Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy

The financial services industry has unique needs for robustness when adopting artificial intelligence and machine learning (AI/ML). Many challenges can be described as intricate relationships between algorithmic fairness, explainability, privacy, data management, and trustworthiness. For example, there are ethical and regulatory needs to prove that models used for activities such as credit decisioning and lending are fair and unbiased, or that machine reliance does not cause humans to miss critical pieces of data. The use and protection of customer data necessitates secure and privacy-aware computation, as well as explainability around the use of sensitive data. Some challenges like entity resolution are exacerbated because of scale, highly nuanced data points and missing information.

On top of these fundamental requirements, the financial industry is ripe with adversaries who purport fraud, resulting in large-scale data breaches and loss of confidential information in the financial industry. The need to counteract malicious actors therefore calls for robust methods that can tolerate noise and adversarial corruption of data. However, recent advances in adversarial attacks of AI/ML systems demonstrate how often generic solutions for robustness and security fail, thus highlighting the need for further advances. The challenge of robust AI/ML is further complicated by constraints on data privacy and fairness, as imposed by ethical and regulatory concerns like GDPR.

This workshop aims to bring together researchers and practitioners to discuss challenges for AI/ML in financial services, and the opportunities such challenges represent to research communities. The workshop will consist of invited talks, panel discussions and short paper presentations, which will showcase ongoing research and novel algorithms resulting from collaboration of AI/ML and cybersecurity communities, as well as the challenges that arise from applying these ideas in domain-specific contexts.