Client Name

HBL

Faculty Advisor

Dr. Hassan Mahmood

SBS Thought Leadership Areas

Behavioural Studies

SBS Thought Leadership Area Justification

Our Experiential Learning Project (ELP), "Designing a Scalable AI-Enabled Inclusion Framework for HBL," aligns directly with the Behavioral Studies thought-leadership area of the IBA School of Business Studies by re-engineering human decision-making and organizational processes across the employee lifecycle. Rather than focusing on passive diversity training, our framework applies "fairness by design," a core behavioral concept that modifies structural environments to mitigate implicit biases during critical talent decisions such as recruitment, evaluation, and promotion. By understanding that human recruiters are naturally susceptible to intuitive, fast "System 1" cognitive shortcuts, the project establishes algorithmic and process-driven interventions that systematically steer selectors toward objective, analytical decision-making.

A concrete example of this behavioral alignment is our framework's integration of AI-driven resume masking paired with structured evaluation rubrics and locked interview question banks. Standard recruitment methods frequently suffer from affinity and gender-role biases, where demographic indicators like names, photos, or geographical backgrounds on a CV unconsciously skew a recruiter's evaluation before an interview even begins. To neutralize these cognitive distortions, our framework employs technology to automatically strip identifying demographic markers, forcing an objective assessment of skills. Because a candidate's identity naturally resurfaces during an interview, masking alone is insufficient, so the framework mandates the use of predetermined, non-intrusive questions that focus solely on key professional competencies like long-term career ambition and reliability. This prevents interviewer discretion from introducing subjective criteria that pry into personal or marital status. By utilizing technology to "nudge" corporate actors into fairer, more data-driven choices, our project moves workplace equity from an aspirational philosophy into an engineered, auditable behavioral reality within Habib Bank Limited.

Aligned SDGs

GOAL 10: Reduced Inequality

Aligned SDGs Justification

Our work directly fulfils SDG 10 (Reduced Inequalities, Target 10.3) by eliminating discriminatory practices, restricting arbitrary human discretion, and creating transparent institutional baselines that ensure equal entry opportunities for diverse, underrepresented talent pools. Because masking only defers bias until the face-to-face interaction, the framework maintains structural continuity during interviews by utilising locked competency rubrics and non-intrusive, predetermined question banks that focus on professional capacity rather than probing into personal, marital, or socioeconomic statuses.

NDA

No

Abstract

In the context of reducing gender-based bias in the end-to-end employee lifecycle, from hiring to evaluation, promotion and retention, the goal of this Experiential Learning Project is to create a scalable Artificial Intelligence (AI) based inclusion roadmap for Habib Bank Limited (HBL), the largest commercial bank in Pakistan. Although through its International Finance Corporation (IFC) association and family-support programmes, HBL has increased the representation of women from 3% to 22%, they are still having to deal with the disproportionate attrition of women from the ranks of junior management. The project utilizes a four-phase qualitative approach, including industry benchmarking of the best practice of top global banks (HSBC, Barclays, JPMorgan Chase) and their regulators when applying AI in diversity, equity and inclusion (DEI); a baseline process risk mapping tool and a structured evaluation of nine AI tools use in hiring-to-promotion; development of a practical, locally informed framework for differentiating hiring bias from lack of retention, which embeds fairness control points at every AI use decision; and an AI-fairness governance model with a phased 12-month implementation road map. A central finding is that while AI can effectively address resume masking and structured interview scoring, these strategies can only have a material impact on reducing hiring bias, whereas retaining employees is shaped by organisational culture, flexibility, mentorship and clear progression, none of which is achievable through just technology. By bringing in feedback from their client, the report reinforces the business case by adding illustrative quantitative examples including expected reduction in cost-per-hire, time-to-hire and women percent of attrition, and modelling on how to reach the target of 30% of females, to name a few, whilst explaining how they will be implemented, from employee adoption to data quality and privacy, to the ongoing resourcing needed to keep it going. Recommendations are purposefully intended to build on, not replicate, the current investments of HBL, such as its women's returnship initiative, as well as its HR analytics dashboard. The framework provides HBL with a defensible roadmap to drive its endeavours to make its responsible, sustainable, and auditable use of AI technologies in talent management visibly demonstrate measurable gender equity.

Document Type

Restricted Access

Document Name for Citation

Experiential Learning Project

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