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Recent regulatory measures such as the European Union’s AI Act re-quire artificial intelligence (AI) systems to be explainable. As such, under-standing how explainability impacts human-AI interaction and pinpoint-ing the specific circumstances and groups affected, is imperative. In this study, we devise a formal framework and conduct an empirical investiga-tion involving real estate agents to explore the complex interplay between explainability of and delegation to AI systems. On an aggregate level, our findings indicate that real estate agents display a higher propensity to delegate apartment evaluations to an AI system when its workings are explainable, thereby surrendering control to the machine. However, at an individual level, we detect considerable heterogeneity. Agents possess-ing extensive domain knowledge are generally more inclined to delegate decisions to AI and minimize their effort when provided with explana-tions. Conversely, agents with limited domain knowledge only exhibit this behavior when explanations correspond with their preconceived no-tions regarding the relationship between apartment features and listing prices. Our results illustrate that the introduction of explainability in AI systems may transfer the decision-making control from humans to AI under the veil of transparency, which has notable implications for policy makers and practitioners that we discuss.
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.