Question

Which of the following is an explainability risk of self-learning models?

1) Self-learning models are black boxes whose decisions cannot be understood.
2) Self-learning models are unable to generate decisions as output.
3) Self-learning models may introduce new data you're unaware of.
4) Self-learning models can corrupt data or otherwise make it unreadable.

Answer 1

Self-learning models, or AI, can be 'black boxes' with decision-making processes that are hard to interpret, possibly containing biases, leading to a risk of lack of explainability and transparency.

Step-by-step explanation:

The explainability risk of self-learning models lies in their nature as potential black boxes whose decision-making processes cannot be easily understood or deciphered by human operators. While self-learning models, such as certain types of artificial intelligence (AI), provide the advantage of quick and sometimes accurate predictions, they also suffer from the disadvantage of possibly making erroneous predictions due to the current limitations in our understanding of their complex mechanisms. Moreover, these models can incorporate algorithms with biases, potentially perpetuating existing biases if left unchecked. Therefore, one major risk associated with self-learning models is their lack of explainability, which can hinder transparency and control over AI-driven decisions.