The expansive role that artificial intelligence (AI) can play is being valued on a global stage for its transformative merits, in plenty of industries, to improve efficiencies and outcomes. But there are also ethical questions around the responsible use of AI for a globalized society, governed by a respect for human rights and democratic principles. Powerful AI-driven technologies have the potential to be misused, and there have already been numerous studies on AI displaying bias towards certain skin shades, amplifying discrimination against women, and members of marginalized ethnic groups. Minority groups often become the unwitting victim of ethical AI conundrums, in large part because machine learning works best off large subsets of data, with the majority of input data coming from, well, the majority. While these AI-driven discrepancies are well-recognized, the power of AI as a transformative force for societal good is also coming to be well-regarded, with research from the Pew Center indicating that over half (53%) of people from 20 countries surveyed saying that artificial intelligence has had a positive effect on society.

Stephen Thaler and Ryan Abbott plan to bring a light beacon, a beverage container, and a machine called Dabus into court, along with a simple question: Does an inventor need to be human? Depending on how they respond, a panel of judges on the U.S. Court of Appeals for the Federal Circuit could open the door to another significant question: What is "obvious" to a machine? A basic tenet of U.S. law is that patents aren't awarded for inventions that are obvious. The standard of obviousness in patent law is measured against a hypothetical person of ordinary skill in the art. Putting artificial intelligence, with its potential for near omnipotent capabilities, on equal footing as human inventors could have a significant impact on patent law's obviousness standard, attorneys and patent professionals say.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. This week, The European Parliament, the body responsible for adopting European Union (EU) legislation, passed a non-binding resolution calling for a ban on law enforcement use of facial recognition technology in public places. The resolution, which also proposes a moratorium on the deployment of predictive policing software, would restrict the use of remote biometric identification unless it's to fight "serious" crime, such as kidnapping and terrorism. The approach stands in contrast to that of U.S. agencies, which continue to embrace facial recognition even in light of studies showing the potential for ethnic, racial, and gender bias. A recent report from the U.S. Government Accountability Office found that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.

We propose a new formalization of legal competences, and in particular for the Hohfeldian categories of power and immunity, through a deontic reinterpretation of dynamic epistemic logic. We argue that this logic explicitly captures the norm-changing character of legal competences while providing a sophisticated reduction of the latter to static normative positions. The logic is completely axiomatizable, and we apply it to a concrete case in German contract law to illustrate that it can capture the distinction between legal ability and legal permissibility.

Artificial Intelligence (AI) logic formalizes the reasoning of intelligent agents. In this paper, we discuss how an argumentation-based AI logic could be used also to formalize important aspects of social reasoning. Besides reasoning about the knowledge and actions of individual agents, social AI logic can reason also about social dependencies among agents using the rights, obligations and permissions of the agents. We discuss four aspects of social AI logic. First, we discuss how rights represent relations between the obligations and permissions of intelligent agents. Second, we discuss how to argue about the right-to-know, a central issue in the recent discussion of privacy and ethics. Third, we discuss how a wide variety of conflicts among intelligent agents can be identified and (sometimes) resolved by comparing formal arguments. Importantly, to cover a wide range of arguments occurring in daily life, also fallacious arguments can be represented and reasoned about. Fourth, we discuss how to argue about the freedom to act for intelligent agents. Examples from social, legal and ethical reasoning highlight the challenges in developing social AI logic. The discussion of the four challenges leads to a research program for argumentation-based social AI logic, contributing towards the future development of AI logic.

In late 2020, the presidency of the Council of the European Union (EU) released its conclusions on artificial intelligence and human rights. In contrast to the American and Chinese approaches, this effort formalizes what has been dubbed as "a third way" for artificial intelligence -- tech regulation that prioritizes shared "European values." The EU document focuses on the "underlying idea of human dignity" as the key element of a "human-centric approach to AI." While it states that AI-based solutions can "perpetuate and amplify discrimination, including structural inequalities," it also notes that "one Member State continued to object to the use of the term'gender equality'" in the final draft. This dissenting voice, it was later reported, was that of Poland.

Genesys, a global cloud leader in customer experience orchestration, announced that it has entered into agreements to acquire Pointillist and Exceed.ai to enhance its ability to deliver the power of empathy in every customer experience as it executes on the promise of Experience as a ServiceSM. Both transactions are expected to close before the end of the calendar year 2021. Genesys pioneered Experience as a Service to help companies transform how they connect with customers and employees. Companies need new technologies and artificial intelligence (AI) to orchestrate end-to-end, meaningful experiences with the objective of driving trust and loyalty. This experience orchestration provides superior business outcomes, as companies embrace more contextual, predictive and relevant experiences with every point of customer or employee contact.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A New Jersey man who used dating apps to lure and kill three women five years ago was sentenced Wednesday to 160 years in prison after a trial in which it was revealed that friends of one victim did their own detective work on social media to ferret out the suspect. Khalil Wheeler-Weaver, 25, sat motionless as the judge gave the sentence in state court in Newark. The sentencing was preceded by emotional statements by family members of victims Robin West and Sarah Butler.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. There are 40.3 million victims of human trafficking globally, according to the International Labor Organization. Marinus Analytics, a startup based in Pittsburgh, Pennsylvania, hopes to make a dent in that number. The company's mission is to "serve those working on the frontlines of public safety by developing technology for them to disrupt human trafficking, child abuse, and cyber fraud." For its achievements, Marinus won $500,000 as part of its third-place ranking in the 2021 IBM Watson AI XPRIZE competition.

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings. Keywords: Accountability, Explainable AI, Algorithmic Bias, Regulation.