Imagined communities: Reflections on the origin and spread of nationalism

With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

A Survey on Bias and Fairness in Machine Learning

Flexible Citizenship: The Cultural Logics of Transnationality. Aihwa Ong. Durham, NIC: Duke University Press, 1999. ix. 322 pp., notes, bibliography, index.

Flexible Citizenship: The Cultural Logics of Transnationality

1. Resolving Ethical Issues 1.01 Misuse of Psychologists’ Work 1.02 Conflicts Between Ethics and Law, Regulations, or Other Governing Legal Authority 1.03 Conflicts Between Ethics and Organizational Demands 1.04 Informal Resolution of Ethical Violations 1.05 Reporting Ethical Violations 1.06 Cooperating With Ethics Committees 1.07 Improper Complaints 1.08 Unfair Discrimination Against Complainants and Respondents

Ethical Principles of Psychologists and Code of Conduct

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Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.

https://dl.acm.org/doi/pdf/10.1145/3287560.3287598

Fairness and Abstraction in Sociotechnical Systems

We trace how cultural probes have been adopted and adapted by the HCI community. The flexibility of probes has been central to their uptake, resulting in a proliferation of divergent uses and derivatives. The varying patterns of adaptation of the probes reveal important underlying issues in HCI, suggesting underacknowledged disagreements about valid interpretation and the relationship between methods and their underlying methodology. With this analysis, we aim to clarify discussions around probes, and, more importantly, around how we define and evaluate methods in HCI, especially those grounded in unfamiliar conceptions of how research should be done.

/pdf/how-hci-interprets-the-probes-3pmixiabyn.pdf

How HCI interprets the probes

The personal archive is not only about efficient storage and retrieval of information. This paper describes a study of forty-eight academics and the techniques and tools they use to manage their digital and material archiving of papers, emails, documents, internet bookmarks, correspondence, and other artifacts. We present two sets of results: we first discuss rationales behind subjects' archiving, which go beyond information retrieval to include creating a legacy, sharing resources, confronting fears and anxieties, and identity construction. We then show how these rationales were mapped into our subjects' physical, social and electronic spaces, and discuss implications for development of digital tools that allow for personal archiving.

/pdf/to-have-and-to-hold-exploring-the-personal-archive-4sgvpqzbm7.pdf

To have and to hold: exploring the personal archive

A fresh approach to visualization practices in the sciences that considers novel forms of imaging technology and draws on recent theoretical perspectives on representation. Representation in Scientific Practice, published by the MIT Press in 1990, helped coalesce a long-standing interest in scientific visualization among historians, philosophers, and sociologists of science and remains a touchstone for current investigations in science and technology studies. This volume revisits the topic, taking into account both the changing conceptual landscape of STS and the emergence of new imaging technologies in scientific practice. It offers cutting-edge research on a broad array of fields that study information as well as short reflections on the evolution of the field by leading scholars, including some of the contributors to the 1990 volume. The essays consider the ways in which viewing experiences are crafted in the digital era; the embodied nature of work with digital technologies; the constitutive role of materials and technologies-from chalkboards to brain scans-in the production of new scientific knowledge; the metaphors and images mobilized by communities of practice; and the status and significance of scientific imagery in professional and popular culture. Contributors Morana Alac, Michael Barany, Anne Beaulieu, Annamaria Carusi, Catelijne Coopmans, Lorraine Daston, Sarah de Rijcke, Joseph Dumit, Emma Frow, Yann Giraud, Aud Sissel Hoel, Martin Kemp, Bruno Latour, John Law, Michael Lynch, Donald MacKenzie, Cyrus Mody, Natasha Myers, Rachel Prentice, Arie Rip, Martin Ruivenkamp, Lucy Suchman, Janet Vertesi, Steve Woolgar

Representation in Scientific Practice Revisited

Understanding contemporary environments in the laboratory and elsewhere requires grappling conceptually with multiple, coexisting, nonconforming infrastructures which actors engage at the same time...

Janet Vertesi

Papers

Fairness and Abstraction in Sociotechnical Systems

How HCI interprets the probes

To have and to hold: exploring the personal archive

Representation in Scientific Practice Revisited

Seamful Spaces Heterogeneous Infrastructures in Interaction