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Found 6 papers, 2 papers with code
LUCID-GAN: Conditional Generative Models to Locate Unfairness
Most group fairness notions detect unethical biases by computing statistical parity metrics on a model's output.
LUCID: Exposing Algorithmic Bias through Inverse Design
The results show that by shifting the focus towards equality of treatment and looking into the algorithm's internal workings, the canonical sets are a valuable addition to the toolbox of algorithmic fairness evaluation.
Simulation Intelligence: Towards a New Generation of Scientific Methods
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
Institutionalising Ethics in AI through Broader Impact Requirements
In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research.
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development.
Computers and Society
Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society
One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research.
