A lot of systems integrated into one leave open the possibility for vulnerabilities. Before algorithms can be applied to sensor or text data, ensuring their quality meets the same standards as when the algorithm was developed is prudent. If the data is not on par it can result in:
higher false results
low trust of the inspection and
increased workloads overall
There is a way to mitigate these issues: apply a pre-algorithm data quality check.
In a fast paced operational workflow simultaneous activities should not be hindered by bad data. With continuous data monitoring implementing algorithms into your inspection process will:
Decrease probability of a biased classification
Determine algorithm performance issues
Identify drifts in algorithm performance
As inspection algorithms become mainstream, assessing the current state of sensor hardware and data systems is paramount. Is leaving your inspection operation at the mercy of unchecked AI good enough to carry your program into the future?
If the answer is no, you must consider the opportunity to evaluate the data before an algorithm is applied. Implement a data quality check into the workflow that will ensure your inspections are performing at the highest levels.
Take a look at the S2U website’s special feature, Global Smuggling Trends Report . This interacti...
In the world of artificial intelligence, data is the most valuable asset.
Algorithms that build upon a weak data foundation can lead to subtle vulnerabilities in the screening process.