title: Distribution of willingness-to-pay for speed reduction with non-positive bidders: Is choice modelling consistent with contingent valuation?

authors: Scarpa, Riccardo

subjects: contingent valuation

abstract: The paper addresses the issue of consistency between two commonly employed stated preference data—referendum contingent valuation (CV) and discrete choice modelling (CM)—with respect to estimated distributions of individual willingness-to-pay (WTP) for non-market goods. The policy context is that of a local externality: effective speed reduction by means of traffic-calming in towns crossed by fast roads. In particular, data from two independent samples of the same population are contrasted. The findings show that both methods indicate that speed reduction via traffic-calming is valued in a polarized fashion. Results from both methods are consistent with the presence of two groups of preferences: a larger group holding positive values and a smaller one with non-positive values. While the estimates of the relative proportions of the two groups are similar across the two data sources, once the econometric analysis of the CM responses allows for polarized preferences the estimates of the distribution of individual WTP differ substantially. The results from the choice modelling survey indicate that residents are also willing to pay for other benefits from traffic-calming, such as noise reduction and a decreased waiting time for crossing, but preferences for these are also polarized, with WTP for aesthetic improvements being positive only for those supporting effective speed control. In comparing distributions of value estimates from CM and CV, surveys practitioners should account for the effects of taste heterogeneity over externalities and take advantage of the ability to derive individual-specific WTP estimates from panel estimation rather than simply deriving estimates for common features of the WTP distribution.

text: Distribution of willingness-to-pay for speed reduction with non-positive bidders: Is choice modelling consistent with contingent valuation? The paper addresses the issue of consistency between two commonly employed stated preference data—referendum contingent valuation (CV) and discrete choice modelling (CM)—with respect to estimated distributions of individual willingness-to-pay (WTP) for non-market goods. The policy context is that of a local externality: effective speed reduction by means of traffic-calming in towns crossed by fast roads. In particular, data from two independent samples of the same population are contrasted. The findings show that both methods indicate that speed reduction via traffic-calming is valued in a polarized fashion. Results from both methods are consistent with the presence of two groups of preferences: a larger group holding positive values and a smaller one with non-positive values. While the estimates of the relative proportions of the two groups are similar across the two data sources, once the econometric analysis of the CM responses allows for polarized preferences the estimates of the distribution of individual WTP differ substantially. The results from the choice modelling survey indicate that residents are also willing to pay for other benefits from traffic-calming, such as noise reduction and a decreased waiting time for crossing, but preferences for these are also polarized, with WTP for aesthetic improvements being positive only for those supporting effective speed control. In comparing distributions of value estimates from CM and CV, surveys practitioners should account for the effects of taste heterogeneity over externalities and take advantage of the ability to derive individual-specific WTP estimates from panel estimation rather than simply deriving estimates for common features of the WTP distribution.

year: 2006

title: Review of Jon and Susan Josephson, “Abductive Inference”

authors: Legg, Catherine

abstract: This article reviews the book '“Abductive Inference” by Jon and Susan Josephson.

text: Review of Jon and Susan Josephson, “Abductive Inference” This article reviews the book '“Abductive Inference” by Jon and Susan Josephson.

year: 1996

title: Data and model bias in artificial intelligence for healthcare applications in New Zealand

authors: Yogarajan, Vithya

abstract: Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

text: Data and model bias in artificial intelligence for healthcare applications in New Zealand Introduction: Developments in Artificial Intelligence (AI) are adopted widely in healthcare. However, the introduction and use of AI may come with biases and disparities, resulting in concerns about healthcare access and outcomes for underrepresented indigenous populations. In New Zealand, Māori experience significant inequities in health compared to the non-Indigenous population. This research explores equity concepts and fairness measures concerning AI for healthcare in New Zealand. Methods: This research considers data and model bias in NZ-based electronic health records (EHRs). Two very distinct NZ datasets are used in this research, one obtained from one hospital and another from multiple GP practices, where clinicians obtain both datasets. To ensure research equality and fair inclusion of Māori, we combine expertise in Artificial Intelligence (AI), New Zealand clinical context, and te ao Māori. The mitigation of inequity needs to be addressed in data collection, model development, and model deployment. In this paper, we analyze data and algorithmic bias concerning data collection and model development, training and testing using health data collected by experts. We use fairness measures such as disparate impact scores, equal opportunities and equalized odds to analyze tabular data. Furthermore, token frequencies, statistical significance testing and fairness measures for word embeddings, such as WEAT and WEFE frameworks, are used to analyze bias in free-form medical text. The AI model predictions are also explained using SHAP and LIME. Results: This research analyzed fairness metrics for NZ EHRs while considering data and algorithmic bias. We show evidence of bias due to the changes made in algorithmic design. Furthermore, we observe unintentional bias due to the underlying pre-trained models used to represent text data. This research addresses some vital issues while opening up the need and opportunity for future research. Discussions: This research takes early steps toward developing a model of socially responsible and fair AI for New Zealand's population. We provided an overview of reproducible concepts that can be adopted toward any NZ population data. Furthermore, we discuss the gaps and future research avenues that will enable more focused development of fairness measures suitable for the New Zealand population's needs and social structure. One of the primary focuses of this research was ensuring fair inclusions. As such, we combine expertise in AI, clinical knowledge, and the representation of indigenous populations. This inclusion of experts will be vital moving forward, proving a stepping stone toward the integration of AI for better outcomes in healthcare.

year: 2022-12-22