NegotiAge: Development and pilot testing of an artificial intelligence-based family caregiver negotiation program.

Background: Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial-intelligence negotiation training program, NegotiAge, for family caregivers.

Methods: We convened negotiation experts, a geriatrician, a social worker, and community-based family caregivers.

These Aren't The Droids You Are Looking for: Promises and Challenges for the Intersection of Affective Science and Robotics/AI.

AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do.

Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers.

Background: Family caregivers of older people with Alzheimer's dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions).

The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective.

In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control.

The power to harm: AI assistants pave the way to unethical behavior.

Advances in artificial intelligence (AI) enable new ways of exercising and experiencing power by automating interpersonal tasks such as interviewing and hiring workers, managing and evaluating work, setting compensation, and negotiating deals. As these techniques become more sophisticated, they increasingly support personalization where users can "tell" their AI assistants not only what to do, but how to do it: in effect, dictating the ethical values that govern the assistant's behavior. Importantly, these new forms of power could bypass existing social and regulatory checks on unethical behavior by introducing a new agent into the equation.

Designing Man's New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance.

To understand how to improve interactions with dog-like robots, we evaluated the importance of "dog-like" framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e.

Risk of Injury in Moral Dilemmas With Autonomous Vehicles.

As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e.

The rise of affectivism.

Research over the past decades has demonstrated the explanatory power of emotions, feelings, motivations, moods, and other affective processes when trying to understand and predict how we think and behave. In this consensus article, we ask: has the increasingly recognized impact of affective phenomena ushered in a new era, the era of affectivism?

Heuristic thinking and altruism toward machines in people impacted by COVID-19.

Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19-as measured by a post-traumatic stress disorder scale-show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans.

Emotion Regulation in the Prisoner's Dilemma: Effects of Reappraisal on Behavioral Measures and Cardiovascular Measures of Challenge and Threat.

The current study examines cooperation and cardiovascular responses in individuals that were defected on by their opponent in the first round of an iterated Prisoner's Dilemma. In this scenario, participants were either primed with the emotion regulation strategy of reappraisal or no emotion regulation strategy, and their opponent either expressed an amused smile or a polite smile after the results were presented. We found that cooperation behavior decreased in the no emotion regulation group when the opponent expressed an amused smile compared to a polite smile.

Human Cooperation When Acting Through Autonomous Machines.

Recent times have seen an emergence of intelligent machines that act autonomously on our behalf, such as autonomous vehicles. Despite promises of increased efficiency, it is not clear whether this paradigm shift will change how we decide when our self-interest (e.g.

Automatic Behavior Analysis During a Clinical Interview with a Virtual Human.

SimSensei is a Virtual Human (VH) interviewing platform that uses off-the-shelf sensors (i.e., webcams, Microsoft Kinect and a microphone) to capture and interpret real-time audiovisual behavioral signals from users interacting with the VH system.

Reading people's minds from emotion expressions in interdependent decision making.

How do people make inferences about other people's minds from their emotion displays? The ability to infer others' beliefs, desires, and intentions from their facial expressions should be especially important in interdependent decision making when people make decisions from beliefs about the others' intention to cooperate. Five experiments tested the general proposition that people follow principles of appraisal when making inferences from emotion displays, in context. Experiment 1 revealed that the same emotion display produced opposite effects depending on context: When the other was competitive, a smile on the other's face evoked a more negative response than when the other was cooperative.

User-state sensing for virtual health agents and telehealth applications.

Nonverbal behaviors play a crucial role in shaping outcomes in face-to-face clinical interactions. Experienced clinicians use nonverbals to foster rapport and "read" their clients to inform diagnoses. The rise of telemedicine and virtual health agents creates new opportunities, but it also strips away much of this nonverbal channel.

Socially anxious people reveal more personal information with virtual counselors that talk about themselves using intimate human back stories.

In this paper, we describe our findings from research designed to explore the effect of virtual human counselors' self-disclosure using intimate human back stories on real human clients' social responses in psychological counseling sessions. To investigate this subject, we designed an experiment involving two conditions of the counselors' self-disclosure: human back stories and computer back stories. We then measured socially anxious users' verbal self-disclosure.

People like virtual counselors that highly-disclose about themselves.

In this paper, we describe our findings from research designed to explore the effect of self-disclosure between virtual human counselors (interviewers) and human users (interviewees) on users' social responses in counseling sessions. To investigate this subject, we designed an experiment involving three conditions of self-disclosure: high-disclosure, low-disclosure, and non-disclosure. We measured users' sense of co-presence and social attraction to virtual counselors.