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1. BEFORE reading the Digital-interactive NY Times article, think about this question: 
Machine learning algorithms allow organizations to monitor, assess, and make decisions related to employee performance with greater accuracy and precision than ever before. What do you think organizations stand to gain or lose from implementing such tools to manage the performance of their employees?  Write about a page focusing on what these tradeoffs might include.   
2. Now, read the “the rise of the worker productivity scoreLinks to an external site."(https:
usiness/worker-productivity-tracking.html) article--note: the article is tracking your performance as you read!
3. After engaging with the interactive article, does anything about your perspective change? What sorts of feelings emerged for you as you read through the article and watched your score? Write about a page focusing on your reaction.
4. Reconsider and evaluate the nature of algorithmic-based performance management systems from one or two different philosophical lenses discussed earlier in the course. What would moral philosophers have to say about these technologies?
5. Finally, given what you've just learned about trust, goal-setting, and procedural justice in organizations, what might be some wise considerations, restrictions, or communication efforts for managers to deploy when using these kinds of technologies? Be specific and map back onto course content, please!  
Submission Details
6. All challenge papers should be approximately 4-5 double-spaced pages in length. Please cite all references consistently (format of your choosing, as long as it is consistent).
Answered 3 days After May 15, 2023


Deblina answered on May 19 2023
5 Votes
Last Name 2
Title: Impact of Machine Learning Algorithms
Table of Contents
Review & Analysis    3
References    7
Review & Analysis
Implementing machine learning algorithms for managing employee performance can offers several benefits to the organisation. These tools can provide accurate and precise data about employee performance that enables organisation to make informed decisions regarding the promotions and training opportunities. It is retrospective that providing training opportunities and targeting to support the employee performance can enhance the overall productivity. Additionally, these tools can help in identifying patterns and the aspects that may otherwise go unnoticed allowing the organisation to optimise the workforce allocation and make some strategic decisions (Choudhury et al.). However, the trade-off that needs to be considered when implementing this technology is a serious aspect. One of the most important potential concerns is the invasion of privacy. Machine learning algorithms often required access to vast amounts of employee data including personnel information and performance metrics. This raises questions about data security and the potential misuse of sensitive information.
Employees may feel uncomfortable knowing that their every action is being monitored and analysed leading to a decreased trust and moral within the organisation. Another trade-off is the potential for bios and discrimination. Machine learning algorithms are trend on historical data which may contain biases and reflect existing inequalities within the organisation. If these biases are not identified and co
ected the algorithms may take into unfair practices or amplifier existing disparities (Chang et al.). This can result in discrimination based on factors such as gender or age leading to a negative impact on diversity and inclusive aspects. Moreover, there is a risk ove
eliance over algorithms and the loss of human judgement. While machine learning algorithms can provide valuable insights, they should not replace human evaluation and feedback entirely. Human judgement and context are crucial in understanding the new answers of employee performance and considering factors that may not be captured by data alone. Relying entirely on algorithms may lead to reductionist approach that fails to account for individual circumstances and unique...

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