Abstract
We approach the issue of interpretability in artificial intelligence and law through the lens of evolutionary
theory. Evolution is understood as a form of blind or mindless ‘direct fitting’, an iterative process through
which a system and its environment are mutually constituted and aligned. The core case is natural
selection as described in biology but it is not the only one. Legal reasoning can be understood as a step
in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context.
Machine learning, insofar as it relies on error correction through backpropagation, is a version of the same
process. It may therefore have value for understanding the long-run dynamics of legal and social change.
This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in
the context of the individual or instant case depends upon the generative power of natural language to
extrapolate from existing precedents to novel fact situations. This type of prospective or forward-looking
reasoning is unlikely to be well captured by machine learning approaches.
theory. Evolution is understood as a form of blind or mindless ‘direct fitting’, an iterative process through
which a system and its environment are mutually constituted and aligned. The core case is natural
selection as described in biology but it is not the only one. Legal reasoning can be understood as a step
in the ‘direct fitting’ of law, through a cycle of variation, selection and retention, to its social context.
Machine learning, insofar as it relies on error correction through backpropagation, is a version of the same
process. It may therefore have value for understanding the long-run dynamics of legal and social change.
This is distinct, however, from any use it may have in predicting case outcomes. Legal interpretation in
the context of the individual or instant case depends upon the generative power of natural language to
extrapolate from existing precedents to novel fact situations. This type of prospective or forward-looking
reasoning is unlikely to be well captured by machine learning approaches.
| Original language | English |
|---|---|
| Journal | Journal of Cross-Disciplinary Research in Computational Law |
| Volume | 1 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 17 Mar 2022 |