That the regulation of Artificial intelligence is a hot topic is hardly surprising. AI is being adopted at speed, news reports frequently appear about high-profile AI decision-making, and the sheer volume of guidance and regulatory proposals for interested parties to digest can seem challenging. What can we expect in terms of future regulation? And what might compliance with "ethical" AI entail? High-level ethical AI principles were made by the OECD, EU and G20 in 2019.

March 4, 2021--Artificial intelligence is present in most facets of American digital life, but experts are in a constant race to identify and address potential dangers before they impact consumers. From making a simple search on Google to listening to music on Spotify to streaming Tiger King on Netflix, AI is everywhere. Predictive algorithms learn from a consumer's viewing habits and attempt to direct consumers to other content an algorithm thinks a consumer will be interested in. While this can be extremely convenient for consumers, it also raises many concerns. Jaisha Wray, associate administrator for international affairs at the National Telecommunications and Information Administration, was a panelist at a conference hosted Tuesday by the Federal Communications Bar Association.

There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.

It's easy to decry cancel culture, but hard to turn it back. And as I explain below, the lessons that members of the AI community have learned in this regard can be generalized to other professional subcultures. To understand the flash point at issue, it's necessary to delve briefly into how AI functions. In many cases, AI algorithms have partly replaced both formal and informal human decision-making systems that pick who gets hired or promoted within organizations. Financial institutions use AI to determine who gets a loan. And some police agencies use AI to anticipate which neighborhoods will be afflicted by crime.

The Washington state Legislature, which has proposed legislation in the past to tackle issues such as data privacy and the use of facial recognition tech, is now reviewing a bill that would regulate the use of "automated decision systems" and AI technology within state government. According to the bill, these systems use algorithms to analyze data to help make or support decisions that could result in discrimination against different groups or make decisions that could negatively impact constitutional or legal rights. As a result, Senate Bill 5116 aims to regulate these systems to prevent discrimination and ban government agencies from using AI tech to profile individuals in public areas. Sen. Bob Hasegawa, one of the bill's sponsors, said in an email, "I introduced this bill after reading disturbing reports about the disproportionate impact that AI technology has on communities of color." "This technology is changing how we live our day to day lives, and it's important that we make sure it's not further deepening the inequities and discrimination in our society before we create a reliance on it," he said.

While 2020 showed that the best-laid plans can be quickly thrown for a loop, it also proved that there is an adaptability in the legal industry that few believed really existed. We don't want to jinx 2021, but we're starting to feel like it's "safe" to start making plans again, taking the lessons learned and changes made in 2020 and applying them to strategic efforts. To get a sense of where the legal industry is focused for 2021, we took some time to talk to professionals in various roles to hear what their strategic plans include for this year. We learned of three major initiatives that organizations across the legal industry will focus on. Long known as the department of "no's" focused on mitigating risks, legal teams will have the chance to re-position themselves as facilitators of commerce.

This article was originally published by Industry Today on March 3, 2021, and is reproduced below in full with permission. With rapid changes, pressure to innovate, and acceleration of implementation of advanced technology across all stages of the supply chain over the past year, there are important intellectual property (IP) considerations that companies need to make to protect their inventions. Leading edge tech like Augmented and Virtual Reality, machine learning and Artificial Intelligence, and 3D printing have become integral to business success yet continue to cause confusion around how the technology should be patented. This article explores some of the nuances as they relate to the art of protecting the software that fuels the base technology of these advanced innovations and important considerations that need to be made in the current environment. Most machine learning (ML) and artificial intelligence (AI) innovations are generally based in computer software. While courts and the U.S. Patent and Trademark Office ("U.S. PTO") have established limits on the ability to patent computer software, it is still possible to obtain meaningful, broad, and valuable patent protection on computer software.

Tiger Woods has told authorities he doesn't remember the rollover crash that landed him in a hospital with metal rods and pins in his leg. But the SUV he was driving does. Like other modern cars and trucks, the Genesis GV80 that Woods was driving when he crashed was equipped with an electronic data recorder and other computer hardware meant to serve as a digital witness of sorts -- filled with information investigators can use to piece together the seconds before and during the accident. The devices are part of a broader array of safety technology built into many newer vehicles. Vehicles in the Genesis line -- Hyundai's luxury brand -- for example, also feature artificial intelligence software that keeps a watchful eye, sending alerts if it detects the driver is distracted or closes his or her eyes while driving.

India has, so far, seen very low robot adoption compared to its regional and global peers. According to a recent study, the total number of jobs related to developing and deploying new technologies, like automation, AI and robotics-related applications, may grow to 20 to 50 million globally by 2030 and more than 375 million workers globally. "Reduction of customs duty/IGST and providing tax breaks/incentives to robotics adopters can boost demand," Hi- Tech Robotics Systemz founder & CEO Anuj Kapuria said. It is among the first companies in industrial autonomous and AI technology in the country. "For accelerating technology and R&D, setting up of robotics centres of excellence, incubation centres, continued research grants for robotics R&D and continuation of income tax deduction will be the key drivers," he said.

An artificial intelligence commission led by former Google CEO Eric Schmidt is urging the U.S. to boost its AI skills to counter China, including by pursuing "AI-enabled" weapons – something that Google itself has shied away from on ethical grounds. Schmidt and current executives from Google, Microsoft, Oracle and Amazon are among the 15 members of the National Security Commission on Artificial Intelligence, which released its final report to Congress on Monday. "To win in AI we need more money, more talent, stronger leadership," Schmidt said Monday. The report says that machines that can "perceive, decide, and act more quickly" than humans and with more accuracy are going to be deployed for military purposes -- with or without the involvement of the U.S. and other democracies. It warns against unchecked use of autonomous weapons but expresses opposition to a global ban.