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Workshop
Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy
Alina Oprea · Avigdor Gal · Eren K. · Isabelle Moulinier · Jiahao Chen · Manuela Veloso · Senthil Kumar · Tanveer Faruquie

Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West 205 - 207
Event URL: https://sites.google.com/view/robust-ai-in-fs-2019 »

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.

Author Information

Alina Oprea (Northeastern University)
Avigdor Gal (Technion -- Israel Institute of Technology)
Eren K. (BoA/Columbia University)
Isabelle Moulinier (Capital One)
Jiahao Chen (JPMorgan Chase & Co.)

Jiahao Chen is a data science manager working in Capital One New York specializing in emerging technologies and university partnerships. He is currently the lead for the Banking in Explainable Algorithms Research (BEAR) group, focusing on FATES-related machine learning topics and their relation with banking regulations surrounding fair lending and explainability of credit decisioning to customers and regulators. Prior to joining Capital One in 2017, Jiahao was a Research Scientist at MIT CSAIL leading the Julia Lab, focusing on applications of the Julia programming language to various scientific data science problems and challenges in parallel computing and scientific computing.

Manuela Veloso (JPMorgan and Carnegie Mellon University)
Senthil Kumar (Capital One)

Senthil Kumar is a Director of Data Science at Capital One where he applies Machine Learning and AI to various business problems. Prior to joining Capital One, he was at Bell Labs where he developed and managed several successful products that have been licensed around the world. He has published over 30 papers and holds 6 patents. Most recently, he co-organized the KDD 2017 Workshop on Anomaly Detection in Finance, the 2018 NeurIPS Workshop on Challenges and Opportunities of AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, the 2019 ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, and the 2019 2nd KDD Workshop on Anomaly Detection in Finance.

Tanveer Faruquie (Capital One)

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