Sifting the truth from AI-generated content should now be a core responsibility for public health institutions

A recent study by Dr. Hyungrae Noh critiques traditional moral frameworks for ascribing responsibility to human stakeholders and AI systems, instead proposing a distributed model of responsibility where duties are shared among both. The study emphasizes the need for human stakeholders to prevent AI from causing harm through monitoring ...

Researchers found that AI-generated content is perceived as higher quality than human-created content, but reveals the source of production can reduce the gap. Human oversight is still necessary to ensure AI-generated content is appropriate in sensitive contexts.

A study of UK-based Twitter tweets found that the top 1% of users generated 23% of anti-immigration content, with anti-immigration tweets spreading 1.66 times faster than pro-immigration ones. This concentration of power among a few users highlights the need for efforts to curb online hate content.

A University at Buffalo researcher found that Truth Social was as effective as Twitter in driving news attention for Donald Trump, despite its smaller user base. However, journalists embedded Trump's posts less frequently on Truth Social than on Twitter.

Research combines AI-generated disinformation with the repetitive truth effect to create persuasive content. A study found that while neither tactic convinced participants of conspiracy theories, AI-generated disinformation increased perceived consensus among both parties.

A study reveals that environmental responsibility boosts corporate financial performance by increasing sales, especially for large companies after COVID-19. Stronger environmental performance generates higher sales leading to improved returns on assets and equity.