Microsoft and IBM sent congratulatory public messages to president-elect Joe Biden this month. Both expressed hope that his administration would ease the nation's political divisions, and suggested it consider crafting the first federal rules governing face recognition. "When it comes to issues such as safeguards for facial recognition, we have no national law at all," Microsoft president Brad Smith wrote. "We need new laws fit for the future." IBM CEO Arvind Krishna told Biden his company was "ready to work with you" on prohibiting use of the technology for "mass surveillance, racial profiling, or violations of basic human rights and freedoms."

Energized by a nearly all-female lineup of researchers and activists, the film condemns the ways racism and classism underpin big data’s design and applications.

On a summer night in Dallas in 2016, a bomb-handling robot made technological history. Police officers had attached roughly a pound of C-4 explosive to it, steered the device up to a wall near an active shooter and detonated the charge. In the explosion, the assailant, Micah Xavier Johnson, became the first person in the United States to be killed by a police robot. Afterward, then-Dallas Police Chief David Brown called the decision sound. Before the robot attacked, Mr. Johnson had shot five officers dead, wounded nine others and hit two civilians, and negotiations had stalled.

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the time-scale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the time-scale of the race is thus required, as this may induce important non-trivial effects. This article is part of the special track on AI and Society.  

Differential privacy is a data anonymization technique that's used by major technology companies such as Apple and Google. The goal of differential privacy is simple: allow data analysts to build accurate models without sacrificing the privacy of the individual data points. But what does "sacrificing the privacy of the data points" mean? Well, let's think about an example. Suppose I have a dataset that contains information (age, gender, treatment, marriage status, other medical conditions, etc.) about every person who was treated for breast cancer at Hospital X.

Google asserts that the patent industry stands to benefit from AI and machine learning models like BERT, a natural language processing algorithm that attained state-of-the-art results when it was released in 2018. In a whitepaper published today, the tech giant outlines a methodology to train a BERT model on over 100 million patent publications from the U.S. and other countries using open-source tooling, which can then be used to determine the novelty of patents and generate classifications to assist with categorization. The global patent corpus is large, with millions of new patents issued every year. Patent applications average around 10,000 words and are meticulously wordsmithed by inventors, lawyers, and patent examiners. Patent filings are also written with language that can be unintelligible to lay readers and highly context-dependent; many terms are used to mean completely different things in different patents.

HPL has announced the appointment of new president and chief executive officer, Adam Ferguson. In his new role, Ferguson assumes the day-to-day leadership of the company and is tasked with its strategic growth as the company continues to rapidly acquire more customers. Additionally, HPL has had several new hires recently and will be hiring more talent in the coming months. The momentum necessitated Ferguson's addition. Butts decided to bring an experienced partner to support the HPL team, carriers and partners.

We are seeing overwhelming growth in AI/ML systems to process oceans of data that are being generated in the new digital economy. However, with this growth, there is a need to seriously consider the ethical and legal implications of AI. As we entrust increasingly more sophisticated and important tasks to AI systems, such as automatic loan approval, for example, we must be absolutely certain that these systems are responsible and trustworthy. Reducing bias in AI has become a massive area of focus for many researchers and has huge ethical implications, as does the amount of autonomy that we give these systems. The concept of Responsible AI is an important framework that can help build trust in your AI deployments.

New York City is trying to rein in the use of algorithms used to screen job applicants. It's one of the first cities in the U.S. to try to regulate what is an increasingly common -- and opaque -- hiring practice. The city council is considering a bill that would require potential employers to notify job candidates about the use of these tools, referred to as "automated decision systems." Companies would also have to complete an annual audit to make sure the technology doesn't result in bias. The move comes as the use of artificial intelligence in hiring skyrockets, increasingly replacing human screeners.

Artificial intelligence (AI) is steadily becoming a familiar tool for many Australians. We have come to know it through our pocket voice assistants, like Siri and Alexa, and as the brains behind Google's predictive searches. Australian businesses, particularly in the mining sector, view it as a means to gain a competitive advantage, and we have even seen its potential to fight COVID-19. As AI begins to permeate every aspect of our lives, the Australian government has recognised the economic and social opportunities it affords us in its newly proposed AI Action Plan. The discussion paper, released on 29 October 2020, is the latest in a suite of Australian initiatives targeting AI regulation and development, following on from the AI Ethics Framework.