AI and Digital Humanities: Inclusive Approaches and Ethical Considerations

The Evolving Landscape of AI and Digital Humanities

The recent advancements in the intersection of Artificial Intelligence (AI) and Digital Humanities are reshaping traditional research methodologies, particularly in fields like labour history and global inequalities. The integration of AI technologies is enabling historians to quantify and analyze data that were previously inaccessible or underutilized, thereby uncovering new insights into historical phenomena. This shift is not only enhancing the precision and depth of historical research but also fostering a more inclusive approach to data collection and analysis, ensuring that marginalized voices are heard.

In the realm of AI development, there is a growing recognition of the need for diversity and inclusion at every stage of the AI pipeline. This includes not only the diversity of data used to train models but also the diversity of the workforce developing these technologies. Efforts are being made to ensure that AI systems are designed to be inclusive of all populations, addressing biases and ensuring equitable outcomes. This movement is crucial as AI systems increasingly influence decision-making processes in various sectors.

Moreover, the implementation of AI in border management and migration control is raising significant ethical concerns, particularly around privacy, discrimination, and the right to a fair trial. As AI technologies become more integrated into these critical areas, it is imperative to develop robust frameworks for evaluating and mitigating the associated risks to human rights.

Noteworthy Developments

  • Global Inequalities in AI Production: This study highlights the often-overlooked labor conditions in AI data work, particularly in lower-income countries, revealing a complex web of economic dependencies and inequalities.
  • AI for Everyone: This paper advocates for a more inclusive approach to AI development, emphasizing the need for diverse representation at every stage of the AI pipeline to ensure equitable outcomes.
  • Trustworthy AI Evaluation: A comprehensive survey on evaluation criteria for Trustworthy AI provides a new classification system, addressing the need for standardized criteria in AI governance.

Sources

Digital Humanities in the TIME-US Project: Richness and Contribution of Interdisciplinary Methods for Labour History

Global Inequalities in the Production of Artificial Intelligence: A Four-Country Study on Data Work

Challenges and Opportunities: Implementing Diversity and Inclusion in Software Engineering University Level Education in Finland

Em\'ilias Podcast -- Mulheres na Computa\c{c}\~ao: Ampliando Horizontes e Inspirando Carreiras em STEM

Why AI Is WEIRD and Should Not Be This Way: Towards AI For Everyone, With Everyone, By Everyone

A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence

Automated decision-making and artificial intelligence at European borders and their risks for human rights

From Efficiency to Equity: Measuring Fairness in Preference Learning

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