Data cards: Purposeful and transparent dataset documentation for responsible ai
As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that give nuance to …
downstream tasks, the complexity of understanding multi-modal datasets that give nuance to …
Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
Datasets that power machine learning are often used, shared, and reused with little visibility
into the processes of deliberation that led to their creation. As artificial intelligence systems …
into the processes of deliberation that led to their creation. As artificial intelligence systems …
Interactive model cards: A human-centered approach to model documentation
Deep learning models for natural language processing (NLP) are increasingly adopted and
deployed by analysts without formal training in NLP or machine learning (ML). However, the …
deployed by analysts without formal training in NLP or machine learning (ML). However, the …
Advances, challenges and opportunities in creating data for trustworthy AI
As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
Lessons from archives: Strategies for collecting sociocultural data in machine learning
ES Jo, T Gebru - Proceedings of the 2020 conference on fairness …, 2020 - dl.acm.org
A growing body of work shows that many problems in fairness, accountability, transparency,
and ethics in machine learning systems are rooted in decisions surrounding the data …
and ethics in machine learning systems are rooted in decisions surrounding the data …
Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced dataset annotation
Human annotated data plays a crucial role in machine learning (ML) research and
development. However, the ethical considerations around the processes and decisions that …
development. However, the ethical considerations around the processes and decisions that …
The model card authoring toolkit: Toward community-centered, deliberation-driven AI design
There have been increasing calls for centering impacted communities–both online and
offline–in the design of the AI systems that will be deployed in their communities. However …
offline–in the design of the AI systems that will be deployed in their communities. However …
Accounting for privacy in citizen science: Ethical research in a context of openness
In citizen science, volunteers collect and share data with researchers, other volunteers, and
the public at large. Data shared in citizen science includes information on volunteer location …
the public at large. Data shared in citizen science includes information on volunteer location …
Ai ethics—a bird's eye view
M Christoforaki, O Beyan - Applied Sciences, 2022 - mdpi.com
The explosion of data-driven applications using Artificial Intelligence (AI) in recent years has
given rise to a variety of ethical issues regarding data collection, annotation, and processing …
given rise to a variety of ethical issues regarding data collection, annotation, and processing …
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
N Sambasivan, S Kapania, H Highfill… - proceedings of the …, 2021 - dl.acm.org
AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …
Data quality carries an elevated significance in high-stakes AI due to its heightened …