Why biased data prevents artificial intelligence from reaching its full potential
In short: Data scientist Mainak Mazumdar explores how poor-quality data leads to flawed AI decisions and predictions. He examines real-world examples from Shanghai to New York City to explain why ethical AI requires significant infrastructural resets.
The future economy is expected to be driven by algorithms and artificial intelligence rather than traditional factories and human labor. However, when these systems are trained on biased datasets, they produce incorrect outcomes.
To achieve ethical AI, Mazumdar suggests that we must implement three specific infrastructural resets to address the issues caused by less-than-quality data.
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Sources
- Summary by Intellect · source: Mainak Mazumdar: How bad data keeps us from good AI
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