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Customizable Fairness Assessment: From Conceptualization to Automated Implementation

Core Concepts
MODNESS enables users to define and assess customized fairness concepts across diverse application domains through a model-driven approach.
The paper introduces MODNESS, a model-driven framework for conceptualizing, designing, implementing, and executing fairness assessment workflows. MODNESS allows users to define their own bias and fairness concepts, going beyond pre-established definitions. The key highlights are: MODNESS is built on a tailored metamodel that supports the specification of bias definitions (including sensitive variables, privileged/unprivileged groups, and positive outcomes) and fairness analyses (with customizable metrics). MODNESS automatically generates the implementation code to assess fairness based on the user-defined specifications, leveraging libraries like Pandas and AIF360. The evaluation demonstrates MODNESS's expressiveness by modeling diverse use cases from social, financial, and software engineering domains. It also shows MODNESS's ability to overcome limitations of existing MDE-based approaches for fairness assessment. MODNESS enables users to define and evaluate fairness concepts in emerging domains, such as mitigating popularity bias in recommender systems for software engineering and Arduino software component recommendations.
"The positive outcome is non-recidiv (not becoming a repeat offender) in the COMPAS dataset." "The positive outcome is income higher than $50,000 per year in the Adult Census Income dataset." "The positive outcome is having a credit granted in the German Credit dataset." "The positive outcome is will subscribe in the Bank Marketing dataset." "The positive outcome is the list of recommended libraries in the Software third-party libraries dataset." "The positive outcome is the list of recommended Arduino components in the Resyduo dataset."
"Fairness is a critical concept in ethics and social domains, but it is also a challenging property to engineer in software systems." "With the increasing use of machine learning in software systems, researchers have been developing techniques to automatically assess the fairness of software systems." "To overcome this limitation, we propose a novel approach, called MODNESS, that enables users to customize and define their fairness concepts using a dedicated modeling environment."

Deeper Inquiries

How can MODNESS be extended to support the definition and assessment of fairness in other emerging domains, such as healthcare or education?

MODNESS can be extended to support the definition and assessment of fairness in other emerging domains by incorporating domain-specific metamodels and metrics. For healthcare, sensitive variables could include patient demographics or medical history, with positive outcomes related to accurate diagnoses or treatment recommendations. In education, variables like socioeconomic status or previous academic performance could be considered, with positive outcomes being fair access to educational opportunities. By allowing users to customize bias definitions and metrics, MODNESS can adapt to the unique requirements of different domains. Additionally, integrating specific datasets and algorithms relevant to healthcare or education would enhance the tool's applicability in these areas.

What are the potential limitations of a model-driven approach like MODNESS in terms of scalability and performance when dealing with large-scale datasets and complex fairness definitions?

One potential limitation of a model-driven approach like MODNESS when dealing with large-scale datasets is the computational complexity involved in processing and analyzing vast amounts of data. As the size of the dataset increases, the performance of the tool may be impacted, leading to longer processing times and potential resource constraints. Additionally, complex fairness definitions with multiple variables and intricate relationships may require sophisticated modeling techniques, which could strain the scalability of the tool. Ensuring efficient data handling, optimization of algorithms, and scalability testing would be crucial to address these limitations and enhance the tool's performance with large-scale datasets and complex fairness definitions.

How can MODNESS be integrated with existing software development workflows to seamlessly incorporate fairness assessment as a standard practice?

To integrate MODNESS with existing software development workflows for seamless fairness assessment, several steps can be taken: API Integration: Develop APIs that allow MODNESS to interact with existing software systems, enabling data exchange and analysis. Automated Testing: Incorporate MODNESS into automated testing pipelines to assess fairness continuously during the development process. Version Control: Utilize version control systems like Git to track changes in fairness definitions and metrics, ensuring transparency and reproducibility. Documentation: Provide detailed documentation on how to use MODNESS within the software development workflow, including best practices and guidelines. Training and Support: Offer training sessions and support resources to educate developers on using MODNESS effectively for fairness assessment. By seamlessly integrating MODNESS into the software development lifecycle and promoting a culture of fairness awareness, organizations can make fairness assessment a standard practice in their development processes.