The United States Patent and Trademark Office (USPTO) today released a report titled "Public Views on Artificial Intelligence and Intellectual Property Policy." The new report represents the agency's firm commitment to keeping pace with this rapidly changing and critical technology in order to accelerate American innovation. "On February 11, 2019, President Trump signed Executive Order 13859 announcing the American Artificial Intelligence Initiative, our nation's strategy on artificial intelligence," said U.S. Secretary of Commerce Wilbur Ross. "As artificial intelligence technologies continue to advance, the United States will not cede leadership in global innovation. The Department of Commerce recognizes the importance of harnessing American ingenuity to advance and protect our economic security." "The USPTO has long been committed to ensuring our nation maintains its leadership in all areas of innovation, especially in emerging technologies such as artificial intelligence," said Andrei Iancu, Under Secretary of Commerce for Intellectual Property and Director of the USPTO.

The Biometrics Institute has released Three Laws of Biometrics to guide the development and deployment of biometric technologies. The idea is inspired by Isaac Asimov's Three Laws of Robotics, though the Laws themselves have obviously been updated to reflect concerns about a different technology in a different era. According to the Institute, the Three Laws of Biometrics are Policy, Process, and Technology (PPT), and they are meant to be applied in that specific order. First, biometrics developers should make sure that the thing they want to do follows certain legal and ethical principles, which is to say that it should respect the privacy and civil liberties of the people using it. After that, developers should have internal processes in place to make sure that those policies are followed, and only then should they proceed with the development of the technology itself.

Now a new investigation from Human Rights Watch has found that not only are children regularly added to CONARC, but the database also powers a live facial recognition system in Buenos Aires deployed by the city government. This makes the system likely the first known instance of its kind being used to hunt down kids suspected of criminal activity. "It's completely outrageous," says Hye Jung Han, a children's rights advocate at Human Rights Watch, who led the research. Buenos Aires first began trialing live facial recognition on April 24, 2019. Implemented without any public consultation, the system sparked immediate resistance.

Unexpectedly, the COVID-19 pandemic brought much of the world's activities to a stand-still. As the pandemic spread across the globe, with impacts across every country and business, organizations seek to remain operational, and adapt to the new normal. This pandemic created an unprecedented risky environment, where organizations must rapidly make decisions, grounded on trusted, timely and accurate data. Data governance may not seem to be the highest priority. From day one, data is being created, compiled, collected, stored and distributed.

This is an early and tentative document, intended to roughly summarize a line of thinking and to spur discussion and action among relevant individuals, organizations and communities. Many particulars discussed here are expected to evolve as more and more of the concepts described here move to practical realization. This is a living, evolving body of ideas. The introduction of AI tools and agents into all sectors of the economy, from factory robots to highly specialized electronic scientific brains, and the transition from narrow AI (domain specific, at best weakly autonomous) toward Artificial General Intelligence (broadly intelligent and strongly autonomous), are likely to be the biggest story of the next few decades. The tremendous promise and peril of these developments, which are already well underway, have been much discussed in fictional, media and intellectual spheres.

You may not realize it, but artificial intelligence (AI) is already enhancing our lives in a multitude of ways. AI systems already man our call centers, drive our cars, and take orders through kiosks at local fast food restaurants. In the days ahead, AI and machine learning will become a more prominent fixture, disrupting industries and extracting tediousness from our everyday lives. As we hand over larger chunks of our lives to the machines, we need to lift the hood to see what kind of ethics are driving them, and who is defining the rules of the road. Many CIOs have begun experimenting with AI in areas that may not be very visible to end users, such as automating warehouses.

When developing models for regulated decision making, sensitive features like age, race and gender cannot be used and must be obscured from model developers to prevent bias. However, the remaining features still need to be tested for correlation with sensitive features, which can only be done with the knowledge of those features. We resolve this dilemma using a fully homomorphic encryption scheme, allowing model developers to train linear regression and logistic regression models and test them for possible bias without ever revealing the sensitive features in the clear. We demonstrate how it can be applied to leave-one-out regression testing, and show using the adult income data set that our method is practical to run.

AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.

Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence research domain. They provide individuals with alternative scenarios and a set of recommendations to achieve a sought-after machine learning model outcome. Recently, the literature has identified desiderata of counterfactual explanations, such as feasibility, actionability and sparsity that should support their applicability in real-world contexts. However, we show that the literature has neglected the problem of the time dependency of counterfactual explanations. We argue that, due to their time dependency and because of the provision of recommendations, even feasible, actionable and sparse counterfactual explanations may not be appropriate in real-world applications. This is due to the possible emergence of what we call "unfortunate counterfactual events." These events may occur due to the retraining of machine learning models whose outcomes have to be explained via counterfactual explanation. Series of unfortunate counterfactual events frustrate the efforts of those individuals who successfully implemented the recommendations of counterfactual explanations. This negatively affects people's trust in the ability of institutions to provide machine learning-supported decisions consistently. We introduce an approach to address the problem of the emergence of unfortunate counterfactual events that makes use of histories of counterfactual explanations. In the final part of the paper we propose an ethical analysis of two distinct strategies to cope with the challenge of unfortunate counterfactual events. We show that they respond to an ethically responsible imperative to preserve the trustworthiness of credit lending organizations, the decision models they employ, and the social-economic function of credit lending.

Many have emphasized the need for data for artificial intelligence (AI) and machine learning (ML) algorithms, and metaphors from "data is the new oil" to "data is the new sun" further exacerbate the dire need for better data. However, one aspect of data that is often not explicitly mentioned in these circumstances is the role of master data and how it fundamentally impacts the quality of data that is driving the ML algorithms. In the spirit of paying tribute to management guru Peter Drucker, who's credited with the saying, "culture eats strategy for breakfast," this article explores: According to The DAMA Guide to the Data Management Body of Knowledge, master data represents "data about the business entities that provide context for business transactions." Simply put, for any enterprise, it is the customers whom they sell to, the brands they market, the products they sell, the consumers who use their products, the materials used to make the products, the plants that manufacture their products, the suppliers that supply the materials, the employees who build the products directly or indirectly, and the list goes on. Why is there a lack of awareness in enterprises about master data?