Moving beyond AUC to proactive fairness frameworks is how we build integrity into these systems from the start. Performance metrics are just the beginning; the real work is auditing for the outcomes that actually affect people.
Exactly Abedalhady! I am fully aware that incorporating these metrics from the design phase can be perceived as an exhaustive, and sometimes tedious, process. However, experience shows that bypassing this initial technical analysis can lead to disastrous consequences, not only operationally but also in terms of ethics and institutional accountability. Assessing outcome fairness before deployment is not merely an option; it is a critical safeguard.
This is a vital perspective, Marcela! 💯
Moving beyond AUC to proactive fairness frameworks is how we build integrity into these systems from the start. Performance metrics are just the beginning; the real work is auditing for the outcomes that actually affect people.
Looking forward to the rest of the series!
This is such a complex topic and a career of the future if governments start to understand the importance of creating fair and ethical AI systems
Thanks Noemi! It means a lot coming from you!
People often optimize the metric instead of the system — which improves the numbers without improving the real outcome.
Exactly Abedalhady! I am fully aware that incorporating these metrics from the design phase can be perceived as an exhaustive, and sometimes tedious, process. However, experience shows that bypassing this initial technical analysis can lead to disastrous consequences, not only operationally but also in terms of ethics and institutional accountability. Assessing outcome fairness before deployment is not merely an option; it is a critical safeguard.