Most FBR Officers Fail Surprise Competency Test

The Federal Board of Revenue’s (FBR) unexpected competency assessments for Inland Revenue officers have drawn criticism following a report highlighting that only 15 out of 150 officers evaluated in sales tax and 14 out of 143 in income tax demonstrated competence in independently adjudicating cases. This translates to approximately one in ten officers in each tax category.

The assessments, conducted in Islamabad and Karachi, will next take place in Lahore on Tuesday, November 17. Legal and Methodological Concerns The Karachi capability assessment report has sparked concerns regarding the legality, transparency, and methodology of the exercise. Skeptics question whether a single test based on fabricated case studies can accurately gauge the competence of officers who were recruited through the Combined Services Competitive (CSS) examination.

Doubts have also been raised about the legal authority under the Civil Servants Act of 1973, as well as service rules governing such tests and their potential implications for restricting officers’ statutory functions and postings. It is worth noting that the assessment itself explicitly states that its findings are based on a single test of three cases in each tax, and should thus be viewed as indicative only.

Artificial Intelligence and Disseminated Scores The reported use of artificial intelligence, notably Claude AI, to generate case studies and assess answer scripts has also come under scrutiny, although the exact extent of AI involvement remains unverified and awaits official clarification. The public disclosure of individual officers’ names, scores, and classifications has raised concerns over confidentiality and morale.

Some fear the results could be exploited to construct a narrative of widespread incompetence as justification for sweeping reforms under the FBR’s transformation plan, potentially benefiting outside consultants. Calls for Transparency Growing calls are now being made for an independent review of the test framework, disclosure of its legal foundation, AI usage, and marking criteria, as well as a fair review process for officers affected by the outcomes.