How should a virtual agent present psychoeducation?

BACKGROUND AND OBJECTIVE: With the rise of autonomous e-mental health applications, virtual agents can play a major role in improving trustworthiness, therapy outcome and adherence. In these applications, it is important that patients adhere in the sense that they perform the tasks, but also that they adhere to the specific recommendations on how to do them well. One important construct in improving adherence is psychoeducation, information on the why and how of therapeutic interventions. In an e-mental health context, this can be delivered in two different ways: verbally by a (virtual) embodied conversational agent or just via text on the scree

Deselling: Cross-Selling Without Upsetting Customers

To boost revenue, many firms are encouraging their service salespeople to cross-sell while providing a service; but cross-selling can upset customers. How, then, may firms effectively cross-sell without upsetting customers? The authors address this question by introducing the concept of deselling behaviors, defined as service salespeople’s actions that are incongruent with persuasive intent. They combine insights gleaned from 101 inconspicuous, fly-on-the-wall videos of actual service salesperson-customer exchanges with theoretical underpinnings of the persuasion knowledge model and reactance theory to advance a novel conceptual framework of deselling behaviors. Their framework advances prior literature by illuminating three unique sets of deselling behaviors that reduce customers’ reactance to cross-selling recommendations, and thereby enhance ambidextrous effects (i.e., enhance cross-selling performance and customer satisfaction): 1) nonverbal source signals (e.g., tangibilizing cooperativeness and passive proxemic positioning), 2) verbal source signals (e.g., proactively discounting and attribution externalizing), and 3) verbal message signals (e.g., vividly educating and piecemeal recommending). Further, they delineate how enacting deselling behaviors prior to a cross-selling episode may impact the relationships between deselling behaviors during a cross-selling episode and reactance to cross-selling recommendations

AI loyalty: A New Paradigm for Aligning Stakeholder Interests

When we consult with a doctor, lawyer, or financial advisor, we generally assume that they are acting in our best interests. But what should we assume when it is an artificial intelligence (AI) system that is acting on our behalf? Early examples of AI assistants like Alexa, Siri, Google, and Cortana already serve as a key interface between consumers and information on the web, and users routinely rely upon AI-driven systems like these to take automated actions or provide information. Superficially, such systems may appear to be acting according to user interests. However, many AI systems are designed with embedded conflicts of interests, acting in ways that subtly benefit their creators (or funders) at the expense of users. To address this problem, in this paper we introduce the concept of AI loyalty. AI systems are loyal to the degree that they are designed to minimize, and make transparent, conflicts of interest, and to act in ways that prioritize the interests of users. Properly designed, such systems could have considerable functional and competitive - not to mention ethical - advantages relative to those that do not. Loyal AI products hold an obvious appeal for the end-user and could serve to promote the alignment of the long-term interests of AI developers and customers. To this end, we suggest criteria for assessing whether an AI system is sufficiently transparent about conflicts of interest, and acting in a manner that is loyal to the user, and argue that AI loyalty should be considered during the technological design process alongside other important values in AI ethics such as fairness, accountability privacy, and equity. We discuss a range of mechanisms, from pure market forces to strong regulatory frameworks, that could support incorporation of AI loyalty into a variety of future AI systems

Sound Trust and the Ethics of Telecare

The adoption of web-based telecare services has raised multifarious ethical concerns, but a traditional principle-based approach provides limited insight into how these concerns might be addressed and what, if anything, makes them problematic. We take an alternative approach, diagnosing some of the main concerns as arising from a core phenomenon of shifting trust relations that come about when the physician plays a less central role in the delivery of care, and new actors and entities are introduced. Correspondingly, we propose an applied ethics of trust based on the idea that patients should be provided with good reasons to trust telecare services, which we call sound trust. On the basis of this approach, we propose several concrete strategies for safeguarding sound trust in telecare

The Societal Implications of Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is an avenue of research in Artificial Intelligence (AI) that has received increasing attention within the research community in recent years, and is beginning to show potential for real-world application. DRL is one of the most promising routes towards developing more autonomous AI systems that interact with and take actions in complex real-world environments, and can more flexibly solve a range of problems for which we may not be able to precisely specify a correct ‘answer’. This could have substantial implications for people’s lives: for example by speeding up automation in various sectors, changing the nature and potential harms of online influence, or introducing new safety risks in physical infrastructure. In this paper, we review recent progress in DRL, discuss how this may introduce novel and pressing issues for society, ethics, and governance, and highlight important avenues for future research to better understand DRL’s societal implications.&#x0D; &#x0D; &#x0D; &#x0D; This article appears in the special track on AI and Society.&#x0D; &#x0D; &#x0D; </jats:p

Credibility: A multidisciplinary framework

No Abstract.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61241/1/1440410114_ftp.pd