Software Fairness Debt
- URL: http://arxiv.org/abs/2405.02490v2
- Date: Sat, 28 Sep 2024 22:13:36 GMT
- Title: Software Fairness Debt
- Authors: Ronnie de Souza Santos, Felipe Fronchetti, Savio Freire, Rodrigo Spinola,
- Abstract summary: This paper focuses on exploring the multifaceted nature of bias in software systems.
We identify the primary causes of fairness deficiency in software development and highlight their adverse effects on individuals and communities.
Our study contributes to a deeper understanding of fairness in software engineering and paves the way for the development of more equitable and socially responsible software systems.
- Score: 0.5249805590164902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As software systems continue to play a significant role in modern society, ensuring their fairness has become a critical concern in software engineering. Motivated by this scenario, this paper focused on exploring the multifaceted nature of bias in software systems, aiming to provide a comprehensive understanding of its origins, manifestations, and impacts. Through a scoping study, we identified the primary causes of fairness deficiency in software development and highlighted their adverse effects on individuals and communities, including instances of discrimination and the perpetuation of inequalities. Our investigation culminated in the introduction of the concept of software fairness debt, which complements the notions of technical and social debt, encapsulating the accumulation of biases in software engineering practices while emphasizing the societal ramifications of bias embedded within software systems. Our study contributes to a deeper understanding of fairness in software engineering and paves the way for the development of more equitable and socially responsible software systems.
Related papers
- Beyond the Classroom: Bridging the Gap Between Academia and Industry with a Hands-on Learning Approach [2.8123958518740544]
Self-adaptive software systems have emerged as a critical focus in software design and operation.
A survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry.
We present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies.
arXiv Detail & Related papers (2025-04-14T21:32:25Z) - Who Speaks for Ethics? How Demographics Shape Ethical Advocacy in Software Development [3.504870208291045]
This study investigates ethical concerns in software development, focusing on how they are perceived, prioritized, and addressed by demographically different practitioners.
Our findings reveal pronounced demographic disparities, with marginalized groups - including women, BIPOC, and disabled individuals - reporting ethical concerns at higher frequencies.
These insights underscore the urgent need for reforms in software education and development processes that center on diverse perspectives.
arXiv Detail & Related papers (2025-04-14T14:43:57Z) - Realigning Incentives to Build Better Software: a Holistic Approach to Vendor Accountability [7.627207028377776]
We argue that the challenge around better quality software is due in no small part to a sequence of misaligned incentives.
Lack of liability means software vendors have every incentive to rush low-quality software onto the market.
This paper outlines a holistic technical and policy framework we believe is needed to incentivize better and more secure software development.
arXiv Detail & Related papers (2025-04-10T14:05:24Z) - Metamorphic Debugging for Accountable Software [8.001739956625483]
Translating legalese into formal specifications is one challenge.
Lack of a definitive 'truth' for queries (the oracle problem) is another.
We propose that these challenges can be tackled by focusing on relational specifications.
arXiv Detail & Related papers (2024-09-24T14:45:13Z) - Beyond Diversity:Computing for Inclusive Software [2.867517731896504]
This chapter presents from our research on inclusive software within the context of a diversity and inclusion based STEM program at the University of Victoria.
We found that empathy-based requirements gathering techniques and certain influences on the software development teams' motivation levels impact the teams' ability to build inclusive software.
arXiv Detail & Related papers (2024-08-09T19:01:58Z) - Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE.
We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
arXiv Detail & Related papers (2024-04-10T16:18:11Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Software engineering in start-up companies: An analysis of 88 experience
reports [3.944126365759018]
This study investigates how software engineering is applied in start-up context.
We identify the most frequently reported software engineering (requirements engineering, software design and quality) and business aspect (vision and strategy development) knowledge areas.
We conclude that most engineering challenges in start-ups stem from inadequacies in requirements engineering.
arXiv Detail & Related papers (2023-11-20T19:42:37Z) - Software development in startup companies: A systematic mapping study [4.881718571745022]
This study aims to structure and analyze the literature on software development in startup companies.
A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups.
From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed.
arXiv Detail & Related papers (2023-07-24T19:49:57Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
We argue that ethical considerations need to be embedded into the (agile) software development process.
We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
arXiv Detail & Related papers (2021-07-15T11:14:03Z) - Interdisciplinary Approaches to Understanding Artificial Intelligence's
Impact on Society [7.016365171255391]
AI has come with a storm of unanticipated socio-technical problems.
We need tighter coupling of computer science and those disciplines that study society and societal values.
arXiv Detail & Related papers (2020-12-11T00:43:47Z) - Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems.
No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages.
This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z) - On Consequentialism and Fairness [64.35872952140677]
We provide a consequentialist critique of common definitions of fairness within machine learning.
We conclude with a broader discussion of the issues of learning and randomization.
arXiv Detail & Related papers (2020-01-02T05:39:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.