MBA $6620$ FOURTH ASSIGNMENT Management of Risks associated with Asymmetric Information Learning Outcomes $1.$ Able to uncover the presence of asymmetric information. $2.$ Able to analyze the impact of asymmetric information on behaviors and actions. $3.$ Able to assess management alternative strategies$/$measures to reduce risks associated with asymmetric. In this assignment, you are given two mini$-$cases $($see below$).$

Each case describes business decisions grappling with the consequences and risks of asymmetric information. Questions In responding to the following question, please ensure that you demonstrate the learning objectives stated above. $1.$ Explore the characteristics of asymmetric information in each case $($moral hazard or adverse selection$).$ You must present the reasoning for $2.$ Investigate which partys$)$ has superior information and the one$($s$)$ with less. $3.$ Analyze the business risks arising from the presence of asymmetric information. $4.$ Based on your readings, present strategies for mitigating the presence and risks arising from asymmetric information. CASE $1$: Use of Artificial Intelligence $($AI$)$ in Recruitment The hiring$/$recruitment process can be especially tedious and long. The process involves several steps, starting with business needs analysis, writing clear and sufficiently detailed job descriptions accurately reflecting needs, posting them on digital job boards, waiting for candidates to respond, sorting qualified applicants, scheduling and conducting interviews, decision$-$making, and onboarding the new candidate. Algorithm rules$-$based decision processes are regularly used to speed up the process and simultaneously make the best hiring decisions. With the advent of AI$,$ the participation of these capabilities is finding its way into hiring decisions. Based on survey data in Statista $($Jobvite $2023$ AI capabilities are deployed in several phases of the recruitment process. AI systems can become effective partners in developing job descriptions, executing outreach strategies to reach candidates, using generative chatbots for communicating with candidates, shortlisting top candidates, and potentially making hiring recommendations. Additionally, AI can generate interview questions and detect positive and negative visual behavior cues during video interviews. The benefits are clear $-$ AI tools relieve recruiters from performing administrative tasks and offer intelligent assistance at various stages of the hiring process. However, there is ongoing debate on how well AI can help the business achieve the values of being an equal opportunity employer $($nondiscriminatory about race, color, religion, sex, national origin, age, disability, or genetic information$).$ AI models are trained on historical employment and demographic data, and behavioral psychology. AI systems can also learn from the data to use personal candidate information such as a persons name, location, and other social activities in their predictive models, models that predict the likelihood of the candidates success on the job. Would a candidate with an odd $/$ethnic name face a lower likelihood of being short$-$listed? Or$,$ would an unusual hobby or activity receive a higher or lower predictive score? Would the historical patterns and biases effectually reduce the pool of job applicants or turn down excellent applicants? The presence of asymmetric information in the hiring process, compounded by human biases and embedded biases in AI learning historical data sets is well documented. Truly, human biases also exist in traditional recruitment processes where the parties are the hiring manager and the candidate. With the advent of rule$-$based hiring and now AI$,$ there are three parties in the hiring process $-$ the hiring manager, the candidate, and the AI system. It is unconventional to view AI as a party to the hiring transaction. However, given its heightened roles in sorting, selecting, advising, and predicting systems regarding top candidates, AI is figuratively transformed into an active participant in decision making. As an illustration, according to Pew Research, the demographics of the engineering workforce are predominately white $(67\%)$ followed by Asians $(15\%).($Kennedy $2021).$ The dominance of white and more recently Asian is now part of the historical data system used to train AI systems. Consider the likelihood of a non$-$Asian minority engineering applicant with good qualifications and an unusual name being shortlisted within a large pool of applicants for an engineering position.