Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, accomplishing trustable and fair machine learning has been challenging. Maintaining data fairness and privacy is one of the top challenges faced by the industry as organizations employ various machine learning algorithms to automatically make decisions based on trends from previously collected data. Protected group or attribute refers to the group of individuals towards whom the system has some preconceived reservations and hence is discriminatory. Discrimination is the unjustified treatment towards a particular category of people based on their race, age, gender, religion, sexual orientation, or disability. If we use the data with preconceived reservation or inbuilt discrimination towards certain group, then the model trained on such data will also be discriminatory towards these specific individuals.
","abstract_html":"<p>Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, accomplishing trustable and fair machine learning has been challenging. Maintaining data fairness and privacy is one of the top challenges faced by the industry as organizations employ various machine learning algorithms to automatically make decisions based on trends from previously collected data. Protected group or attribute refers to the group of individuals towards whom the system has some preconceived reservations and hence is discriminatory. Discrimination is the unjustified treatment towards a particular category of people based on their race, age, gender, religion, sexual orientation, or disability. If we use the data with preconceived reservation or inbuilt discrimination towards certain group, then the model trained on such data will also be discriminatory towards these specific individuals.</p>","abstract_has_math":false,"creators":["Bhargava, Neha"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Ramazan Aygun","Dr. Yong Pei","Dr. Jiho Noh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-13T08:00:00Z","date_published":"2022-12-13T08:00:00Z","updated_at":"2026-07-24T02:43:58Z","subjects":["Big Data","GAN","Fairness","Adult","COMPAS","German Credit","Discrimination","Computer Sciences","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/54","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Ramazan Aygun","Dr. Yong Pei","Dr. Jiho Noh"]},{"key":"dc:creator","label":"Author","values":["Bhargava, Neha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-12-13T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Big Data","GAN","Fairness","Adult","COMPAS","German Credit","Discrimination","Computer Sciences","Data Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/54"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, accomplishing trustable and fair machine learning has been challenging. Maintaining data fairness and privacy is one of the top challenges faced by the industry as organizations employ various machine learning algorithms to automatically make decisions based on trends from previously collected data. Protected group or attribute refers to the group of individuals towards whom the system has some preconceived reservations and hence is discriminatory. Discrimination is the unjustified treatment towards a particular category of people based on their race, age, gender, religion, sexual orientation, or disability. If we use the data with preconceived reservation or inbuilt discrimination towards certain group, then the model trained on such data will also be discriminatory towards these specific individuals.
"]},{"key":"dc:title","label":"Title","values":["Fairness and Privacy in Machine Learning Algorithms"]}]}],"canonical_facts":{"dc:contributor":["Dr. Ramazan Aygun","Dr. Yong Pei","Dr. Jiho Noh"],"dc:creator":["Bhargava, Neha"],"dc:date.available":["2022-12-13T08:00:00Z"],"dc:description.abstract":["Roughly 2.5 quintillion bytes of data is generated daily in this digital era. Manual processing of such huge amounts of data to extract useful information is nearly impossible but with the widespread use of machine learning algorithms and their ability to process enormous data in a fast, cost-effective, and scalable way has proven to be a preferred choice to glean useful insights and solve business problems in many domains. With this widespread use of machine learning algorithms there has always been concerns about the ethical issues that may arise from the use of this modern technology. While achieving high accuracies, accomplishing trustable and fair machine learning has been challenging. Maintaining data fairness and privacy is one of the top challenges faced by the industry as organizations employ various machine learning algorithms to automatically make decisions based on trends from previously collected data. Protected group or attribute refers to the group of individuals towards whom the system has some preconceived reservations and hence is discriminatory. Discrimination is the unjustified treatment towards a particular category of people based on their race, age, gender, religion, sexual orientation, or disability. If we use the data with preconceived reservation or inbuilt discrimination towards certain group, then the model trained on such data will also be discriminatory towards these specific individuals.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/54"],"dc:subject":["Big Data","GAN","Fairness","Adult","COMPAS","German Credit","Discrimination","Computer Sciences","Data Science"],"dc:title":["Fairness and Privacy in Machine Learning Algorithms"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:43:58Z"}