“HR Goes Agile” article

 

After reading “HR Goes Agile” article and reviewing the course slides, using business and/or news media sources find an example of an organization that you believe has exhibited agility in its HR policies and practices in responding to the COVID19 pandemic. Share the article link or pdf and explain why you selected this company. Please follow the attached Rubric

 

Sample Solution

rime opportunities will increase
When one is using facial recognition to prevent crime, it will also create opportunities for criminals. Stalking and identity fraud, for instance, will be made much easier. Cameras with facial recognition technology can be used to keep track of people. These cameras do not have to be expensive, which will make it more tempting for criminals to buy them, and use them for the wrong purposes. The facial recognition technologies can provide stalkers with more data about their victims: they can be used to get to know a person’s weekly schedule or to keep track of when someone is going away. It provides critical information, which for instance tells a criminal if it is safe for them to break into a house.
These cameras can also be used to replace the user ID/password authentication method to access computer systems to obtain services in the name of another person. Even though the new methods can effectively distinguish the real face from fake photos by calculating the depth of the face, it is not that hard to break into a system that uses facial recognition. [3][8] US senator Al Franken has given his opinion on the problem of this topic in an open letter to the creators of an app that uses facial recognition (i.e. NameTag): “Unlike other biometric identifiers such as iris scans and fingerprints, facial recognition is designed to operate at a distance, without the knowledge or consent of the person being identified,” he wrote. “Individuals cannot reasonably prevent themselves from being identified by cameras that could be anywhere – on a lamp post, attached to an unmanned aerial vehicle or, now, integrated into the eyewear of a stranger.”. [9]
ii. Racial/ethnic bias
Recent research suggests that the algorithms behind facial-recognition technology may suffer from a racial or ethnic bias: many algorithms expose differences in accuracy across race, gender and other demographics [10].

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