# regulator

### Black box problem stunting ML adoption in default risk analysis

Difficulties in explaining machine learning (ML) models is causing concern as banks look to the technology for default risk analysis, according to market participants. "Many different types of'black-box' models have been developed out there even by banks claiming that they can accurately predict mortgage defaults. This is only partially true," said Panos Skliamis, chief executive officer at SPIN Analytics in an email. "[These models] usually target a relatively short-term horizon and their validation windows of testing remain actually in an environment too similar to that of the development samples. However, mortgage loans are almost always long-term and their lives extend to multiple economic cycles, while the entire world changes over time and several features of ML models severely influenced by these changes of the environment," he said.

### Explaining Machine Learning and Artificial Intelligence in Collections to the Regulator

There is significant growth in the application of machine learning (ML) and artificial intelligence (AI) techniques within collections as it has been proven to create countless efficiencies; from enhancing the results of predictive models, to powering AI bots that interact with customers leaving staff free to address more complex issues. At present, one of the major constraining factors to using this advanced technology is the difficulty that comes with explaining the decisions made by these solutions to regulators. This regulatory focus is unlikely to diminish, especially with the various examples of AI bias which continue to be uncovered within various applications, resulting in discriminatory behaviors towards different groups of people. While collections-specific regulations remain somewhat undefined on the subject, major institutions are resorting to their broader policy; namely that any decision needs be fully explainable. Although there are explainable Artificial Intelligence (xAI) techniques that can help us gain deeper insights from ML models such as FICO's xAI Toolkit, the path to achieving sign-off within an organization can be a challenge.

### From self-tuning regulators to reinforcement learning and back again

Machine and reinforcement learning (RL) are being applied to plan and control the behavior of autonomous systems interacting with the physical world -- examples include self-driving vehicles, distributed sensor networks, and agile robots. However, if machine learning is to be applied in these new settings, the resulting algorithms must come with the reliability, robustness, and safety guarantees that are hallmarks of the control theory literature, as failures could be catastrophic. Thus, as RL algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists be part of the conversation. The goal of this tutorial paper is to provide a jumping off point for control theorists wishing to work on RL related problems by covering recent advances in bridging learning and control theory, and by placing these results within the appropriate historical context of the system identification and adaptive control literatures.

### An X-ray was once between you and your doctor, but for how long?

A visit to the doctor seems one-on-one. But how will that feeling change when the data gleaned from that interaction takes on unprecedented value? It's a question that doctors and health regulators are grappling with as algorithms learn how to spot pneumonia, and health data becomes the chaff needed to train artificial intelligence. "Previously, the patient is agreeing to supply their very intimate personal information ... to the doctor to help with the diagnosis and management of their own health," said Jacob Jaremko, an associate professor in radiology and diagnostic imaging at the University of Alberta. You provide, for your own care, for your own benefit ... your data."

### How Silicon Valley's whiz-kids finally ran out of friends John Naughton

Remember the time when tech companies were cool? Once upon a time, Silicon Valley was the jewel in the American crown, a magnet for high IQ – and predominately male – talent from all over the world. Palo Alto was the centre of what its more delusional inhabitants regarded as the Florence of Renaissance 2.0. Parents swelled with pride when their offspring landed a job with the Googles, Facebooks and Apples of that world, where they stood a sporting chance of becoming as rich as they might have done if they had joined Goldman Sachs or Lehman Brothers, but without the moral odium attendant on investment backing. I mean to say, where else could you be employed by a company to which every president, prime minister and aspirant politician craved an invitation?