Societal ideas and trends dictate media narratives and cinematic depictions which in turn influences people's beliefs and perceptions of the real world. Media portrayal of culture, education, government, religion, and family affect their function and evolution over time as people interpret and perceive these representations and incorporate them into their beliefs and actions. It is important to study media depictions of these social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against any demographic section. In this work, we examine media representation of professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups and titles to facilitate their retrieval from speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing (NLP) models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes like genre, country of production, and title type on these trends, and investigate if the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors is becoming negative over time, whereas astronauts, musicians, singers, and engineers are mentioned favorably. Professions that employ more people have increased media frequency, supporting our hypothesis that media acts as a mirror to society.

Brazil's House of Representatives has approved a bill that sets out legal regulations for artificial intelligence (AI). Bill No. 21/20 outlines guidelines to develop and utilize AI in Brazil. The bill will regulate transparency regarding the use of AI in the public sector, promote the creation of AI for the public sector, and require the "adoption of regulatory instruments that promote innovation." AI can predict and make decisions when implemented into computer systems and machines. The innovation in society and the regulations that have been introduced have been welcomed by the author of the project, Deputy Eduardo Bismarck (PDT-CA). He stated that "the time is now to outline principles: rights and duties and responsibilities" to account for this innovation already integrated into reality.

Bias, understood to be an inclination or prejudice for or against one person or group, especially in a way considered to be unfair, is rife in AI and ML. In fact, algorithmic bias in AI systems can take varied forms such as gender bias, racial prejudice and age discrimination. In regard to bias in AI, it is widely recognized that there are two prominent types. These manifest themselves as cognitive bias, meaning that bias is inserted into algorithms through designers introducing them into models or using training data already inclusive of bias, and also lack of complete data, therefore not being representative of the population, culture and encompassing of all demographics. Kishore Karra, Executive Director, Model Risk Governance & Review at J.P. Morgan recently discussed bias in AI models, suggesting that it is at an individual level which bias can creep in: Kishore also recognized that bias can creep into machine learning models unintentionally and it's important to identify sources of bias.

In 2017, Sophia made history by becoming the first android to be granted legal citizenship . The humanoid, with nationality of Saudi Arabia, has made several controversial statements, but the most recent has left the world speechless: she wants to have a robot baby and start a family . "The notion of family is very important, it seems. I think it is wonderful that people can find the same emotions and relationships that they call family outside of their blood group, " said Sophia in an interview for an international media cited by ADN40 . The famous android, operated by an advanced Artificial Intelligence (AI) system, commented that it is very important to be surrounded by people who love and love you.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. After a 2019 research paper demonstrated that commercially available facial analysis tools fail to work for women with dark skin, AWS executives went on the attack. Instead of offering up more equitable performance results or allowing the federal government to assess their algorithm like other companies with facial recognition tech have done, AWS executives attempted to discredit study coauthors Joy Buolamwini and Deb Raji in multiple blog posts. More than 70 respected AI researchers rebuked this attack, defended the study, and called on Amazon to stop selling the technology to police, a position the company temporarily adopted last year after the death of George Floyd. But according to the Abuse and Misogynoir Playbook, published earlier this year by a trio of MIT researchers, Amazon's attempt to smear two Black women AI researchers and discredit their work follows a set of tactics that have been used against Black women for centuries.

The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to regularly overestimate the applicability of recent progress in machine learning to the complex coordination problem firms face in forming cartels. Similarly, modelling results supporting the possibility of collusion by learning algorithms often use simple market simulations which allows them to use simple algorithms that do not produce many of the problems machine learning practitioners have to deal with in real-world problems, which could prove to be particularly detrimental to learning collusive agreements. After critically reviewing the literature on algorithmic collusion, and connecting it to results from computer science, we find that while it is likely too early to adapt antitrust law to be able to deal with self-learning algorithms colluding in real markets, other forms of algorithmic collusion, such as hub-and-spoke arrangements facilitated by centralized pricing algorithms might already warrant legislative action.

In this paper we present a new classification model in machine learning. Our result is threefold: 1) The model produces comparable predictive accuracy to that of most common classification models. 2) It runs significantly faster than most common classification models. 3) It has the ability to identify a portion of unseen samples for which class labels can be found with much higher predictive accuracy. Currently there are several patents pending on the proposed model.

In the future, if armies deploy autonomous robot soldiers and they fire on the wrong targets, who will we hold responsible - the general who deployed them or their designer, Singapore law professor Simon Chesterman asks in We, the Robots.

On September 2, 2021, the US District Court for the Eastern District of Virginia granted the United States Patent and Trademark Office's (USPTO's) motion for summary judgement, finding that an artificial intelligence (AI) system cannot be named as an inventor on a patent. The action concerned two patent applications that Stephen Thaler had filed with the USPTO, which he alleged should not have been rejected by the Office. The USPTO had rejected the applications on the basis that no natural person was identified as an inventor. Thaler argued that a patent application for an AI-generated invention should list the AI system as the inventor when the AI system has met the invention criteria. Thaler alleged that he developed and applied advanced AI systems that are capable of generating patentable output under conditions where no natural person traditionally meets inventorship criteria. Thaler is the owner of "DABUS," an AI machine that "invented" a light beacon that flashes in a new and inventive manner to attract attention, and a beverage container based on fractal geometry.

The European Commission announced first-of-its-kind legislation regulating the use of artificial intelligence in April. This unleashed criticism that the regulations could slow AI innovation, hamstringing Europe in its competition with the U.S. and China for leadership in AI. For example, Andrew McAfee wrote an article titled "EU proposals to regulate AI are only going to hinder innovation." Anticipating this criticism and mindful of the example of GDPR, where Europe's thought-leadership position didn't necessarily translate into data-related innovation, the EC has tried to address AI innovation directly by publishing a new Coordinated Plan on AI. Released in conjunction with the proposed regulations, the plan is full of initiatives intended to help the EU become a leader in AI technology.