good ways to gather information about a job candidate, but one cannot rely solely upon a test otherwise they won’t get the full picture. The same situation applies in the example including the Impact tool, this only uses mathematical scores, it doesn’t include human responses. In the case with Sarah Wysocki she was known to be a great educator, but her impact score said otherwise and she lost her job. The DIKW framework in this situation would result similarly to that of the Kronos test. The data the algorithm receives is just a number, it reduces a person down to one single score, which isn’t a fair way to estimate someone’s value. The computer would receive this number with the information including many similar scores from other teachers. The knowledge the impact score has is to compare these scores to find the lowest scores. The wisdom that the algorithm shows is that the lower scoring teachers are less fit for their job and therefore should be laid off. This method is unethical and eliminates possibilities for having strong educators like the Kronos test eliminates the possibility of some good workers.