arXiv subjects

Muhammad Salar Khan

Publications and source records attributed to Muhammad Salar Khan.

5 recordsLinked to original sources

arXiv2026

When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice

Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias remains underexamined. This study provides systematic mixed-methods evidence of such bias across three LLMs (ChatGPT, Gemini, and Grok) using 432 simulated advisor-client interactions spanning 16 religious identity pairings (Christian, Muslim, Hindu, and non-religious) and three core household financial decisions: stock investment, house purchase, and life insurance. Combining regression and reflexive thematic analyses, we identify structural biases across models and decision contexts and the discursive mechanisms through which they are linguistically enacted. Unbiased advice appeared in only 12-18% of cases. Gemini consistently produced more bias than Grok, while ChatGPT's outputs were statistically comparable to Grok's. Religiously symmetric advisor-client pairings almost always triggered explicit religious framing, and non-religious clients often received advisor-centered religious appeals. Qualitative findings show that bias is linguistically manifested through religious anchoring, uneven cultural signaling, and tone modulation, varying by model and financial scenario. Stock investment prompts produced more financially technical responses, whereas life insurance advice triggered stronger religious language. The study develops a dual-dimensional framework linking structural bias rooted in model training and design with discursive bias expressed through language, advancing understanding of algorithmic bias in LLM-generated financial advice. It also shows that such advice adapts linguistically to identity cues, revealing a managerial dilemma between personalization and neutrality. Finally, it highlights implications for businesses, financial institutions, and regulators seeking to ensure neutrality, cultural sensitivity, and trust in AI-mediated advice.

cs.CY↗

arXiv2025

Sacred or Secular? Religious Bias in AI-Generated Financial Advice

This study examines religious biases in AI-generated financial advice, focusing on ChatGPT's responses to financial queries. Using a prompt-based methodology and content analysis, we find that 50% of the financial emails generated by ChatGPT exhibit religious biases, with explicit biases present in both ingroup and outgroup interactions. While ingroup biases personalize responses based on religious alignment, outgroup biases introduce religious framing that may alienate clients or create ideological friction. These findings align with broader research on AI bias and suggest that ChatGPT is not merely reflecting societal biases but actively shaping financial discourse based on perceived religious identity. Using the Critical Algorithm Studies framework, we argue that ChatGPT functions as a mediator of financial narratives, selectively reinforcing religious perspectives. This study underscores the need for greater transparency, bias mitigation strategies, and regulatory oversight to ensure neutrality in AI-driven financial services.

cs.CY↗

arXiv2021

Absorptive capacities and economic growth in low and middle income economies

I extend the concept of absorptive capacity, used in the analysis of firms, to a framework applicable to the national level. First, employing confirmatory factor analyses on 47 variables, I build 13 composite factors crucial to measuring six national level capacities: technological capacity, financial capacity, human capacity, infrastructural capacity, public policy capacity, and social capacity. My data cover most low- and middle-income- economies (LMICs), eligible for the World Bank's International Development Association (IDA) support between 2005 and 2019. Second, I analyze the relationship between the estimated capacity factors and economic growth while controlling for some of the incoming flows from abroad and other confounders that might influence the relationship. Lastly, I conduct K-means cluster analysis and then analyze the results alongside regression estimates to glean patterns and classifications within the LMICs. Results indicate that enhancing infrastructure (ICT, energy, trade, and transport), financial (apparatus and environment), and public policy capacities is a prerequisite for attaining economic growth. Similarly, I find improving human capital with specialized skills positively impacts economic growth. Finally, by providing a ranking of which capacity is empirically more important for economic growth, I offer suggestions to governments with limited budgets to make wise investments. Likewise, my findings inform international policy and monetary bodies on how they could better channel their funding in LMICs to achieve sustainable development goals and boost shared prosperity.

econ.GN↗

arXiv2021

Estimating a new panel MSK dataset for comparative analyses of national absorptive capacity systems, economic growth, and development in low and middle income economies

Within the national innovation system literature, empirical analyses are severely lacking for developing economies. Particularly, the low- and middle-income countries (LMICs) eligible for the World Bank's International Development Association (IDA) support, are rarely part of any empirical discourse on growth, development, and innovation. One major issue hindering panel analyses in LMICs, and thus them being subject to any empirical discussion, is the lack of complete data availability. This work offers a new complete panel dataset with no missing values for LMICs eligible for IDA's support. I use a standard, widely respected multiple imputation technique (specifically, Predictive Mean Matching) developed by Rubin (1987). This technique respects the structure of multivariate continuous panel data at the country level. I employ this technique to create a large dataset consisting of many variables drawn from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can thus be used for comparative analyses of national absorptive capacities and development, transition, and convergence analyses among LMICs.

econ.EM↗

arXiv2020

Evaluating the Effectiveness of Regional Lockdown Policies in the Containment of Covid-19: Evidence from Pakistan

To slow down the spread of Covid-19, administrative regions within Pakistan imposed complete and partial lockdown restrictions on socio-economic activities, religious congregations, and human movement. Here we examine the impact of regional lockdown strategies on Covid-19 outcomes. After conducting econometric analyses (Regression Discontinuity and Negative Binomial Regressions) on official data from the National Institute of Health (NIH) Pakistan, we find that the strategies did not lead to a similar level of Covid-19 caseload (positive cases and deaths) in all regions. In terms of reduction in the overall caseload (positive cases and deaths), compared to no lockdown, complete and partial lockdown appeared to be effective in four regions: Balochistan, Gilgit Baltistan (GT), Islamabad Capital Territory (ICT), and Azad Jammu and Kashmir (AJK). Contrarily, complete and partial lockdowns did not appear to be effective in containing the virus in the three largest provinces of Punjab, Sindh, and Khyber Pakhtunkhwa (KPK). The observed regional heterogeneity in the effectiveness of lockdowns advocates for a careful use of lockdown strategies based on the demographic, social, and economic factors.

econ.EM↗