https://psyarxiv.com/4ynkp/ Surveys around the world show that the public perceives artificial intelligence (AI) as a double-edged sword: As a risk but also as an opportunity. However, if and how this ambiguous perception of AI relates to people’s willingness to use … Continued
https://psyarxiv.com/743hn/ Decision makers such as industrial and organizational psychologists often combine multiple pieces of information to make performance predictions and hiring decisions. More valid performance predictions are made when information is combined algorithmically (mechanical prediction) rather than in the decision-maker’s mind … Continued
https://openai.com/blog/our-approach-to-alignment-research/ Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can … Continued
nesslabs.com/liminal-creativity?utm_source=rss&utm_medium=rss&utm_campaign=liminal-creativity Liminality (from the Latin word līmen, “threshold”) is the ambiguity that emerges in the middle of a fundamental transition. Liminality is the “in-between”, where the space and the participants no longer hold their past status, but have not yet … Continued
https://lastweekin.ai/p/ai-chatbots?triedSigningIn=true Recent advances in large-language models (LLMs) will probably power the next generation of chatbots. However, as Meta’s BlenderBot 3 public demo shows, making AI chatbots powerful enough to be useful while avoiding harmful responses is a nontrivial task. While LLMs will … Continued
https://www.worksinprogress.co/issue/biases-the-wrong-model/ Behavioural economics assumes a logically consistent model of human behaviour, then seeks to explain why actual human behaviour “deviates” from this model. It blames an ever-longer list of systemic “biases” in our decision-making methods. This cannot be right. Where … Continued
Wise decisions are often guided by an accurate understanding of the expected values of different possible choices. In social contexts, wisdom comes from understanding how others are likely to respond to one’s actions, enabling people to make choices that maximize … Continued
Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of … Continued
In this work, we empirically examine human-AI decision-making in the presence of explanations based on estimated outcomes. This type of explanation provides a human decision-maker with expected consequences for each decision alternative at inference time – where the estimated outcomes … Continued
Human-centered AI considers human experiences with AI performance. While abundant research has been helping AI achieve superhuman performance either by fully automatic or weak supervision learning, fewer endeavors are experimenting with how AI can tailor to humans’ preferred skill level … Continued