A Survey of Value Sensitive Design Methods

By Batya Friedman, David G. Hendry, Alan Borning. 2018

355 citations recorded by the source corpus.

Subfields: Management of Technology and Innovation, Mechanical Engineering, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management of Technology and Innovation

1. Emily M. Bender
2. Verena Rieser
3. Gavin Abercrombie
4. Dirk Hovy
5. A. S. Bergman
6. Emily Dinan
7. Shannon L. Spruit
8. Y-Lan Boureau

In Mechanical Engineering

1. Batya Friedman

In Safety, Robustness & Interpretability

1. Y-Lan Boureau
2. Gavin Abercrombie
3. Dirk Hovy
4. Verena Rieser
5. Shannon L. Spruit
6. Emily Dinan
7. Emily M. Bender
8. A. S. Bergman

Citations in the corpus

• Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, methodology): “Friedman et al. (2017) survey numerous techniques to help practitioners implement value-sensitive design, such as the “value dams and flows” heuristic (Miller et al., 2007).”
• Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, methodology): “Friedman et al. (2017) survey several techniques to help practitioners implement value-sensitive design, such as the “value dams and flows" heuristic (Miller et al., 2007).”
• The ethics of AI in health care: A mapping review. (2020, neutral): “…solutions have been put forward for mitigating issues with data bias (Gebru et al., 2018; Holland et al., 2018) and data quality (Dai et al., 2018) and ensuring social inclusion in decision-making (Balthazar et al., 2018; Friedman et al., 2017; Rahwan, 2018), these remain relatively untested.”
• Human-Computer Insurrection: Notes on an Anarchist HCI (2019, background): “" [74] The last few decades have seen HCI take a turn to examine the societal implications of our work: who is included [10, 68, 71, 79], what values it promotes or embodies [56, 57, 129], and how we respond (or do not) to social shifts [93].”
• Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science (2018, background): “to come into widespread use, over longer periods of time, with attention to a wide range of stakeholders, potential benefits, and harms (Friedman et al., 2006a; Friedman et al., 2017).”