Week7Algorithms and Decision MakingNelsonDanietta (1)

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1 Algorithms and Decision Making Algorithms and Decision Making Danietta Nelson Walden University Professor Amy Puderbaugh Human Resource Analytics MHRM-6401 Date: February 28, 2021
2 Algorithms and Decision Making Introduce the topic of algorithms in the selection process. How might the recommendations an algorithm makes differ from those of a hiring manager who is not using data analytics? In the ever growing controversial landscape of the hiring practice, one of the most hotly debated and lawfully challenging aspects comes from the information that is gathered in the selection process. This information often contains heavily biased and over prejudiced assertions. How this information is even obtained? One manner is through algorithms or rather hiring algorithms. Hiring algorithms are used quite a lot, throughout the entire employment route, despite what many may think. They are found on job forums, recruiting websites, or tools that evaluate the resumes of potential employees. But what exactly are these hiring algorithms? They are a set of rules and procedures produced by technology to aid in hiring the most suited candidates. On paper, it sounds like a great idea. On the other hand, often these hiring algorithms are heavily riddled with preconceived notions and discrimination under the guise of something else. The difference between an algorithm’s recommendation and hiring manager who lacks data analytics can be quite broad. For example, a candidate may have all of the workings that are required for the job but if the name and the level of education don’t fit the criteria that the algorithm desires, it most likely will skip over the candidate whereas the hiring manger would take more into consideration. While algorithms can possibly weed out the best candidates when utilizing a thinking process, they can be limited by certain standards of functionality accorded to them, especially when it comes to predilection.(Bogen 2019)
3 Algorithms and Decision Making How might using algorithms to analyze customers differ from using them on employees? Should companies be more cautious in implementing these methodologies internally? The difference algorithms use between the analysis of customers and employees may lie in the needs that are presented in the customers and which employees fit the specifications of those needs. Customers may desire service that is precise, well-documented, and easy to handle. If so, algorithms may take these conditions and through analyzing the pool of employees in its grasp, decide which employee better compliments the desires and needs of the customers so that a higher customer satisfaction level is gained. Companies however should be very cautious in implementing such methods and procedures because whether they like it or not biases, inclinations, and other mindsets can easily tip the balance of the algorithm. After all, algorithms aren’t humans. While they have a better grasp at being able to “think” of the best solutions and are able to boost the quality of hiring candidates, they’re programmed and maintained by humans, who can easily allow their own preconceived notions and bigoted practices to rife its programming. Describe a situation where you would base a decision on data analysis.  A situation that I would base a decision on data analysis is at my current job because we assist individuals with tuition assistance, job placement and varies others things with the hopes of them gaining successful employment after completing the program. So it is very important that we keep track of each clients progress throughout the program. When keeping the track of the cases and making successful closures the data is used to determines the amount of funding that will be awarded for each physical year. Data analysis is very important because if information is not accurate it could cause our funding to be cut.
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4 Algorithms and Decision Making Should Aliyah Jones choose Molly or Ed? Analyze each alternative solution. Consider the short-term and long-term implications. What are the advantages and disadvantages of each decision? Support your decision with two additional scholarly articles. After reading the case study the decision on who Aliyah should choose for the marketing director position is a very hard decision to make. Ed is very well qualified for the position and also he has researched the position and was able to ask questions about what the job duties consist of. Ed also had experience collaborating with other departments throughout the organization which is something that Aliyah eventually wanted to do. Although he knows a lot about the position some of his answers seemed to be a little unsure which was a red flag to Aliyah still leaving her not wanting to hire him. Then you have Molly who is experienced in the particular department in which the marketing director position is needed. Molly is very well knowledgeable about the ins and outs of the position. When interviewing Molly she is able to answer the questions with no hesitation as well as offer some ideas about collaborating with other departments. The problem is that Aliyah and Molly are friends on a business and personal level which is clouding Aliyah sound judgement because she feels that Molly has aced the interview and had her mind set before conducting any interview that she already mad up her mind that she wanted Molly in the position. In conclusion, Aliyah should keep her decision based on the hiring criteria that is for this current position. Also, using sound judgment or having a hiring committee could keep the selection from being biases and will yield the best possible applicant for the marketing director job. Reference
5 Algorithms and Decision Making Polzer, J.T. (2018).Trust the algorithm or your gut?(HBR case study).Harvard Business Review. McPhail, R., Herington, C., & Guiding, C. (2008).Human resource managers perceptions of the applications and merit of the balanced scorecard in hotels. International Journal of Hospitality Management, 27(4), 623-631. Bogen, M.(2019).All the ways Hiring Algorithms Can Introduce Bias.htt://hbr.org

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