Live streaming has become an important feature on social media platforms like Facebook and Instagram. More and more entrepreneurs (content creators) are competing in live streaming platforms like Twitch to maximize the attention they receive from consumers (viewers). In this competitive landscape, it is crucial for entrepreneurs to develop and provide new compelling content that can maximize the consumers’ attention and aid the discovery of their content. We adopt an exploration-exploitation framework and assess the four strategies these new entrepreneurs could use to attract viewership and position themselves on Twitch: exploration, exploitation, learning from viewers, and their neighbor streamers. We combined the natural language processing techniques with theory-driven measures to accomplish this. Using our proposed measures, we estimate the utility of consumers from these different strategies using the discrete choice demand model.
Mental health care has become a concern globally, and the challenges of providing and accessing appropriate care are only magnified by a dearth of available counselors. At the same time, Large Language Models (LLMs) are anticipated to hold transformative potential for numerous industries including healthcare. LLM’s exceptional capacity to offer individually tailored recommendations derived from massive medical knowledge can facilitate access to affordable mental health services. Naturally, an inquiry arises regarding how human counselors respond to the emergence of AI counselors. We focus on an online peer counseling forum in China wherein expert and layperson counselors offer support to mental health care seekers through question-and-answer (Q&A). In 2023, an LLM-powered chatbot was integrated into the forum to address inquiries from seekers, providing an excellent context for us to study changes in the behavior of incumbent human counselors in the presence of an AI chatbot. Leveraging the (unanticipated) introduction of the chatbot as a natural experiment, we investigate how human counselors re-act to the AI chatbot in terms of the level of engagement, quality of assistance and counseling strategies.
Recent research at the intersection of AI explainability and fairness has focused on how explanations can improve human-plus-AI task performance as assessed by fairness measures. We propose to characterize what constitutes an explanation that is itself "fair" -- an explanation that does not adversely impact specific populations. We formulate a novel evaluation method of "fair explanations" using not just accuracy and label time, but also psychological impact of explanations on different user groups across many metrics (mental discomfort, stereotype activation, and perceived workload). We apply this method in the context of content moderation of potential hate speech, and its differential impact on Asian vs. non-Asian proxy moderators, across explanation approaches (saliency map and counterfactual explanation). We find that saliency maps generally perform better and show less evidence of disparate impact (group) and individual unfairness than counterfactual explanations.
Information on illicit drug trafficking networks can be obtained by interviewing or interrogating people working in the illegal business. Nevertheless, it usually represents only a part of the network, which makes it hard to figure out the entire network structure. Then, a trustworthy inference on possible connections in the network based on the observed or reported links will be helpful to locate previously unknown trafficking routes and eventually will be beneficial for disrupting illicit drug supply chains. To do that, this talk introduces a specific method known as positive and unlabeled (PU) learning, which considers a binary classification problem using data composed of positive and unlabeled examples without true negative samples. Afterward, a tentative plan of its application will be briefly discussed to predict unreported links of cocaine supply chains in Colombia with associated covariates data.