Suchergebnisse

Professionals are increasingly relying on algorithmic systems for decision making however, algorithmic decisions occasionally per- ceived as biased or not just. Prior work has provided evidences that education can make a difference on the perception of young devel- opers on algorithmic fairness. In this paper, we investigate com- puter science students' perception of FATE in algorithmic decision- making and whether their views on FATE can be changed by at- tending a seminar on FATE topics. Participants attended a seminar on FATE in algorithmic decision-making and they were asked to respond to two online questionnaires to measure their pre- and post-seminar perception on FATE. Results show that a short semi- nar can make a difference in understanding and perception as well as the attitude of the students towards FATE in algorithmic decision support. However, short seminars are not a complete and long term solution in educating CS students on FATE. CS curricula need to be updated and include FATE topics if we want algorithmic decision support systems to be just to their users. ; This project is partially funded by the Cyprus Research and Innova- tion Foundation under grant EXCELLENCE/0918/0086 (DESCANT) and by the European Union's Horizon 2020 Research and Innova- tion Programme under agreements No. 739578 (RISE) and 810105 (CyCAT).

AbstractModern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. Based on complications with this approach identified in the literature, this article discusses how Rawls's theory in general, and especially the difference principle, should reasonably be applied to algorithmic fairness decisions. It is observed that proposals to achieve Rawlsian algorithmic fairness often aim to uphold the difference principle in the individual situations where automated decision-making occurs. However, the Rawlsian difference principle applies to society at large and does not aggregate in such a way that upholding it in constituent situations also upholds it in the aggregate. But such aggregation is a hidden premise of many proposals in the literature and its falsity explains many complications encountered.

The multi-stakeholder approach is a valuable methodology for governance and policy development. In this paper we describe our use of the approach in the ongoing UnBias study, which seeks to identify opportunities for the effective governance of algorithmic online services. We use the multi-stakeholder methodology to bring together experts from relevant sectors including academia, education, government, regulation, law, civil society, media, and industry and commerce. This paper outlines how our work so far has facilitated open and constructive debate that can drive the development of meaningful policy recommendations. We also describe some challenges of this approach and our next steps towards producing actionable design and policy recommendations, including engagement with international industry standards development.1

This article discusses fairness in artificial intelligence (AI) based policing procedures using facial recognition as an example. Algorithmic decisions based on discriminatory dynamics can (re)produce and automate injustice. AI fairness here concerns not only the creation and sharing of datasets or the training of models but also how systems are deployed in the real world. Quantifying fairness can distract rom how discrimination and oppression translate concretely into social phenomena. Integrative approaches can help actively incorporate ethical, legal, social, and economic factors into technology development to more holistically assess the consequences of deployment through continuous interdisciplinary collaboration.

AbstractNational Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022, Statistical Journal of the IAOS). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as we provide introductions to these subjects. These introductions are also targeted to the discussion of quality dimensions and fairness.

AbstractThere is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented within narrow and targeted fairness toolkits for algorithm assessments that are difficult to integrate into an algorithm's broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around the acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker's ethical values.

Abstract
Despite women's early contributions to computer science, the field became seen as male-dominated, especially after 1980s marketing of computers mainly to men, reducing female participation. Yet, figures like Ada Lovelace and the female ENIAC programmers, with backgrounds in math and physics, significantly influenced the digital world.
This article explores the ongoing gap in digital skills and literacy between genders, focusing on how biases in technology affect women. It highlights the achievements of notable women in tech, from Angela Robles, who created the first e-book reader in 1949, to Hedy Lamarr's work on GPS and Wi-Fi, and Margaret Hamilton's role in the Apollo 11 mission. The article shows the importance of including women in technology, supporting the idea that their participation is key to fair and dynamic digital societies, as suggested by Manuel Castells' network society.
The article reviews the McKinsey report on job changes due to automation, showing a likely rise in technology-related jobs, and discusses the World Economic Forum's (WEF) forecast of increased tech spending from 2015 to 2030, noting many new jobs in IT services. This study highlights the shift in future job skills towards creativity, critical thinking, and negotiation, along with technical skills, and notes the WEF's prediction of 6.1 million new jobs in areas like data science, AI, and cloud computing.
In conclusion, the article advocates for concerted efforts to bridge the gender gap in digital skills, mitigate biases in algorithms, and support inclusivity in the digital era. It underscores the imperative of empowering women in technology to harness their talents and perspectives for driving innovation and equitable socio-economic development.
Key Words: Algorithmic Bias, Gender Gap, Network Society, Digital Skills, Digital Divide