Third-party evaluators perceive AI as more compassionate than expert humans

Empathy connects us but strains under demanding settings. This study explored how third parties evaluated AI-generated empathetic responses versus human responses in terms of compassion, responsiveness, and overall preference across four preregistered experiments. Participants (N = 556) read empathy prompts describing valenced personal experiences and compared the AI responses to select non-expert or expert humans.

The Importance of Empathy and the Challenges of Human Support

Empathy is crucial for fostering societal unity and effective communication. It allows individuals to balance their own interests with the wellbeing of others through shared experiences and emotions. It can promote cooperation, altruism, and helping behaviors, thereby strengthening social bonds. Psychologically, empathy also has a nourishing effect on its recipients, such that people feel validated, understood, and connected when others empathize with them.

Despite the positive impact of empathy on its recipients, the effort required to express empathy can be costly and burdensome to the empathizer. This can lead to a phenomenon known as empathy avoidance and compassion fatigue. This seems to be particularly apparent in clinical settings, where healthcare professionals may sacrifice some of their ability to empathize in order to avoid burnout, to manage personal distress, or to balance their emotional engagement with the need to effectively allocate resources to each client, particularly individuals with complex cases.

Mental Health as a Global Public Health Challenge

Mental illness is increasingly recognized as a substantial public health challenge worldwide. Mental health disorders represent one of the most prevalent illnesses globally and are closely linked to an increased risk of suicide. According to the World Health Organization (WHO), mental health issues cost approximately USD 2.5 trillion in 2010, with an estimated increase of USD 6.0 trillion predicted by 2030, as more than 350 million people are impacted by depression. In Australia alone, half of the population faces mental health challenges, with about 3000 individuals tragically ending their lives each year.

One consequence of these challenges is that empathy is in short supply, especially as the mental health sector struggles with accessible service and workforce shortages amid the increasing incidence of mental health disorders. This clearly indicates future economic strain on governments and requires new modes of intervention and prevention strategies to reduce mental illness and suicide.

Evaluating AI in Supportive Communication Contexts

In response to the gap between the supply and demand of empathy, scientists have asked if AI could provide consistent and quality supportive care. Despite arguments that AI cannot experience empathy or feel emotions, it can express empathy by generating responses or behaviors that appear to reflect empathic concern or the intention to alleviate distress. As such, scientists have begun exploring the effectiveness of AI powered by large language models in providing empathic support.

Experimental Results and Key Findings

The research experiments provided the following comparative data regarding AI and human-generated empathy:

  • Study 1: AI responses were preferred and rated as more compassionate compared to select human responders.
  • Study 2: This pattern of results remained when author identity was made transparent.
  • Study 3: Results remained consistent when AI was compared to expert crisis responders.
  • Study 4: Third parties perceived AI as being more responsive—conveying understanding, validation, and care—which partially explained AI’s higher compassion ratings.

Article Impact and Visibility Metrics

Based on the provided material, the reach of this research is evidenced by the following metrics:

  • Accesses: 59,000
  • Citations: 39
  • Altmetric: 278

These findings suggest that AI has robust utility in contexts requiring empathetic interaction, with the potential to address the increasing need for empathy in supportive communication contexts. This dataset and machine learning approach offer a critical resource for training and fine-tuning models to identify foundational triggers, offering valuable insights for practical interventions and future research in this domain.