AI-Assisted Decisions and Evidentiary Risk
At a Glance
AI-assisted systems are increasingly used in consequential decision-making contexts, including finance, healthcare, employment, procurement and public administration.
While such systems may improve speed or analytical capacity, they also amplify evidentiary risk where decisions must later be explained or defended.
This page explains why evidentiary risk increases in AI-assisted environments and how decision provenance addresses that risk.
What Is Evidentiary Risk?
Evidentiary risk refers to the risk that a decision cannot be adequately explained, justified or defended when later examined.
This risk arises not only from wrongdoing, but from inadequate preservation of the decision context, assumptions and human judgement present at the time the decision was made.
Why AI-Assisted Decisions Increase Evidentiary Complexity
AI-assisted systems introduce additional layers between input and outcome.
- model outputs may be probabilistic rather than deterministic
- training data and model parameters may not be fully transparent
- human discretion may be shaped by system recommendations
- interfaces may obscure underlying assumptions
Even where a human remains formally responsible for the decision, the evidentiary chain becomes more complex.
The Problem of Retrospective Explanation
When AI-assisted decisions are later reviewed, organisations often attempt to reconstruct how the system contributed to the outcome.
This reconstruction may rely on technical logs, model documentation, email exchanges or personal recollection.
As explained in Why Decision Reconstruction Fails , retrospective explanation is vulnerable to missing context and hindsight bias.
AI systems amplify these weaknesses because system behaviour may change over time and intermediate reasoning steps may not be preserved.
Human Judgement in AI-Assisted Contexts
In many regulated settings, a human decision-maker remains accountable even where AI tools are used.
However, the interaction between human judgement and system output is rarely preserved in a structured manner.
Without contemporaneous preservation, later reviewers may struggle to determine:
- what the system recommended
- what the human decision-maker considered
- what constraints applied at the time
- how discretion was exercised
Decision Provenance in AI-Assisted Systems
Decision provenance preserves decision context, judgement and outcome at the time a decision is made.
In AI-assisted environments, this may include preserving:
- the system output presented to the human decision-maker
- the material context considered
- documented constraints or policies
- the human’s recorded judgement
By preserving this information contemporaneously, reliance on later reconstruction is reduced.
Clarification
Decision provenance is not itself an AI governance regime, certification framework or regulatory mandate.
It is a conceptual approach to evidentiary preservation that may support accountability in both human-only and AI-assisted decision-making contexts.