Description
Code generated with AI tools may be offered as a plausible solution based on only (or not much more than) being functional, without consideration for the performance/efficiency and the underlying hardware the code is run on - that is, it can often generate code which is computationally naive.
As a consequence, such code and contributions can slow the tool/library in question and in turn cause it to be inefficient, badly scaling, or having bottlenecks, leading to the need for more power hence higher cost and GHG emissions.
Likelihood
High
Severity
Medium
Reach
Medium
Mitigations
- thoroughly review AI-generated code for performance aspects including suitability for the underlying hardware it will be run on (if known);
- measure, profile and optimise code continuously on intended hardware to validate performance.
Ownership
RSEs, RSE managers, team leads, project leads, institutions
Examples
No response
Related Risks
No response
Tags
Software Sustainability
Other Tags
No response
Description
Code generated with AI tools may be offered as a plausible solution based on only (or not much more than) being functional, without consideration for the performance/efficiency and the underlying hardware the code is run on - that is, it can often generate code which is computationally naive.
As a consequence, such code and contributions can slow the tool/library in question and in turn cause it to be inefficient, badly scaling, or having bottlenecks, leading to the need for more power hence higher cost and GHG emissions.
Likelihood
High
Severity
Medium
Reach
Medium
Mitigations
Ownership
RSEs, RSE managers, team leads, project leads, institutions
Examples
No response
Related Risks
No response
Tags
Software Sustainability
Other Tags
No response