While numbers are often seen as objective and factual, the data used to train artificial intelligence (AI) models can introduce biases that may lead to discriminatory outcomes, such as racial profiling (Nicoletti, 2023). AI algorithms learn from the data they are fed, and if that data contains biases, the AI system can inadvertently perpetuate and amplify those biases in its decision-making processes.
To address this issue, it is essential to implement measures such as diverse and representative data collection, rigorous testing for bias, and continuous monitoring of AI systems in real-world applications. Additionally, incorporating ethical guidelines and diverse perspectives into the development and deployment of AI can help mitigate bias and promote fairness.
Increasing transparency in AI algorithms and decision-making processes is crucial to understanding and addressing bias. Collaboration among technologists, ethicists, policymakers, and affected communities is key to developing more equitable and inclusive AI systems. By actively evaluating and improving AI technologies to align with ethical standards and uphold human dignity, we can strive towards a future where AI does not perpetuate harmful biases and instead contributes to a more just society.
Reference
Nicoletti, L. (2023, June 13). AI Has Some Serious Racial Profiling Issues. Bloomberg.
Retrieved from https://www.bloomberg.com/graphics/2023-generative-ai-bias/
https://www.bloomberg.com/graphics/2023-generative-ai-bias/
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Brittney McCarey
Grand Canyon University (Active Student)
Aspiring Educator OEA/NEA
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