Professional Judgment in an Era of Artificial Intelligence and Machine Learning

Though artificial intelligence (AI) in healthcare and education now accomplishes diverse tasks, there are two features that tend to unite the information processing behind efforts to substitute it for professionals in these fields: reductionism and functionalism. True believers in substitutive automation tend to model work in human services by reducing the professional role to a set of behaviors initiated by some stimulus, which are intended to accomplish some predetermined goal, or maximize some measure of well-being. However, true professional judgment hinges on a way of knowing the world that is at odds with the epistemology of substitutive automation. Instead of reductionism, an encompassing holism is a hallmark of professional practice—an ability to integrate facts and values, the demands of the particular case and prerogatives of society, and the delicate balance between mission and margin. Any presently plausible vision of substituting AI for education and health-care professionals would necessitate a corrosive reductionism. The only way these sectors can progress is to maintain, at their core, autonomous professionals capable of carefully intermediating between technology and the patients it would help treat, or the students it would help learn

A tractable model of buffer stock saving

We present a tractable model of the effects of nonfinancial risk on intertemporal choice. Our purpose is to provide a simple framework that can be adopted in fields like representative-agent macroeconomics, corporate finance, or political economy, where most modelers have chosen not to incorporate serious nonfinancial risk because available methods were too complex to yield transparent insights. Our model produces an intuitive analytical formula for target assets, and we show how to analyze transition dynamics using a familiar Ramsey-style phase diagram. Despite its starkness, our model captures most of the key implications of nonfinancial risk for intertemporal choice

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

The Role of Emotions in Human-AI Collaboration

Because AI has distinct abilities that enable the automation of tasks that usually require human intelligence, it can affect various emotions ranging from excitement about efficiency gains to the fear of being replaced. At the same time, emotions constitute key drivers of employee behavior and thus may determine whether and how AI users collaborate with AI. We raise the question of what AI users consider important in the light of human-AI collaboration and how these aspects relate to their emotional attitudes towards this technology. We address this research question by collecting longitudinal data regarding the AI implementation in three companies. We use qualitative coding to identify important aspects of human-AI collaboration from semi-structured interviews conducted at different stages of the AI implementation. Moreover, we use voice analysis to measure the interviewees’ emotional state while speaking about different aspects of human-AI collaboration

Reconsidering the classification of sweet taste liker phenotypes: a methodological review

Human ingestive behavior depends on myriad factors, including both sensory and non-sensory determinants. Of the sensory determinants, sweet taste is a powerful stimulus and liking for sweetness is widely accepted as an innate human trait. However, the universality of sweet-liking has been challenged. Sub-groups exhibiting strong liking (sweet likers) or having aversive responses to sweet taste (sweet dislikers) have been described, but the methods defining these phenotypes are varied and inconsistent across studies. Here, we explore the strengths and weaknesses of different methodological approaches in identifying sweet taste liker phenotypes in a comprehensive review. Prior studies (N = 71) using aqueous sucrose solution-based taste tests and a definition of two or more distinct hedonic responses reported between 1970 and 2017 were summarized. Broadly speaking, four different phenotyping methods have been used: 1. Interpretation (visual or statistical) of the shape of hedonic response curves, 2. Highest preference using ratings, 3. Average liking above mid-point or Positive/Negative average liking method, and 4. Highest preference via paired comparisons. Key methodological weaknesses included the use of subjective or arbitrary criteria as well as adoption of protocols unsuitable for large-scale implementation. Overall, we did not identify a method distinctly superior to the others. Given the role of both hedonics and reward in food intake, a better understanding of individual variations in sweet taste perception could clarify how sweet-liking interplays with obesity or addictive behaviors such as alcohol misuse and abuse. The development of a universally used statistically robust and less time-consuming classification method is needed

Integrating Multiple Criteria Decision-Making Models Into the Decision Support System Framework for Marketing Decisions

The paper focuses on integrating the multiple criteria decision making (MCDM) models within the decision support system (DSS) framework to encourage greater use of these models. A DSS framework and the criteria used for the choice of a model is discussed. Based on these criteria MCDM models generally used in the marketing field are evaluated. The possibility of using a mixture of MCDM models within the DSS framework is also explored. Following this, the role of the MCDM models in DSS is delineated. It is argued that, within the problem-solving process, the confluence of MCDM models and DSS plays a vital role in developing high-quality solutions