Auditing fairness under unawareness through counterfactual reasoning

Cornacchia, Giandomenico; Anelli, Vito Walter; Biancofiore, Giovanni; Narducci, Fedelucio; Pomo, Claudio; Ragone, Azzurra; Sciascio, Eugenio Di

doi:10.1016/j.ipm.2022.103224

Cited by 18 publications

(12 citation statements)

References 17 publications

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“…Bias auditing tools typically rely on a combination of several methods to detect and analyze bias in AI systems. These methods can include fairness metrics, counterfactual analysis, sensitivity analysis, algorithmic transparency, and adversarial testing [25][26][27]. For example, a bias auditing tool may apply fairness metrics to spotlight potential biases in a model and then use counterfactual analysis to understand the underlying causes of the bias.…”

Section: Bias Auditing Toolsmentioning

confidence: 99%

Human-Centered Design to Address Biases in Artificial Intelligence

Chen¹,

Clayton²,

Novak³

et al. 2023

J Med Internet Res

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The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

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Section: Bias Auditing Toolsmentioning

confidence: 99%

Human-Centered Design to Address Biases in Artificial Intelligence

Chen¹,

Clayton²,

Novak³

et al. 2023

J Med Internet Res

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show abstract

“…However, defining what constitutes fairness in AI is a complex and multifaceted task. So far, there are mainly seven types of definitions, including individual fairness [40,41], group fairness [42], equality of opportunity [11], disparate treatment [43], fairness through unawareness [44,45], disparate impact [46], and subgroup fairness [47].…”

Section: Definitionmentioning

confidence: 99%

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

Chen,

Wu,

Wang

2023

Applied Sciences

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This article provides a comprehensive overview of the fairness issues in artificial intelligence (AI) systems, delving into its background, definition, and development process. The article explores the fairness problem in AI through practical applications and current advances and focuses on bias analysis and fairness training as key research directions. The paper explains in detail the concept, implementation, characteristics, and use cases of each method. The paper explores strategies to reduce bias and improve fairness in AI systems, reviews challenges and solutions to real-world AI fairness applications, and proposes future research directions. In addition, this study provides an in-depth comparative analysis of the various approaches, utilizing cutting-edge research information to elucidate their different characteristics, strengths, and weaknesses. The results of the comparison provide guidance for future research. The paper concludes with an overview of existing challenges in practical applications and suggests priorities and solutions for future research. The conclusions provide insights for promoting fairness in AI systems. The information reviewed in this paper is drawn from reputable sources, including leading academic journals, prominent conference proceedings, and well-established online repositories dedicated to AI fairness. However, it is important to recognize that research nuances, sample sizes, and contextual factors may create limitations that affect the generalizability of the findings.

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“…Once the model performs the prediction task, the customer will be provided with an explanation, especially in case of rejection. In previous work [17][18][19], the authors provide different pipelines for generating natural language-based explanations, using both Shapley values and Counterfactual reasoning. As a game theory approach, the Shapley values give ranked feature importance of the most discriminating features for the decision task.…”

Section: A Trustworthy Credit Assessment Platformmentioning

confidence: 99%

A General Architecture for a Trustworthy Creditworthiness-Assessment Platform in the Financial Domain

Cornacchia¹,

Anelli²,

Narducci³

et al. 2023

AETiC

Self Cite

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The financial domain is making huge advancements thanks to the exploitation of artificial intelligence. As an example, the credit-worthiness-assessment task is now strongly based on Machine Learning algorithms that make decisions independently from humans. Several studies showed remarkable improvement in reliability, customer care, and return on investment. Nonetheless, many users remain sceptical since they perceive the whole as only partially transparent. The trust in the system decision, the guarantee of fairness in the decision-making process, the explanation of the reasons behind the decision are just some of the open challenges for this task. Moreover, from the financial institution's perspective, another compelling problem is credit-repayment monitoring. Even here, traditional models (e.g., credit scorecards) and machine learning models can help the financial institution in identifying, at an early stage, customers that will fall into default on payments. The monitoring task is critical for the debt-repayment success of identifying bad debtors or simply users who are momentarily in difficulty. The financial institution can thus prevent possible defaults and, if possible, meet the debtor's needs. In this work, the authors propose an architecture for a Creditworthiness-Assessment duty that can meet the transparency needs of the customers while monitoring the credit-repayment risk. This preliminary study carried out an experimental evaluation of the component devoted to the credit-score computation and monitoring credit repayments. The study shows that the authors’ architecture can be an effective tool to improve current Credit-scoring systems. Combining a static and a subsequent dynamic approach can correct mistakes made in the first phase and foil possible false positives for good creditors.

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Auditing fairness under unawareness through counterfactual reasoning

Cited by 18 publications

References 17 publications

Human-Centered Design to Address Biases in Artificial Intelligence

Human-Centered Design to Address Biases in Artificial Intelligence

AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications

A General Architecture for a Trustworthy Creditworthiness-Assessment Platform in the Financial Domain

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