Read the following passage carefully:
"The rapid integration of automated decision-making systems into public administration promises unprecedented efficiency in civil service delivery and resource allocation. Advocates contend that data-driven algorithmic models can eliminate bureaucratic inertia, minimize human discretion, and optimize welfare distribution with objective precision. However, an uncritical adoption of these digital tools threatens to entrench systemic inequalities by codifying historical biases latent within training datasets under the guise of technological neutrality. Moreover, the inherent opacity of complex predictive algorithms—often termed the 'black box' problem—severely undermines the constitutional principle of procedural fairness, which guarantees citizens the right to reasoned administrative decisions. While recent policy frameworks emphasize algorithmic audits and human oversight as safeguards, such measures frequently degenerate into routine administrative formalities rather than meaningful checks on executive authority. Consequently, ensuring genuine accountability in digital governance demands more than technical refinements or nominal oversight; it necessitates a critical re-evaluation of the limits of automated delegation where public rights and discretionary governance intersect."
Which of the following statements best reflects the central theme of the passage?
- AAlgorithmic decision-making systems must be completely prohibited in public governance because training datasets invariably reflect institutionalized corruption.
- BPublic welfare distribution is most effectively optimized by replacing human discretion with decentralized blockchain architectures to ensure administrative neutrality.
- Achieving authentic accountability in digital administration requires questioning the boundaries of automated delegation, as existing procedural safeguards fail to protect fairness.Answer
- DRecent legislative frameworks establishing mandatory algorithmic audits have successfully neutralized the threats of dataset bias and administrative opacity.