arXiv subjects

Diptish Dey

Publications and source records attributed to Diptish Dey.

7 recordsLinked to original sources

arXiv2025

The AI-Fraud Diamond: A Novel Lens for Auditing Algorithmic Deception

As artificial intelligence (AI) systems become increasingly integral to organizational processes, they introduce new forms of fraud that are often subtle, systemic, and concealed within technical complexity. This paper introduces the AI-Fraud Diamond, an extension of the traditional Fraud Triangle that adds technical opacity as a fourth condition alongside pressure, opportunity, and rationalization. Unlike traditional fraud, AI-enabled deception may not involve clear human intent but can arise from system-level features such as opaque model behavior, flawed training data, or unregulated deployment practices. The paper develops a taxonomy of AI-fraud across five categories: input data manipulation, model exploitation, algorithmic decision manipulation, synthetic misinformation, and ethics-based fraud. To assess the relevance and applicability of the AI-Fraud Diamond, the study draws on expert interviews with auditors from two of the Big Four consulting firms. The findings underscore the challenges auditors face when addressing fraud in opaque and automated environments, including limited technical expertise, insufficient cross-disciplinary collaboration, and constrained access to internal system processes. These conditions hinder fraud detection and reduce accountability. The paper argues for a shift in audit methodology-from outcome-based checks to a more diagnostic approach focused on identifying systemic vulnerabilities. Ultimately, the work lays a foundation for future empirical research and audit innovation in a rapidly evolving AI governance landscape.

cs.CY↗

arXiv2023

Metrics for popularity bias in dynamic recommender systems

Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on determining whether a RecSys is discriminating or not but does not compute the amount of bias present in these systems. Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. Hence, it is important to quantify these biases for fair and safe commercial applications of these systems. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models, leading to over recommendation of popular items that are likely to be misaligned with user preferences. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed. These metrics have been demonstrated for four collaborative filtering based RecSys algorithms trained on two commonly used benchmark datasets in the literature. Results obtained show that the metrics proposed provide a comprehensive understanding of growing disparities in treatment between sensitive groups over time when used conjointly.

cs.CY↗

arXiv2023

APPRAISE: a governance framework for innovation with AI systems

As artificial intelligence (AI) systems increasingly impact society, the EU Artificial Intelligence Act (AIA) is the first serious legislative attempt to contain the harmful effects of AI systems. This paper proposes a governance framework for AI innovation. The framework bridges the gap between strategic variables and responsible value creation, recommending audit as an enforcement mechanism. Strategic variables include, among others, organization size, exploration versus exploitation -, and build versus buy dilemmas. The proposed framework is based on primary and secondary research; the latter describes four pressures that organizations innovating with AI experience. Primary research includes an experimental setup, using which 34 organizations in the Netherlands are surveyed, followed up by 2 validation interviews. The survey measures the extent to which organizations coordinate technical elements of AI systems to ultimately comply with the AIA. The validation interviews generated additional in-depth insights and provided root causes. The moderating effect of the strategic variables is tested and found to be statistically significant for variables such as organization size. Relevant insights from primary and secondary research are eventually combined to propose the APPRAISE framework.

cs.CY↗

arXiv2023

Complying with the EU AI Act

The EU AI Act is the proposed EU legislation concerning AI systems. This paper identifies several categories of the AI Act. Based on this categorization, a questionnaire is developed that serves as a tool to offer insights by creating quantitative data. Analysis of the data shows various challenges for organizations in different compliance categories. The influence of organization characteristics, such as size and sector, is examined to determine the impact on compliance. The paper will also share qualitative data on which questions were prevalent among respondents, both on the content of the AI Act as the application. The paper concludes by stating that there is still room for improvement in terms of compliance with the AIA and refers to a related project that examines a solution to help these organizations.

cs.AI↗

arXiv2022

An Audit Framework for Technical Assessment of Binary Classifiers

Multilevel models using logistic regression (MLogRM) and random forest models (RFM) are increasingly deployed in industry for the purpose of binary classification. The European Commission's proposed Artificial Intelligence Act (AIA) necessitates, under certain conditions, that application of such models is fair, transparent, and ethical, which consequently implies technical assessment of these models. This paper proposes and demonstrates an audit framework for technical assessment of RFMs and MLogRMs by focussing on model-, discrimination-, and transparency & explainability-related aspects. To measure these aspects 20 KPIs are proposed, which are paired to a traffic light risk assessment method. An open-source dataset is used to train a RFM and a MLogRM model and these KPIs are computed and compared with the traffic lights. The performance of popular explainability methods such as kernel- and tree-SHAP are assessed. The framework is expected to assist regulatory bodies in performing conformity assessments of binary classifiers and also benefits providers and users deploying such AI-systems to comply with the AIA.

cs.CY↗

arXiv2022

A Framework for Auditing Multilevel Models using Explainability Methods

Applications of multilevel models usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects, model, discrimination, and transparency and explainability. These aspects are subsequently divided into sub aspects. Contributors, such as inter MLM group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs. A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency and explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling. Using an open source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution. For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying MLMs to be future proof and aligned with the European Commission proposed Regulation on Artificial Intelligence.

cs.CY↗

arXiv2022

Inter-relational Model for understanding Chatbot acceptance across retail sectors

Despite the rising interest in chatbots, deployment has been slow in the retail sector. In the absence of comparative cross sector research on the user acceptance of chatbots in retail, we present a model and a research framework that proposes customer and chatbot antecedents using trust and customer satisfaction as relationship mediators and word of mouth and expectation of continuity as relationship outcomes. In determining our framework, we assimilate constructs from different models and theories overarching user experience with chatbots, technology acceptance and relationship marketing and propose a selection of 11 constructs as antecedents. Furthermore, we suggest retail sectors as one of our 4 moderators. Eventually, we provide insight into our current activities that is expected to identify which factors impact relationship outcomes to which extent across different retail sectors.

cs.HC↗