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Found 7 papers, 0 papers with code
On the Societal Impact of Open Foundation Models
To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk.
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints.
On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services
Artificial intelligence (AI) continues to find more numerous and more critical applications in the financial services industry, giving rise to fair and ethical AI as an industry-wide objective.
Seven challenges for harmonizing explainability requirements
Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry.
Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses.
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable.
SIM-GAN: Adversarial Calibration of Multi-Agent Market Simulators.
In this paper, we present SIM-GAN -- a multi-agent simulator calibration method that allows to tune simulator parameters and to support more accurate evaluations of candidate trading algorithm.
