In which of the following scenarios would implementing a machine learning algorithm for anomaly detection raise data privacy concerns?

The correct answer is C. Evaluating employee behavior to identify potential fraud. Using machine learning to evaluate employee behavior for fraud detection raises significant data privacy concerns because it involves continuous monitoring, profiling, and behavioral analysis of individuals in the workplace. This can constitute covert surveillance, may violate em

Data Life Cycle

Question

Options

AEstablishing benchmarks to identify outliers
BDetermining employee email spam classification
CEvaluating employee behavior to identify potential fraud
DAccessing personal information in audits

How the community answered

(40 responses)

A
8% (3)
B
5% (2)
C
75% (30)
D
13% (5)

Explanation

Using machine learning to evaluate employee behavior for fraud detection raises significant data privacy concerns because it involves continuous monitoring, profiling, and behavioral analysis of individuals in the workplace. This can constitute covert surveillance, may violate employees' reasonable expectation of privacy, and risks discriminatory or inaccurate profiling with serious consequences to employment. Establishing benchmarks for outliers (A) is a technical, data-agnostic activity. Email spam classification (B) analyzes content for filtering purposes and is a standard, low-privacy-impact use case. Accessing personal information in audits (D) is a controlled, purposeful, and typically disclosed process with clear legal justification, unlike continuous behavioral monitoring.

Topics

#Machine Learning#Anomaly Detection#Employee Monitoring#Privacy Concerns

Community Discussion

No community discussion yet for this question.

Full CDPSE Practice