The phrase "auditing algorithm" is correct and usable in written English.
You can use it when discussing a specific algorithm designed to evaluate or verify processes, data, or systems, particularly in the context of compliance or quality assurance.
Example: "The auditing algorithm was implemented to ensure that all financial transactions were accurately recorded and compliant with regulations."
Alternatives: "verification algorithm" or "compliance algorithm".
To address this, auditing algorithm is devised (Section 3.1.2).
If the measured RSSI is above the RSSI threshold value, then auditing algorithm assigns the malicious level as high.
When the maximum PDR of a CM is lower than the PDR threshold (Section 2.5.2), then auditing algorithm measures the RSSI of the corresponding CM.
The auditing algorithm used in the auditing step for detecting the presence of jamming and classifying the type of jamming is given below: In the auditing algorithm, evaluation of PDR, maximum PDR, maliciousness level, and jamming identification/jamming classification are run concurrently in order to detect the presence of jamming and classify them.
Auditing algorithm is implemented in CH.
In addition to jamming detection, the auditing algorithm also performs classification of jamming.
"What we'd like to see is a trusted third party, perhaps a regulatory or supervisory body, that would have the power to scrutinise and audit algorithms, so they could go in and see whether the system is actually transparent and fair," said Wachter.
We're also working on our own tool as a regulator on how we are going to audit algorithms," she added.
Establish an AI regulatory function working alongside the Information Commissioner's Office and Centre for Data Ethics – to audit algorithms, investigate complaints by individuals, issue notices and fines for breaches of GDPR and equality and human rights law, spread best practice and ensure algorithms must be fully explained to users and open to public scrutiny.
Giving a view from outside academia, author and data science consultant Cathy O'Neil, who has written a book on how big data can increase inequality, argues that the most pressing challenge is not so much how to audit algorithms but how to get technologists to agree algorithms need to be audited.
Table 4 displays the effects of our audit algorithm on code flag status and code-use counts.
