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- Enhancing diversity in clinical trials with AI and human . . .
Diversity in clinical trials is essential for ensuring that clinical research and the resulting treatments are effective for broader populations By combining AI with human expertise, tools like One Search offer a promising path forward and enable sponsors to better address the complex factors that influence patient participation in a study
- Examining inclusivity: the use of AI and diverse populations . . .
Background: Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or insufficiently represented within these systems
- Top Challenges of Artificial Intelligence (AI) in 2025
9 Ensuring Inclusivity and Diversity in AI A lack of representation within AI development teams often results in biased algorithms that fail to address the needs of diverse populations When development teams lack cultural, gender, or socioeconomic diversity, AI systems risk perpetuating inequalities and marginalizing underrepresented groups
- AI and the quest for diversity and inclusion: a systematic . . .
The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency However, diversity and inclusion (D I) considerations are significantly neglected in AI systems design, development, and deployment
- Diversity in AI - Towards a Problem Statement
The AI industry faces a lack of representation from diverse groups, which hinders the development of AI tools Diverse teams bring varied backgrounds and experiences, making them sensitive to different issues and designing AI tools accordingly Technical expertise is essential, but diverse perspectives provide unique viewpoints on data
- Inclusive AI: Representing Diverse Voices – Unlocking . . .
Explore the importance of inclusive AI in representing diverse voices and creating equitable technological solutions This article highlights how integrating varied perspectives reduces bias, fosters innovation, and builds trust It discusses challenges like data and algorithmic bias, underrepresentation in AI teams, and offers strategies for inclusive data collection, diverse development
- AI and Diversity, Equity, and Inclusion (DEI): Examining the . . .
The opaque nature of many AI systems, often referred to as "black boxes," poses a significant challenge in ensuring that these systems operate fairly and without unintended negative consequences If the decision-making processes of AI systems are not transparent, it becomes difficult to identify, understand, and rectify biases when they arise
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