Over the past several years, legal applications of deep learning have been on the rise. However, as with other high-stakes decision making areas, the requirement for interpretability is of crucial importance. Current models utilized by legal practitioners are more of the conventional machine learning type, wherein they are inherently interpretable, yet unable to harness the performance capabilities of data-driven deep learning models. In this work, we utilize deep learning models in the area of trademark law to shed light on the issue of likelihood of confusion between trademarks. Specifically, we introduce a model-agnostic interpretable intermediate layer, a technique which proves to be effective for legal documents. Furthermore, we utilize weakly supervised learning by means of a curriculum learning strategy, effectively demonstrating the improved performance of a deep learning model. This is in contrast to the conventional models which are only able to utilize the limited number of expensive manually-annotated samples by legal experts. Although the methods presented in this work tackles the task of risk of confusion for trademarks, it is straightforward to extend them to other fields of law, or more generally, to other similar high-stakes application scenarios.

Earlier in the year, our new book was released on Amazon, called the AI Dilemma, and it discussed the impacts of AI on different industries, ranging from: financial services, government, healthcare, media and technology, manufacturing, retail, to name just a few. As we review the AI ethics landscape, there have been many developments, some reinforcing that we do not have sufficient guardrails in place yet, while other developments on ethical AI policy fronts giving us increased confidence that the right approaches on AI risks are seriously being considered. This blog reviews aspects of AI Ethics from A Perfect Storm lens and highlights lessons learned from AI failures. Microsoft's Bot Tay - the bot Tay debacle that came up with racists remarks within 24 hours of its interaction with people -it's an important learning on continued risks of AI bots using ML generalizations from large amounts of data. Microsoft trained Tay's algorithm on public data along with material provided by professional comedians to increase language literacy for the bot.

A new California law designed to prevent the warehouse industry from overworking employees doesn't name a specific company. But the legislation's target is clear: Amazon, which has given machines unparalleled control over workers and is accused of using the technology to impose unreasonable demands on them. Authored by Assemblywoman Lorena Gonzalez, the bill prohibits the use of monitoring systems that thwart basic worker rights such as rest periods, bathroom breaks and safety. The legislation will help determine whether governments can regulate human resources software that's expected to play an increasing role in deciding who gets hired and fired, how much workers are paid and how hard they work. "This is just the beginning of our work to regulate Amazon & its algorithms that put profits over workers' safety," Gonzalez, a San Diego Democrat, tweeted earlier this year.

Recent conversations around gender and AI have centred around the need to understand gender beyond the binary of male and female. For example, facial recognition technology used by Uber in the US has problems with correctly recognising transgender persons (see here and here). Yet Uber is no exception. The U.S. National Science Foundation, for example, has highlighted research that shows that "facial analysis services performed consistently worse on transgender individuals, and were universally unable to classify non-binary genders." According to CNN Business,¹ "The way a computer sees gender isn't always the same way people see it. A growing number of terms for describing one's gender are becoming common in everyday life."

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In the seventy-three years since the United Nations ratified the Universal Declaration of Human Rights, the world has been unable to agree on what rights, exactly, should be accorded to human beings. The increasing scale of AI is raising the stakes for major ethical questions. The very idea itself is controversial. The best that can be said of such idealistic documents is that they limit the most extreme abuses to which people may subject one another, including enslavement and economic exploitation. That's an important achievement because the impending arrival of virtual worlds threatens to compromise human autonomy in the most basic sense.

With New York City's passage of one of the toughest U.S. laws regulating the use of artificial intelligence tools in the workplace, federal officials are signaling that they too want to scrutinize how that new technology is being used to sift through a growing job applicant pool without running afoul of civil rights laws and baking in discrimination. The use of that new technology in hiring and other employment decisions is growing, but its volume remains hard to quantify, and the regulations aimed at combating bias in its application may be difficult to implement, academics and employment attorneys say. "Basically, these are largely untested technologies with virtually no oversight," said Lisa Kresge, research and policy associate at the University of California, Berkeley Labor Center, who studies the intersection of technological change and inequality. We have rules about pesticides or safety on the shop floor. We have these digital technologies, and in virtual space, and that ...

The Federal Trade Commission (FTC) is considering a wide range of options, including new rules and guidelines, to tackle data privacy concerns and algorithmic discrimination. FTC s Chair Lina Khan, in a letter to Senator Richard Blumenthal (D-CT), outlined her goals to "protect Americans from unfair or deceptive practices online" and in particular, Khan said that the FTC is considering rulemaking to address "lax security practices, data privacy abuses and algorithmic decision-making that may result in unlawful discrimination." The FTC s letter comes in response to a letter from several lawmakers, including Senator Blumenthal, who urged the FTC to start a rulemaking process that would "protect consumer privacy, promote civil rights and set clear safeguards on the collection and use of data in the digital economy." "Rulemaking may prove a useful tool to address the breadth of challenges that can result from commercial surveillance and other data practices […] and could establish clear market-wide requirements," Khan wrote. The FTC can resort to its rulemaking authority to address unfair or deceptive practices that occur commonly, instead of relying on actions against individual companies.

Quick, think of a scary technology – one with the potential to enslave humankind or destroy the earth. Did you think of AI? Few other technologies generate the fear factor of artificial intelligence. Ever since Alan Turing introduced the idea in 1948, people have wondered what would happen if machines outsmarted their creators and took charge of the planet. Legal protections could avert such a calamity, and the first AI regulations have been published and are awaiting public comment. But some of these draft rules set impossibly high standards.

The use of artificial intelligence to determine access to credit and other important life opportunities has been targeted by legislation under consideration by the District of Columbia City Council. On December 9, 2021, DC Attorney General Karl Racine introduced the "Stop Discrimination by Algorithms Act of 2021." "Not surprisingly, algorithmic decision-making computer programs have been convincingly proven to replicate and, worse, exacerbate racial and other illegal bias in critical services that all residents of the United States require to function in our treasured capitalistic society, " said AG Racine. "This so-called artificial intelligence is the engine of algorithms that are, in fact, far less smart than they are portrayed, and more discriminatory and unfair than big data wants you to know. Our legislation would end the myth of the intrinsic egalitarian nature of AI."