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Investigating Responsible AI for Scientific Research: An Empirical Study
Scientific research organizations that are developing and deploying Artificial Intelligence (AI) systems are at the intersection of technological progress and ethical considerations.
A Vision for Operationalising Diversity and Inclusion in AI
The growing presence of Artificial Intelligence (AI) in various sectors necessitates systems that accurately reflect societal diversity.
AI for All: Operationalising Diversity and Inclusion Requirements for AI Systems
As Artificial Intelligence (AI) permeates many aspects of society, it brings numerous advantages while at the same time raising ethical concerns and potential risks, such as perpetuating inequalities through biased or discriminatory decision-making.
The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills
The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements.
Challenges and Solutions in AI for All
Artificial Intelligence (AI)'s pervasive presence and variety necessitate diversity and inclusivity (D&I) principles in its design for fairness, trust, and transparency.
Exploring Qualitative Research Using LLMs
We compared the results with human classification and reasoning.
Diversity and Inclusion in Artificial Intelligence
To date, there has been little concrete practical advice about how to ensure that diversity and inclusion considerations should be embedded within both specific Artificial Intelligence (AI) systems and the larger global AI ecosystem.
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.
Dynamic Visual Analytics for Elicitation Meetings with ELICA
Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand.
