How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations

By Ujwal Kayande, Arnaud De Bruyn, Gary L. Lilien, Arvind Rangaswamy, Gerrit van Bruggen. 2009

126 citations recorded by the source corpus.

Subfields: General Decision Sciences, Management Information Systems, Management Science and Operations Research

How researchers see this paper

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

In General Decision Sciences

1. David Martens
2. Foster J. Provost

In Management Information Systems

1. David Martens
2. Foster J. Provost

In Management Science and Operations Research

1. Chris Russell
2. David Martens
3. Foster J. Provost
4. Julie Moeyersoms
5. Sandra Wachter
6. Brent Daniel Mittelstadt
7. Brian M. D’Alessandro
8. Emilio Carrizosa

Citations in the corpus

• Explaining Classification Models Built on High-Dimensional Sparse Data (2016, background): “Prior work suggests that when users do not understand the workings of a classification model, they can be reluctant to use it, even if the model is known to improve decision performance (Kayande et al., 2009; Martens & Provost, 2014).”
• Explaining Explanations in AI (2018, background): “…machine learning models, it is perhaps most useful to always treat explanation generation as an interactive process, initially involving a mix of human and automated actors, at a minimum an inquirer (e.g. a developer, user) and the model or system (Kayande et al., 2009; Martens and Provost, 2013).”
• Explaining Data-Driven Document Classifications (2014, methodology): “…do not understand the workings of the DS model, they will be skeptical and reluctant to use the model, even ifthe model is known to improve deci- sion performance, see, e.g., Umanath and Vessey (1994), Limaye and De Sanctis (2000), Lilien et al. (2004), Arnold et al. (2006), Kayande et al. (2009).”