Artificial intelligence and bias
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Artificial intelligence and bias
Artificial intelligence (AI) can be biased if the data used to train it is biased. Bias in AI can lead to unfair or discriminatory outcomes. For example, if an AI system is trained on data that is biased against a certain group of people, it may produce results that discriminate against that group.
One way to prevent bias in AI is to ensure that the training data used to create the AI model is diverse and representative of the population it is meant to serve. Additionally, AI developers can use techniques such as debiasing algorithms, which can help to remove or reduce bias in the data and the AI model.
However, it is important to recognize that bias in AI is a complex issue that cannot be entirely eliminated. Bias can be inherent in the data itself or can arise from the assumptions made by the developers who create the AI models. Therefore, it is crucial to continually monitor and evaluate AI systems to identify and address any instances of bias that may arise.
Artificial intelligence and bias
RUBRIC
Excellent Quality
95-100%
Introduction 45-41 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Literature Support
91-84 points
The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned.
Methodology
58-53 points
Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met.
Average Score
50-85%
40-38 points
More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided.
83-76 points
Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration.
52-49 points
Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met.
Poor Quality
0-45%
37-1 points
The background and/or significance are missing. No search history information is provided.
75-1 points
Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration.
48-1 points
There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met
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