Medical school essays amazon
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Useful links: 1 5 10 20 Report 1 day ago Original post by emj Mine came through immediately. Original post by Vet Student I got an essay Q in mine but it might be just me? Vet Student Badges: Original post by oberon Strange Felicity Felix Badges: 6. Any one have the same issue or can help me out? Original post by Vet Student I just checked and that's the exact same as me!
Platforms are rife with attempts to game their algorithms, to show up higher in search results or recommended content, through fake clicks, fake reviews, fake followers, and more. Automatic essay grading software focuses primarily on metrics like sentence length, vocabulary, spelling, and subject-verb agreement, but is unable to evaluate aspects of writing that are hard to quantify, such as creativity. As a result, gibberish essays randomly generated by computer programs to contain lots of sophisticated words score well.
Essays from students in mainland China, which do well on essay length and sophisticated word choice, received higher scores from the algorithms than from expert human graders, suggesting that these students may be using chunks of pre-memorized text. As USA education policy began over-emphasizing student test scores as the primary way to evaluate teachers, there have been widespread scandals of teachers and principals cheating by altering students scores, in Georgia, Indiana, Massachusetts, Nevada, Virginia, Texas, and elsewhere.
When metrics are given undue importance, attempts to game those metrics become common. It is much easier to measure short-term quantities: click through rates, month-over-month churn, quarterly earnings. Many long-term trends have a complex mix of factors and are tougher to quantify.
What is the long-term impact on user trust of having your brand associated with promoting pedophilia , white supremacy , and flat-earth theories? What is the long-term impact on hiring to be the subject of years worth of privacy scandals , political manipulation , and facilitating genocide? A recent Harvard Business Review article looked at Wells Fargo as a case study of how letting metrics replace strategy can harm a business. After identifying cross-selling as a measure of long-term customer relationships, Wells Fargo went overboard emphasizing the cross-selling metric: intense pressure on employees combined with an unethical sales culture led to 3.
The metric of cross-selling is a much more short-term concern compared to the loftier goal of nurturing long-term customer relationships.
Overemphasizing metrics removes our focus from long-term concerns such as our values, trust and reputation, and our impact on society and the environment, and myopically focuses on the short-term. It matters which metrics we gather and in what environment we do so. Unfortunately, these metrics are gathered in environments engineered to be highly addictive, laden with dark patterns , and where financial and design decisions have already greatly circumscribed the range of options. So you make a line offering such food, automatically loading the next plate as soon as the bag of chips or candy in front of the young person has been consumed.
Such metrics are not indicative of what we would prefer in a healthier or more empowering environment. All this is not to say that we should throw metrics out altogether. Data can be valuable in helping us understand the world, test hypotheses, and move beyond gut instincts or hunches.
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Metrics can be useful when they are in their proper context and place. One way to keep metrics in their place is to consider a slate of many metrics for a fuller picture and resist the temptation to try to boil these down to a single score. For instance, knowing the rates at which tech companies hire people from under-indexed groups is a very limited data point.
For evaluating diversity and inclusion at tech companies, we need to know comparative promotion rates, cap table ownership, retention rates many tech companies are revolving doors driving people from under-indexed groups away with their toxic cultures , number of harassment victims silenced by NDAs, rates of under-leveling, and more. Even then, all this data should still be combined with listening to first-person experiences of those working at these companies.
To that end, data scientists and machine learning engineers must partner with or learn the skills of user experience research, giving users a voice. Another key to keeping metrics in their proper place is to keep domain experts and those who will be most impacted closely involved in their development and use. Surely most teachers could have foreseen that evaluating teachers primarily on the standardized test scores of their students would lead to a host of negative consequences.
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I am not opposed to metrics; I am alarmed about the harms caused when metrics are overemphasized, a phenomenon that we see frequently with AI, and which is having a negative, real-world impact. By keeping the risks of metrics in mind, we can try to prevent these harms. Police departments are allowing Amazon to stream call information directly in real-time, and Amazon requires police departments to read pre-approved scripts when talking about the program.
This creation of an extensive surveillance network, the murky private-public partnership surrounding it, and a lack of any sort of regulations or oversight is frightening. And this is just one of many examples related to surveillance technology that have recently come to light.
I frequently talk with people who are not that concerned about surveillance, or who feel that the positives outweigh the risks. Here, I want to share some important truths about surveillance:. While I was writing this post, a number of investigative articles came out with disturbing new developments related to surveillance.
I decided that rather than attempt to include everything in one post which would make it too long and too dense , I would go ahead and share the above facts about surveillance, as they are just a relevant as ever. There is a long history of data about sensitive attributes being misused, including the use of the USA Census to intern Japanese Americans, a system of identity cards introduced by the Belgian colonial government that were later used during the Rwandan genocide in which nearly a million people were murdered , and the role of IBM in helping Nazi Germany use punchcard computers to identify and track the mass killing of millions of Jewish people.