Using Explainable AI in the Production of Biological Age Measures

Standard approaches to generating aging clocks from biological data produce algorithmic combinations of factors that are opaque. It is entirely unclear as to how they relate to underlying mechanisms of damage and dysfunction that produce degenerative aging, and thus hard to use them as a tool to assess ways to modify those mechanisms. Explainable artificial intelligence is a term of art used to describe approaches to machine learning that produce more insight into how the final product actually works, what factors went into its construction, how it relates to underlying processes. Given that the primary challenge in the field of measuring biological age, such as via epigenetic clocks, is that we don't understand how these clocks relate to specific causes and processes of aging, it seems sensible to make more of an effort to produce aging clocks that are comprehensible from the outset. The work here is a step in that direction. Existing biological age clocks have three main limitations. First, they necessitate a trade-off between accuracy (ie, predictive performance for chronological age or mortality) and interpretability (ie, understanding each feature's contribution to the prediction). Most of them use linear models that offer interpretability but weaker predictive power for mortality prediction than complex machine-learning models. This choice is natural given that interpretability is a key goal of biological age clocks: identifying biomarkers of biological age can...
Source: Fight Aging! - Category: Research Authors: Tags: Daily News Source Type: blogs