Question

Read the following passage carefully.

In the evolving landscape of digital privacy law, the Federal Trade Commission’s (FTC) relatively recent deployment of 'algorithmic disgorgement'—the mandated deletion of machine learning models trained on improperly harvested user data—has provoked fierce debate. Traditional privacy advocates have championed an absolutist stance, demanding the blanket deletion of any algorithm touched by illicit data. While their underlying concern for user autonomy is highly understandable and morally resonant, such a draconian remedy ignores the indivisible nature of modern neural networks, making it functionally impossible to excise specific data without destroying entirely legitimate, parallel developmental frameworks.

Conversely, tech industry lobbyists argue that algorithmic disgorgement is fundamentally anti-innovation, proposing instead that regulatory bodies rely exclusively on heavy financial penalties. This position, however, entirely fails to grasp the economic realities of the data broker ecosystem. For massive tech conglomerates, fines are merely the cost of doing business; allowing them to retain the infinitely replicable algorithmic insights derived from stolen data essentially permits them to purchase illicit competitive advantages, rendering financial penalties utterly toothless as a deterrent.

Caught between these extremes, the FTC’s current utilization of algorithmic disgorgement represents a highly necessary evolution in regulatory enforcement. By forcing companies to relinquish the actual fruits of their data harvesting, the agency targets the core incentive of data misappropriation. Yet, the agency’s approach is not without its vulnerabilities. Currently, the FTC exercises this power primarily through closed-door settlements rather than litigated precedents. Until algorithmic disgorgement is rigorously tested and contoured within the appellate courts, its boundaries remain troublingly amorphous.

Some legal scholars have attempted to clarify these boundaries by invoking the 'fruit of the poisonous tree' doctrine, a well-established criminal law principle dictating that evidence derived from an illegal search is inadmissible. As a conceptual framework for data privacy, this analogy is elegant and logically compelling. However, transferring a doctrine designed for linear chains of physical evidence to the black-box environment of deep learning—where millions of data points are synthesized into abstract weights and biases in ways even their creators cannot fully trace—presents nearly insurmountable evidentiary hurdles. Thus, while the doctrine provides a useful philosophical touchstone, it ultimately falters as a functional blueprint for courtroom litigation.

Based on the passage provided, match each of the following concepts or viewpoints with the description that most accurately reflects the author’s attitude toward it.