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Answer :
The statement that AI systems can become biased is true, because biases present in training data can lead to unfair outcomes. Bias mitigation techniques are thus critical to ensure fairness and equity, especially since AI's impact extends to important areas such as education, employment, and justice. The statement is true.
The statement that AI systems can be biased if the data used to train them is biased, and that bias mitigation techniques are essential to ensure that AI algorithms are fair and equitable, is true. AI algorithms can inadvertently perpetuate and amplify biases present in the data they are trained on, leading to unfair outcomes. Bias mitigation is therefore crucial in the development of AI systems, to reduce the risk of algorithmic bias and ensure that the technology operates fairly and equitably across diverse populations.
This issue is increasingly important as AI technologies, such as ChatGPT, Bard, and Dall-E, become more widespread in research and other high-stakes domains. Their impact can be profound, influencing not only the fairness of AI-driven decisions but also the validity of information presented to users. The presence of biased algorithms in fields like education, hiring, and justice can exacerbate existing social inequalities and potentially harm already vulnerable and disempowered groups.
The implications of biased AI are substantial in the real world, affecting everything from personal data use to academic and professional opportunities. It is important to have human oversight where serious implications are involved, as humans can provide a complementary perspective to AI, despite their own cultural and environmental biases. Working towards transparent AI and addressing both technical and human aspects of bias, as pointed out by Cennydd Bowles in "Future Ethics", will be critical in mitigating bias and achieving equitable outcomes. The statement is true.
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