From Comparative Risk to Decision Analysis: Ranking Solutions to Multiple-Value Environmental Problems

While recognizing that the making of environmental policy is sufficiently complex that no one method can serve all conditions, Dr. Kadvany urges that more attention be given to multiattribute utility and decision analysis. He suggests this can help, e.g., to illuminate stakeholder values and generate alternative approaches

Comparing Clean Water Act Section 316(b) Policy Options

This paper develops a comparative framework for policy proposals involving fish protection and Section 316(b) of the Clean Water Act (CWA). Section 316(b) addresses the impingement and entrainment of fish by cooling-water intake structures used principally by steam electric power plants. The framework is motivated by examining the role of adverse environmental impacts (AEIs) in the context of Section 316(b) decision making. AEI is mentioned in Section 316(b), but not defined. While various AEI options have been proposed over the years, none has been formalized through environmental regulations nor universally accepted. Using a multiple values approach from decision analysis, AEIs are characterized as measurement criteria for ecological impacts. Criteria for evaluating AEI options are identified, including modeling and assessment issues, the characterization of ecological value, regulatory implementation, and the treatment of uncertainty. Motivated by the difficulties in defining AEI once and for all, a framework is introduced to compare options for 316(b) decision making. Three simplified policy options are considered, each with a different implicit or explicit AEI approach: (1) a technology-driven rule based on a strict reading of the 316(b) regulatory text, and for which any impingement and entrainment count as AEI, (2) a complementary, open-ended risk-assessment process for estimating population effects with AEI characterized on a site-specific basis, and (3) an intermediate position based on proxy measures such as specially constructed definitions of littoral zone, sensitive habitat, or water body type. The first two proposals correspond roughly to responses provided, respectively, by the Riverkeeper environmental organization and the Utility Water Act Group to the U.S. Environmental Protection Agency (EPA)’s proposed 316(b) new facilities rule of August 2000; the third example is a simplified form of the EPA’s proposed August 2000 new facilities rule itself. The simplified policy positions are compared using the three dimensions of the comparative policy framework: (1) the role of CWA philosophy or vision, such as the use of technology-forcing rules, (2) regulatory policy implementation, and (3) the role for scientific information and the knowledge base. Strengths and weaknesses of all three 316(b) policy approaches are identified. The U.S. EPA’s final new facilities rule of November 2001 is briefly characterized using the comparative policy framework and used to further illustrate the approach

Initial validation of the general attitudes towards Artificial Intelligence Scale

Author Accepted Manuscript with Appendix A (Sources of News Stories) and Appendix B (General Attitudes Towards Artificial Intelligence Scale, with instructions and scoring). For data files, please follow the DOI https://doi.org/10.1016/j.chbr.2020.100014 to the publisher's site. This article is available Open Access via the Publisher's site: https://www.sciencedirect.com/science/article/pii/S2451958820300142A new General Attitudes towards Artificial Intelligence Scale (GAAIS) was developed. The scale underwent initial statistical validation via Exploratory Factor Analysis, which identified positive and negative subscales. Both subscales captured emotions in line with their valence. In addition, the positive subscale reflected societal and personal utility, whereas the negative subscale reflected concerns. The scale showed good psychometric indices and convergent and discriminant validity against existing measures. To cross-validate general attitudes with attitudes towards specific instances of AI applications, summaries of tasks accomplished by specific applications of Artificial Intelligence were sourced from newspaper articles. These were rated for comfortableness and perceived capability. Comfortableness with specific applications was a strong predictor of general attitudes as measured by the GAAIS, but perceived capability was a weaker predictor. Participants viewed AI applications involving big data (e.g. astronomy, law, pharmacology) positively, but viewed applications for tasks involving human judgement, (e.g. medical treatment, psychological counselling) negatively. Applications with a strong ethical dimension led to stronger discomfort than their rated capabilities would predict. The survey data suggested that people held mixed views of AI. The initially validated two-factor GAAIS to measure General Attitudes towards Artificial Intelligence is included in the Appendix