By Dennis D. Hirsch, Published on 01/01/20

In this era of the Fourth Industrial Revolution, advances in artificial intelligence ("AI") has resulted in AI capable of generating inventions that are novel and inventive. The question becomes whether these AI generated inventions can be protected under the current patent law framework. A recent development in Australian jurisprudence takes a step toward clarifying the applicability of patent law on AI generated inventions. The Federal Court of Australia recently held in Thaler v Commissioner of Patents [2012] FC 879 ("the Thaler case") that an AI system can be named as an inventor in a patent application. After a brief introduction on basic concepts underlying patent law and AI, this article will discuss the Thaler case followed by an analysis on the Malaysian perspective.

As I've written in my "On AI Ethics," series, machine learning models that aim to mirror and predict real-life as closely as possible are not without their challenges. Household name brands like Amazon, Apple, Facebook, Google have been accused of algorithmic bias that have negatively affected society at large. While some organizations are investing in teams to ensure algorithmic accountability and ethics, Reid Blackman, CEO of Virtue and former professor of philosophy at Colgate University and the University of North Carolina, Chapel Hill, says most are still falling short in ensuring their products perform ethically in the real world. "Despite reputational, regulatory, and legal risks, it's surprising how many companies that rely on AI/ML still lack the ability to identify, evaluate, and mitigate the associated ethical risks," says Blackman. "Teams end up either overlooking risks, scrambling to solve issues as they come up, or crossing their fingers in the hope that the problem will resolve itself."

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.

As businesses continue to adopt artificial intelligence technologies, corporate lawyers and in-house data scientists should prepare to get better acquainted. Lawmakers are increasingly indicating that A.I. regulations are coming, which means that businesses will need to ensure that their machine learning systems aren't violating laws governing privacy, security, and fairness. One upstart law firm specializing in A.I.-related legal matters is betting that companies will be increasingly investigating the various ways their machine learning systems could put their businesses in legal hot water. The bnh.ai law firm, based in Washington D.C., pitches itself as a boutique law firm that caters to both lawyers and technologists alike. Having a solid understanding of A.I. and its family of technologies like computer vision and deep learning is crucial, the firm's founders believe, because solving complicated legal issues related to A.I. isn't as simple as patching a software bug.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

Artificial intelligence (AI) systems operate in increasingly diverse areas, from healthcare to facial recognition, the stock market, autonomous vehicles, and so on. While the underlying digital infrastructure of AI systems is developing rapidly, each area of implementation is subject to different degrees and processes of legitimization. By combining elements from institutional theory and information systems-theory, this paper presents a conceptual framework to analyze and understand AI-induced field-change. The introduction of novel AI-agents into new or existing fields creates a dynamic in which algorithms (re)shape organizations and institutions while existing institutional infrastructures determine the scope and speed at which organizational change is allowed to occur. Where institutional infrastructure and governance arrangements, such as standards, rules, and regulations, still are unelaborate, the field can move fast but is also more likely to be contested. The institutional infrastructure surrounding AI-induced fields is generally little elaborated, which could be an obstacle to the broader institutionalization of AI-systems going forward.

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies. Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

Barely a day goes by without a company talking up their green credentials -- how they're aligning themselves with global climate goals, cutting waste and upping their recycling. With all this corporate happy-talk about saving the planet on the rise, so are concerns about greenwashing. Investors and regulators are increasingly sounding the alarm about companies that exaggerate or misrepresent their environmental bona fides. That's what prompted academics at University College Dublin to develop algorithms to help the financial services sector detect and quantify greenwashing. Greenwashing encompasses everything from slightly disingenuous claims of being environmentally friendly to outright falsehoods.