Publicação
Human-AI Collaboration: Understanding the Dynamics of Decision-Making with Explainable AI
| datacite.subject.fos | FACULDADE DE CIÊNCIAS, FACULDADE DE LETRAS, FACULDADE DE MEDICINA, FACULDADE DE PSICOLOGIA | pt_PT |
| dc.contributor.advisor | Silva, João Carlos Balsa da, 1965- | |
| dc.contributor.advisor | Marques, Marta Moreira | |
| dc.contributor.author | Santana, Mafalda Joana Torres | |
| dc.date.accessioned | 2025-01-27T11:34:18Z | |
| dc.date.available | 2025-01-27T11:34:18Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | 2024 | |
| dc.description | Tese de mestrado, Ciência Cognitiva, 2024, Universidade de Lisboa, Faculdade de Ciências | pt_PT |
| dc.description.abstract | Considering the rapid development of Artificial Intelligence (AI) models like ChatGPT, Copilot or Gemini in recent years, research on how these models influence their users has become essential - not only for user safety but to guide the future development of more efficient models. This study investigates the impact that predictions and explanations acquired from ChatGPT have on human decision-making, with a focus on task complexity, participant confidence, and demographic variables. To evaluate this impact, an experimental study was conducted with 53 participants, using a convenience sampling method. Using Qualtrics, the participants, primarily university students, were divided into two groups: Easy Decision-Making (EDM) and Complex Decision-Making (CDM). Each group was subdivided into three subgroups: those receiving only AI predictions, those receiving predictions with explanations, and a control group with no AI assistance. Performance was measured in terms of decision accuracy, alignment with AI predictions, and confidence, then compared between groups and subgroups. Additionally, demographic data, participants' attitudes towards technology and AI, and perceived relevance of information were collected to explore how these factors might influence the effectiveness of AI assistance and to test the awareness of this influence. Although no statistically significant differences were found between the subgroups, the analyses indicate a tendency for AI assistance to be more effective in more complex tasks, while explanations may be more beneficial in simpler scenarios. The study also highlighted that explanations can reduce ambiguity in decision-making, aligning participants' decisions more closely with AI predictions, even if they are unaware of this influence. Demographic factors did not significantly influence AI's impact, though younger participants generally performed better, possibly due to greater familiarity with technology. Attitudes towards AI also played a role, with scepticism boosting confidence levels, while partial trust in AI reduced it. Future research with larger, more diverse samples is recommended. | pt_PT |
| dc.identifier.tid | 203880641 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/97789 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Inteligência Artificial Explicável | pt_PT |
| dc.subject | Tomada de Decisão na Incerteza | pt_PT |
| dc.subject | Colaboração Humano-IA | pt_PT |
| dc.subject | Influencia Demográfica na Inteligência Artificial | pt_PT |
| dc.subject | Confiança com Inteligência Artificial | pt_PT |
| dc.subject | Teses de mestrado - 2024 | pt_PT |
| dc.title | Human-AI Collaboration: Understanding the Dynamics of Decision-Making with Explainable AI | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Tese de mestrado em Ciência Cognitiva | pt_PT |