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Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels ...

Responsible AI is built upon a set of principles that prioritize fairness, transparency, accountability, and inclusivity in AI development and deployment. As AI systems become increasingly sophisticated, including the explosion of generative AI, there is ...

The ACM KDD 2024 Health Day theme, "Building Health AI Ecosystem: From Data Harmonization to Knowledge Discovery," highlights the transformative potential of AI-driven ecosystems in healthcare, translational biomedical research, and basic biological ...

The recent advances in artificial intelligence (AI) have captivated the attention of many while raising the alarm among activists and policymakers. Although AI has its benefits, it has simultaneously contributed to increased polarization, the ...

Value Sensitive Design (VSD) is a framework for integrating human values throughout the technology design process. In parallel, Responsible AI (RAI) advocates for the development of systems aligning with ethical values, such as fairness and transparency. ...

Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been ...

Privacy protection for personal data and fairness in automated decisions are fundamental requirements for responsible Machine Learning. Both may be enforced through data preprocessing and share a common target: data should remain useful for a task, while ...

This work presents an argument for the use of specific documentation for the ethical development, use, and sharing of energy datasets, and an evaluation of current practice in the energy AI community. Drawing on a recently developed resource from the ...

The development of processes and tools for ethical, trustworthy, and legal AI is only beginning. At the same time, legal requirements are emerging in various jurisdictions, following a deluge of ethical guidelines. It is therefore key to explore the ...

From humanity's existential risks to safety risks in critical systems to ethical risks, responsible AI, as the saviour, has become a major research challenge with significant real-world consequences. However, achieving responsible AI remains elusive ...

Ethical guidelines are an asset for artificial intelligence(AI) development and conforming to them will soon be a procedural requirement once the EU AI Act gets ratified in the European parliament. However, developers often lack explicit knowledge on ...

Recently, the AI/ML research community has indicated an urgent need to establish Responsible AI (RAI) values and practices as part of the AI/ML lifecycle. Several organizations and communities are responding to this call by sharing RAI guidelines. ...

Leveraging pre-trained language models (PLMs) as initializers for efficient transfer learning has become a universal approach for text-related tasks. However, the models not only learn the language understanding abilities but also reproduce prejudices ...

In this era of Big Data and AI, expertise in multiple aspects of data, computing, and the domains of application is needed. This calls for teams of experts with different training and perspectives. Because data analysis can have serious ethical ...

This paper provides two distinct approaches to conduct an ethics assessment of AI in policing. Crucially, ethical scrutiny must engage all aspects of the policing context. The context for adopting AI is neither solely technical nor solely societal in ...

Algorithmic audits (or ‘AI audits’) are an increasingly popular mechanism for algorithmic accountability; however, they remain poorly defined. Without a clear understanding of audit practices, let alone widely used standards or regulatory guidance, ...

Existing fair ranking systems, especially those designed to be demographically fair, assume that accurate demographic information about individuals is available to the ranking algorithm. In practice, however, this assumption may not hold --- in real-...

A moral dilemma is a decision-making paradox without unambiguously acceptable or preferable options. This paper investigates if and how the virtual enactment of two renowned moral dilemmas---the Trolley and the Mad Bomber---influence decision-making when ...