A trademark applies to a word, design, phrase or combination thereof that identifies goods or services as coming from a certain company or source, like McDonalds' Golden Arches or Kellogg's Tony The Tiger. Registering a trademark prevents other businesses or individuals from registering the mark and secures nationwide rights for the registrant, as long as they continue using it in business. What complicates the contest between Ficker and Activision is that the party that first applies for rights over a certain mark does not necessarily grant that party the rights. Essentially, registration with the federal government is not required to gain such rights.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Facial recognition (FR) issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans.

TikTok quietly changed its US privacy policy this week to notify users it may start collecting'faceprint and voiceprint' and other biometric data. The app did not specify what the data would be used for but said it would ask for permission first, 'where required by law.' The update comes just three months after TikTok paid more than $90 million to settle a class-action lawsuit claiming it secretly recorded millions of members' facial features and other biomarkers. TikTok reportedly has 100 million users in the US alone. TikTok has updated its privacy policy to notify US users it may record the'faceprint and voiceprint' and other unique biometric data.

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The United Nations should follow the European Commission in establishing a regulatory framework for artificial intelligence and biometrics to protect people subject to the technologies, build trust in their use and take the pressure off data scientists to constantly justify the ethics, writes Eleonore Fournier-Tombs of McGill University for The Conversation. The European Commission (EC) put forward proposals in April 2021 that seek to harmonize rules on artificial intelligence and create mechanisms which Fournier-Tombs likens to the process for seeking approval for a new drug. Developers of a new high-risk application of AI would have to submit it for regulatory approval. They would also have to provide details on how the models and data are used and how impacts on privacy or discrimination would be addressed. Areas of risk include biometric identification, categorization and evaluation of the eligibility of people for accessing welfare and services, including in emergency response situations.

WASHINGTON--The Supreme Court Thursday narrowed the scope of a federal anti-hacking law, ruling that it doesn't cover individuals who use their authorized access to obtain information for improper purposes. The decision came in the case of a police officer who ran a woman's license plate in exchange for cash from a man, something that "plainly flouted his department's policy," Justice Amy Coney Barrett wrote for a 6-3 court. But his action didn't violate the Computer Fraud and Abuse Act of 1986, which authorizes up to 10 years imprisonment for anyone who "intentionally accesses a computer without authorization or exceeds authorized access" to obtain computer information. In a 20-page opinion that, among other features, focused on the grammatical significance of the modifier "so," Justice Barrett drew a sharp distinction: The law covers people who, although they are authorized to use a computer system, obtain files that are off-limits to them. But it doesn't reach those who are entitled to access particular information--like Nathan Van Buren, a former Cumming, Ga., police sergeant who was authorized to use the motor-vehicle database--even if they misuse the data they pull.

Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but still lead to negative consequences. In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a method for defining warmth and competence axes in semantic embedding space, and show that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM model to textual stereotype data and show that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to counter stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the most effective ways to reduce biased thinking, yet the problem of generating anti-stereotypes has not been previously studied. Thus, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination.

It is well known that two-sided markets are unfair in a number of ways. For instance, female workers at Uber earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalisation of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. While the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semi-definite programming. This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach, and trade-offs between Inter- and Intra-fairness.

Although people have the ability to engage in vapid dialogue without effort, this may not be a uniquely human trait. Since the 1960's researchers have been trying to create agents that can generate artificial conversation. These programs are commonly known as chatbots. With increasing use of neural networks for dialog generation, some conclude that this goal has been achieved. This research joins the quest by creating a dialog generating Recurrent Neural Network (RNN) and by enhancing the ability of this network with auxiliary loss functions and a beam search. Our custom loss functions achieve better cohesion and coherence by including calculations of Maximum Mutual Information (MMI) and entropy. We demonstrate the effectiveness of this system by using a set of custom evaluation metrics inspired by an abundance of previous research and based on tried-and-true principles of Natural Language Processing.

Companies are interested in buying insurance to mitigate the risk of adoption and deployment of new AI applications with no history of use. "When it comes to the commercial use of AI, businesses can't rely on government regulation to protect them against potential losses in the event it fails to live up to its promise," stated Saar Yoskovitch, CEO and cofounder of Augury, in a recent account in Open Access Government. As deployed AI systems mature, they will increasingly make high risk decisions. "But AI models are often brittle, do not deal well with edge cases and may have been trained on a dataset with inherent biases," stated Yoskovitch. This is especially prevalent with AI systems that use human behavior as an input, such as auto insurance applications that capture an individual customer's driving behavior.