Algorithms and Bias (33 pts total) (24 pts total)Types of bi…

Algоrithms аnd Biаs (33 pts tоtаl) (24 pts tоtal)Types of bias: reporting bias, automation bias, coverage bias, non-response bias, sampling bias, in-group bias, out-group homogeneity bias, confirmation bias, experimenter's bias We have discussed many ways in which datasets can exhibit bias. Below are four scenarios involving the collection of data. For each, identify a type of bias that may be applicable (2 pts each), and justify how the approach is likely to be biased in that way (4 pts each). (Multiple types of bias may be present in any given scenario - choose one).   1a. You want to build an automated system to help you select quality applicants for college admission. You train your model using a historical dataset from your university that contains applicant profiles for all those who were accepted since the founding year. 1b. You want to understand how students feel about CS 3001, so you ask everyone who attends class in-person to complete a small survey before they leave. 1c. You need to conduct a survey about attitudes toward home cooking for a class. You ask all of your friends and roommates about their attitudes toward home cooking to gather your sample data. 1d. You are hiring applicants for an advanced software engineering role. One of the deciding criteria you look for on their resume is whether or not they have a college degree in Computer Science or a related field.   (9 pts total)The justice system is seeing growth in the use of artificial intelligence. 2a. Identify one specific application of AI in criminal justice. (3 pts)2b. Describe a potential consequence when biased datasets are used for the application you identified in 2a. (6 pts)