Banks, consumer advocates and think tanks are weighing in to federal bank regulators about potential pitfalls in the use of artificial intelligence and machine learning in making loan decisions. In responses to regulators' call for comments, many expressed interest in an increased use of AI and machine learning in the banking business, along with caveats about fair lending and unlawful discrimination concerns. FinRegLab, a Washington-based research group that says it has launched a broad inquiry into the use of AI in financial services, told the agencies that machine learning could be "transformational," as current gaps "increase the cost or risk of serving particular consumer and small-business populations using traditional models and data." At the same time, the predictive power of machine learning models can increase potential risks "due to the models' greater complexity and to their potential to exacerbate historical disparities and flaws in underlying data," FinRegLab said. AI and machine learning might amplify patterns of historical discrimination and financial exclusion through reliance on flawed data or mistakes in development.

Banks and other financial firms are investing in "explainable" artificial intelligence that lets auditors and analysts trace how decisions about loans and other services are made by financial technologies, experts say. The increasing use of software with AI capabilities such as machine learning and data mining has automated banking operations, increasing efficiency and providing more services. But privacy and civil liberties groups contend that has come at a cost, with bias in the AI systems' algorithms leading to discrimination in the form of loans or other services denied based on sex or ethnicity. This perception of algorithmic bias is a big problem for banks, which are investing in technical solutions to solve the problem, Moutusi Sau, an analyst at research and advisory company Gartner Inc., told CQ Roll Call. That issue is known as the black box problem with AI systems: software decision-making processes that often are opaque to humans, making it difficult or impossible to determine how a decision was made.

Artificial intelligence-based tools can soon begin assisting patients with chronic conditions, the National Academy of Medicine said, describing some of the emerging applications of such technologies both for consumers and health care providers. New devices could help patients with heart disease, diabetes or depression, and with taking their medications, modifying their diet, wound care and injections, the academy said. Health experts also can use data drawn from wearable devices such as accelerometers, gyroscopes, microphones, cameras and smartphones for monitoring patients' health and predicting risks, the academy said. The report said startup companies with a focus on health and medicine have raised $4.3 billion to develop so-called smart clothing such as bras that can predict breast cancer risk and other clothes that can assess cardiac and lung conditions based on movement. In hospitals, clinicians are testing whether artificial intelligence technologies would allow them to personalize chemotherapy dosing -- a kind of precision medicine that's tailored to each patient.

Watch CQ Roll Call's senior writers Gopal Ratnam and Kate Ackley dispel some common misconceptions about artificial intelligence. They also help explain some yet-to-be mitigated risks with this still developing technology, and how you use Hollywood's favorite dystopian theme every day. Get breaking news alerts and more from Roll Call on your iPhone.

Covenant Eyes is not the only tech firm to play on these concerns, however. California-based X3watch, for example, offers a similar tracking and reporting feature, albeit one that works by creating a categorised list of the sites users visit that is then shared with their accountability partners. 'This is an opportunity to know and be known,' the X3watch website argues. 'Whether your chosen partner is a friend or a spouse, or you've come across explicit activity on your children's devices, the true goal is liberation that blossoms from open and honest relationships with others who are dedicated to your well-being.' An annual subscription to X3watch is currently priced at $70 (£54) per year.

Recently, the Victorian Government brought in new rules stating Victorian state schools will be banned from using facial recognition technology in classrooms unless they have the approval of parents, students and the Department of Education. Students may be justifiably horrified at the thought of being monitored as they move throughout the school during the day. But a roll marking system could be as simple as looking at a tablet or iPad once a day instead of being signed off on a paper roll. It simply depends on the implementation. Trials have already begun in independent schools in NSW and up to 100 campuses across Australia.

We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.

We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.

We report on the use of reinforcement learning with Cobot, a software agent residing in the well-known online community LambdaMOO. Our initial work on Cobot (Isbell et al.2000) provided him with the ability to collect social statistics and report them to users. Here we describe an application of RL allowing Cobot to take proactive actions in this complex social environment, and adapt behavior from multiple sources of human reward. After 5 months of training, and 3171 reward and punishment events from 254 different LambdaMOO users, Cobot learned nontrivial preferences for a number of users, modifing his behavior based on his current state. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.