Specification, Application, and Operationalization of a Metamodel of Fairness
📝 Abstract
This paper presents the AR fairness metamodel, aimed at formally representing, analyzing, and comparing fairness scenarios. The metamodel provides an abstract representation of fairness, enabling the formal definition of fairness notions. We instantiate the metamodel through several examples, with a particular focus on comparing the notions of equity and equality. We use the Tiles framework, which offers modular components that can be interconnected to represent various definitions of fairness. Its primary objective is to support the operationalization of AR-based fairness definitions in a range of scenarios, providing a robust method for defining, comparing, and evaluating fairness. Tiles has an open-source implementation for fairness modeling and evaluation.
💡 Analysis
This paper presents the AR fairness metamodel, aimed at formally representing, analyzing, and comparing fairness scenarios. The metamodel provides an abstract representation of fairness, enabling the formal definition of fairness notions. We instantiate the metamodel through several examples, with a particular focus on comparing the notions of equity and equality. We use the Tiles framework, which offers modular components that can be interconnected to represent various definitions of fairness. Its primary objective is to support the operationalization of AR-based fairness definitions in a range of scenarios, providing a robust method for defining, comparing, and evaluating fairness. Tiles has an open-source implementation for fairness modeling and evaluation.
📄 Content
Fairness is a critical consideration in various domains, including social policy, economics, and technology. Despite its importance, defining and evaluating fairness remains a complex challenge, as assessments of what is fair may vary across contexts and stakeholders. There is no universal definition of fairness, and even seemingly purely technical decisions can have direct fairness implications [3]. Given a specific scenario, a definition of fairness can be addressed by defining a fairness measure that measures the fair distribution of resources among agents. Although fairness measures are subjective, they must be well defined in critical contexts. This makes it essential to systematize how these measures are defined, from an abstract subjective understanding to concrete execution, and to support comparing fairness definitions.
This paper introduces the AR fairness metamodel, designed to represent and analyze fairness measures and scenarios. The metamodel extends the previous research in [26] and serves as a model of models [29], where each model is an instance of the metamodel. The specific instances of these models can then be evaluated to verify whether they comply with a given definition of fairness. The metamodel addresses the challenges of defining and evaluating fairness by offering a structured approach. It provides an abstract representation of fairness scenarios, incorporating key elements such as agents, resources, and attributes, which are essential components for evaluating whether a given outcome adheres to a specific definition of fairness.
We use Tiles [26], a framework designed to support the AR fairness metamodel. Tiles consists of modular blocks, called tiles, that can be interconnected to specify a definition of fairness. Each block is annotated to indicate how it can be connected to other blocks within the framework. The combination of the Tiles framework and the AR fairness metamodel provides a comprehensive set of tools to model fairness and evaluate fairness in various scenarios, and can be applied to real-world situations.
This paper is organized as follows. Section 2 provides an overview of computational models of fairness. Section 3 introduces the AR fairness metamodel and its components, including the definition of identifiers, measures, attributes, and auxiliary functions. Section 4 discusses the structure of the blocks and their graphical notation. Section 5 offers a discussion of the AR fairness metamodel and the Tiles framework, focusing on their capabilities and limitations. Finally, Section 6 concludes with reflections and a discussion of future work.
The importance of fairness in machine learning and artificial intelligence (AI) systems is widely recognized. From a modeling perspective, evaluating fairness requires the ability to identify and quantify unwanted bias, which may lead to prejudice and ultimately to discrimination.
Formalizing fairness can lead to greater transparency in achieving equitable outcomes, which benefits both individuals and the groups they represent. Although operationalizing fairness is challenging, efforts to formalize it and automate fairness verification [1,2] are relevant. Several quantifiable definitions have been proposed [13,17,20,21], reflecting legal, philosophical, and social perspectives. However, different interpretations can inadvertently harm the groups they aim to protect [11] or do not account for intersectionality [21].
Two widely discussed formalizations are individual fairness and group fairness. Individual fairness requires that similar individuals, based on nonprotected attributes, receive similar outcomes. Group fairness stipulates that protected groups should receive similar outcomes when non-protected factors are equal [10]. These notions can conflict [7]. For example, if two individuals with similar qualifications receive different outcomes solely because they belong to different protected groups, then group fairness metrics such as equality of odds or equality of opportunity can be used to address the disparity. In practice, reconciling these notions and managing the associated value tradeoffs remains an active research challenge [14,15,3]. Model-based methodologies, such as MBFair [27], enable the verification of software designs with respect to individual fairness.
Operational tools for fairness assessment include IBM’s AI Fairness 360 [6] (AIF360), Microsoft’s Fairlearn [9], and Google’s What-if Tool [30] (WIT). AIF360 is a comprehensive and technical open-source Python library that includes fairness metrics and bias mitigation algorithms. It works in the three stages: pre-processing, in-processing, and post-processing. Fairlearn includes fairness metrics, bias mitigation algorithms, and also provides fairness dashboards for visual comparisons. WIT is visualization-oriented and provides a dashboard to explore counterfactuals to answer the question “What if this feature changed?”. However, these tools focus on th
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