Supervised/unsupervised AI (AI data quality related - biased training data)

Description

"As above, there are broadly two dimensions of technical failure modes: quality of data or input signal, and training performance. Due to a lack of transparency, it may be difficult to ascertain the type of technical failure that gives rise to a particular risk, and it is often a combination of several factors. Risks pertain- ing to AI failures are exacerbated by poor quality training data and imperfect training signals. Various measures can be implemented to improve the quality of the training data, and fine-tuning techniques can be used to disincentivize harmful model behavior."

From Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024), as extracted by the MIT AI Risk Repository (CC BY 4.0).