2. Data and AI

Data and AI


In the previous module, you learned that AI systems whose models are trained with machine learning algorithms need data to learn. One of the main reasons for the success of machine learning models today is Big Data. Many modern ML algorithms achieve higher accuracy and robustness when trained on large and diverse datasets.

A brightly coloured office populated with all kinds of people working at connected desks.
Jamillah Knowles & Digit / https://betterimagesofai.org / https://creativecommons.org/licenses/by/4.0/
 
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Before we dive deeper into data in relation to AI, please reflect on the following questions:

  • Technical facts: Where do the data for training AI models come from? Why do AI models need data?
  • Social and ethical questions: What social and ethical implications could arise from the data collection mechanisms used to train AI models?
  • Affected target group: Who is primarily affected by these social and ethical implications? And what are the consequences for these individuals?
  • Risk mitigation strategies: What strategies exist to mitigate these social and ethical impacts that could result from the data collection mechanisms for training AI models?