Value Sensitive Design and Information Systems

By Batya Friedman, Peter H. Kahn Jr., Alan Borning, Alina Huldtgren. 2020

1,233 citations recorded by the source corpus.

Subfields: Management Information Systems, Management of Technology and Innovation, Sociology and Political Science, Safety, Robustness & Interpretability

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “Value-sensitive design provides a framework for accounting for the values of different stakeholders in the design of technology (e.g., Friedman et al., 2006; Friedman and Hendry, 2019; Le Dantec et al., 2009; Yoo et al., 2019), while participatory design seeks to involve stakeholders in the design…”
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models (2020, methodology): “(2020), we recommend a reexamination of the relationship between NLP systems and their end users, using methods from humancentered design, such as value-sensitive (Friedman et al., 2008) or participatory design (Sanders, 2002; DiSalvo et al.”
Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Mitigation and Interventions Mitigations should be cognizant of preserving irreducible differences among cultures [73] and practicing value sensitive design [84], including by focusing on system components such as data extraction and use [62].”
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “We start with a working deﬁnition of values as “what a person or group of people consider important in life” (Friedman et al., 2008).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, methodology): “We use the broad definition of values employed in Friedman et al. (2008): “what a person or group of people consider important in life.””
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018, background): “Stakeholders Stakeholders are people impacted directly or indirectly by a system (Friedman et al., 2006; Czeskis et al., 2010).”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “This role of contextual values also applies to the work of computing and technology (Nissenbaum 2001; Van de Poel and Kroes 2014)—a recognition established in the broader field of values in technology (Friedman et al. 2013; Sengers et al. 2005; DiSalvo 2012).”
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection (2021, background): “To try answering this question, we urge researchers and practitioners to consider all stakeholders and end users on which toxicity detection systems might be deployed (e.g., through humancentered design methods; Sanders, 2002; Friedman et al., 2008; Hovy and Yang, 2021).”
A Survey of Race, Racism, and Anti-Racism in NLP (2021, background): “NLP researchers can draw on precedents for this type of engagement from other ﬁelds, such as participatory design and value sensitive design models (Friedman et al., 2013).”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, methodology): “We thus encourage further contributions to S AFE - TY K IT , e.g., research into more comprehensive automatic measures, as well as into human evaluation and iterative, value-based frameworks to assess potential harms, e.g., Friedman et al. (2008).”
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (2019, background): “Scholars in the Value Sensitive Design tradition have studied older cases in which the design of a system influenced the kind of data that could be represented, and the reality that was consequently asserted by that system [31].”
Design for value change (2018, background): “Value sensitive design (VSD) aims at integrating values of ethical import from the start in the design process of new products and systems (Friedman et al. 2006).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “…the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process” that is based on an “integrative and iterative tripartite methodology, consisting of conceptual, empirical, and technical investigations” (Friedman et al. 2013, p. 348).”
Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning (2019, background): “These include Value-Sensitive Design (VSD) [29], Values @ Play [30], reflective design [31], adversarial design”
Computer knows best? The need for value-flexibility in medical AI (2018, background): “(Friedman, p. 72) 38 This analysis is integrated with findings from empirical investigations of the specific human social context in which the technology will be situated.”
Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency (2021, methodology): “These have been long standing topics of research within the HCI community [4, 12, 22, 36, 37, 68].”
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics (2018, background): “Determine what values are relevant in building a decision-making system and how they might be embodied or challenged in the design and implementation by engaging with users and other affected stakeholders (Van den Hoven, 2007; Friedman et al., 2013).”
Towards Transparency by Design for Artificial Intelligence (2020, background): “Approaches such as Friedman et al.’s (2008) Value-Sensitive Design, and subject-specific variations, such as Aimee van Wynsberghe’s Care-Centred Value Sensitive Design for Health-care Robotics (Van Wynsberghe 2013), have conceptualized approaches to design that aim to realize this insight into…”