Search

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

UNSTRUCTURED Nowadays, adolescents have more access to electronic devices and the internet than ever before. The fast adoption of social networking for everyday life purposes has made it difficult for them to refrain from being in a perpetual state of connectivity. In parallel with that, a mental-health epidemic affecting this same generation has emerged, linking the excessive use of technology to the development of mood-related disorders like depression and anxiety. That being said, efforts are being made, by researchers and clinicians, in developing online tools, like chatbots, to provide a much-needed support to younger individuals in their mental health struggles. The purpose of this research is to help improve the efficacy of online mental-health interventions by exploring how variations of question formats lead to greater levels of self-disclosure, which, in turn, improve the efficacy of the mental health intervention. This study explores how different question formats affect adolescents’ likelihood to self-disclose when answering health-related questions. For that, we evaluated the effect of variations in context, language and emojis on the likelihood to provide answers. In order to verify our hypotheses, we conducted a within-subject online survey with teenagers (N=125). Results showed that there is a significant correlation between the three independent variables (i) context, (ii) emojis and (iii) language, and adolescents’ self-disclosure for health-related questions. Several theoretical and managerial implications of this study are outlined in this paper.

The Michigan Stroke Transitions Trial (MISTT) tested whether in-home social work case management (SWCM) or SWCM combined with access to a website providing stoke-related information improved outcomes relative to usual care for patients discharged home post-stroke and their caregivers. The aims of this secondary analysis are 1) to describe the actual support social work case managers (SWCM) provided to MISTT participants and 2) use select case studies to illustrate the relationship between SWCM and quantitative patient and caregiver outcomes. Data for the study were derived from SWCM case notes on 157 patients and their caregivers who received the MISTT intervention. Case notes were coded in two steps with a subset of cases coded by two researchers and reviewed for interrater reliability in each step. The first round of coding was guided by primary SWCM intervention goals. The second round of coding identified SWCM sub-themes within each primary goal. Key themes indicate SWCMs aided with understanding the post-hospitalization period, helped patients navigate a range of systems and services, identified needs and supported patient goals, provided psychosocial support, and centered support on stroke recovery and prevention. Case studies illustrate ways in which SWCM were key supports during the transition period, but that support does not cleanly align with quantitative findings from patient-reported outcomes. This study aligns with a growing body of work documenting the complexity of transitions of care and has implications for how we support patients and caregivers as they move from inpatient to outpatient care and measure outcomes.