Using interactive “Nutrition labels” for financial products to assist decision making under uncertainty

Gunaratne, Junius; Nov, Oded

doi:10.1002/asi.23844

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…The nutritional label combines textual explanations and graphic information, representing a best practice of dual learning theory appropriate for learners (National Research Council, 2000 ). Research supports the effectiveness of this format, demonstrating that nutritional labels, particularly those that have interactive functionality, can increase one’s understanding of a complex topic and lead to better decision making (Byrd-Bredbenner et al, 2009 ; Gunaratne & Nov, 2017 ; Kelley et al, 2010 ; Stoyanovich & Howe, 2019 ). The nutritional label as a paradigm creates a familiar entry point at which one can engage and start to question the interpretability of a model.…”

Section: Objects-to-interpret-with: Tools For Understanding Data Commentioning

confidence: 99%

Teaching Responsible Data Science: Charting New Pedagogical Territory

Lewis

Stoyanovich

2021

Int J Artif Intell Educ

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Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with , a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of “nutritional labels”—a family of interpretability tools that are gaining popularity in responsible data science research and practice.

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Section: Objects-to-interpret-with: Tools For Understanding Data Commentioning

confidence: 99%

Teaching Responsible Data Science: Charting New Pedagogical Territory

Lewis

Stoyanovich

2021

Int J Artif Intell Educ

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“…The inclusion of more advanced navigation and searchability tools could also improve users' ability to find material of interest to them. Studies of the utility of information delivery have shown that it increases when users are able to choose and adjust relevant input parameters and display different results (Gunaratne and Nov, 2017). Allowing user interaction with complex material has also been shown to increase understanding and lead to better decision-making, especially among novice non-repeat users (Gunaratne and Nov, 2017).…”

Section: Digital Integrationmentioning

confidence: 99%

Is mandatory disclosure an effective panacea for buyer beware?

Waye,

Snowden,

Knowler

et al. 2023

JPPEL

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Purpose The purpose of this paper is to examine whether mandatory disclosure of information accompanying the sale of real estate achieves its aim of informed purchasers. Design/methodology/approach Using a case study approach focused on mandatory disclosure in South Australia data was collected from interviews and focus groups with key personnel in the property industry involved in the production of information required to fulfil vendors’ disclosure obligations. Findings The authors found that purchasers are ill-served by a long and complex form of mandatory disclosure with a short time frame that prevents the use of the information provided. Without good form design and increased digital affordances provided by the cadastral and conveyancing systems, mandatory disclosure is insufficient to ensure minimisation of information asymmetry between vendor and purchaser. Originality/value To the best of the authors’ knowledge, this is the first Australian qualitative study that examines the utility of mandatory vendor disclosure in real estate sales and the first to consider the impact of the digitalisation of cadastral and conveyancing systems upon the efficacy of mandatory disclosure regimes.

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“…Their ability to influence consumers' behaviour and purchase intentions has been supported by scientific studies and is being widely exploited by online retailers (Breugelmans et al 2012;Häubl and Murray 2003;Punj and Moore 2007;Xiao and Benbasat 2007). Only recently has the inclusion of nutritional criteria into the matching algorithms between preferences and recommended products received more attention (Gunaratne and Nov 2017;Ngoc et al 2018). However, literature covering empirical user testing remains very scarce and focusses only on nutritional indicators while also from an environmental viewpoint there is a strong need for such facilitating strategies (Trattner and Elsweiler 2017).…”

Section: Conceptual Approachmentioning

confidence: 99%