Master of Science in Information Systems

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https://mcstor.library.milligan.edu/handle/11558/4202

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Browsing Master of Science in Information Systems by Subject "Machine learning"

Now showing 1 - 3 of 3
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    Not Neutral: A Look at Bias in Artificial Intelligence Algorithms that Are Assumed to Be Objective
    (2020-11-18) Warfield, Bradley Wayne Akins; Carter, Teresa
    The world is becoming more digital, and data is ubiquitous. Algorithms are needed to help deal with all of that data. But neither the data, nor the algorithms, nor the use of outputs is neutral. They often reflect the biases of the humans involved in collecting, analyzing, and interpreting the data. Sometimes biased algorithms serve to amplify human bias. Biased algorithms cause people to spend more time in jail. Algorithms and data that are intended to help racial minorities sometimes instead hurt them by limiting their voices. Data and algorithms have great potential for protecting civil rights, but there is also enormous potential harm to civil rights that data and algorithms may facilitate. Biased algorithms impact what is considered fact in society. The Internet cannot be navigated without a search engine. But search engines are focused on advertisers and profits, not knowledge or high-quality information. Biased search engines, especially Google Search, have radicalized domestic terrorists and perpetuated harmful stereotypes. Since every part of the process of choosing data, collecting data, writing an algorithm, and using the outputs can contain harmful bias, care and transparency are needed throughout the entire process. Data scientists and system designers may not be able to fully remove bias from algorithms, but they can be improved. Through education and the creation of a public non-profit search engine, society will be able to better take advantage of the huge amounts of data while increasing equality and reducing harm.
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    Setbacks in Clinical Laboratory Innovations
    (2021-10-25) Grindstaff, Lori; Carter, Teresa
    Technological advancements are soaring in healthcare as a whole; however, advancements in the clinical laboratory have fallen behind. Innovation in the clinical laboratory can be complicated and comes with high risks. Consequently, new technology and its acceptance have fallen short. The slow growth of artificial intelligence (AI), machine learning (ML), and deep learning (DL) keeps laboratory technicians working harder and healthcare costs soaring. This technological lag perpetuates analysis delays, unnecessary testing, human errors, suboptimal patient care, and in an untimely fashion. The goal of this research is to prove the significant need for innovations in laboratory medicine and establish a means to accomplish them. This paper will include analyses of articles, peer reviewed articles, websites, and journals that are no more than three years old to show how laboratory innovations can enhance turnaround times, lower healthcare costs, increase the level of patient care, and allow for greater patient outcomes.
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    Using Artificial Intelligence to Manage and Secure Computer Networks
    (2020-10-11) Schneider, James; Carter, Teresa
    This paper describes the necessity and benefits of using artificial intelligence to manage and secure computer networks in an information system. As the requirements and infrastructure of computer networks continue to increase in complexity, traditional, centrally managed network approaches are not going to be able to efficiently administer, optimize, and secure the networks of the future. The amount of data that is being collected and saved has increased by at least one order of magnitude. Network operators that collect and save this data could leverage artificial intelligence (AI) and machine learning (ML) to allow the networks to be proactive rather than reactive, thus resulting in better performance and reliability. Some companies have already started innovating by adding virtual network assistants that are powered and driven by AI capabilities such as natural language processing. This allows for quicker troubleshooting and other insights. AI has also been leveraged for energy-efficient routing, prediction of network disruptions, intrusion detection, and other security-related issues. However, there is still plenty of work to be done to ensure that the wireless networks of the future are efficient and secure as data transfer and communication increases between consumers and mobile devices and for the infrastructure that is going to power essential services in the community. Nevertheless, AI should not be considered a silver bullet. The proper application of AI will require human intelligence as baseline for computer network operations. All those in the AI industry should work together to ensure knowledge is shared to prevent any negative consequences of mismanaged AI.