Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models -- statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how word choices made by healthcare practitioners and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce the potential for bias in natural language processing pipelines.

Monte Carlo Tree Search (MCTS) is a decision-making algorithm that consists in searching large combinatorial spaces represented by trees. In such trees, nodes denote states, also referred to as configurations of the problem, whereas edges denote transitions (actions) from one state to another. MCTS has been originally proposed in the work by Kocsis and Szepesvári (2006) and by Coulom (2006), as an algorithm for making computer players in Go. It was quickly called a major breakthrough (Gelly et al., 2012) as it allowed for a leap from 14 kyu, which is an average amateur level, to 5 dan, which is considered an advanced level but not professional yet. Before MCTS, bots for combinatorial games had been using various modifications of the min-max alpha-beta pruning algorithm (Junghanns, 1998) such as MTD(f) (Plaat, 2014) and hand-crafted heuristics. In contrast to them, MCTS algorithm is at its core aheuristic, which means that no additional knowledge is required other than just rules of a game (or a problem, generally speaking). However, it is possible to take advantage of heuristics and include them in the MCTS approach to make it more efficient and improve its convergence. Moreover, the given problem often tends to be so complex, from the combinatorial point of view, that some form of external help, e.g.

To determine how close humans and robots can become, we need a clear understanding of what, exactly, friendship is, and defining friendship isn't easy. Our friendships are made, maintained, and repaired all the time. Hopefully, we all have friends and believe, deep in our hearts, that the one ship that won't sink is friendship. Although friendship plays such a profound role in our lives that research links it to both emotional and physical well-being, people disagree about what makes friendship special and how far the bonds can go. Can we be friends with people who do things we find unconscionable?

Former Secretary of the Navy J. William Middendorf II, of Little Compton, lays out the threat posed by the Chinese Communist Party in his recent book, "The Great Nightfall." With the emerging priority of artificial intelligence (AI), China is shifting away from a strategy of neutralizing or destroying an enemy's conventional military assets -- its planes, ships and army units. AI strategy is now evolving into dominating what are termed adversaries' "systems-of-systems" -- the combinations of all their intelligence and conventional military assets. What China would attempt first is to disable all of its adversaries' information networks that bind their military systems and assets. It would destroy individual elements of these now-disaggregated forces, probably with missiles and naval strikes.

In brief US government should avoid hastily banning AI-powered autonomous weapons and instead step up its efforts in developing such systems to keep up with foreign enemies, according to the National Security Commission on AI. The independent group headed by ex-Google CEO Eric Schmidt and funded by the Department of Defense has published its final report advising the White House on how best to advance AI and machine learning to stay ahead of its competitors. Stretching over 750 pages, the report covers a lot of areas, including retaining talent, the future of warfare, protecting IP, and US semiconductor supply chains. The most controversial point raised by Schmidt and the other advisors was that America should not turn its back on autonomous AI weapons. The US government should actually be building its own systems to deter other countries from wreaking havoc, it argued.

This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEO's to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEO's to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in my first blog. Each of the blogs in this series explores either a group of skills or does a deeper dive into one of the skill areas. I have come to the conclusion that to unlock the last mile of AI value realization that board directors and CEOs must accelerate building a unified brain trust (a unified set of leadership skills that are hardwired in relevant digital and AI skills) to modernize their organizations more rapidly.

The US federal government should do more to fund research and facilitate collaboration which helps cities tap the benefits of artificial intelligence (AI) and other emerging technologies, says a new report from non-profit thinktank the Information Technology and Innovation Foundation (ITIF). "Smart cities offer an important opportunity to address both infrastructure needs and strained state and local budgets at the same time," the report says, noting the large revenue shortfalls many cities face due to the pandemic. Cities can use AI in transport, the electrical grid, buildings, city operations and more. Similarly, a 2020 report from Microsoft and PwC found that AI-enabled decarbonisation technologies could reduce the carbon intensity of the global economy. ITIF's research outlines several key challenges to deployment.

Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.

Say you're a job-seeker who's got a pretty good idea of what employers want to hear. Like many companies these days, your potential new workplace will give you a personality test as part of the hiring process. You plan to give answers that show you're enthusiastic, a hard worker and a real people person. Then they put you on camera while you take the test verbally, and you frown slightly during one of your answers, and their facial-analysis program decides you're "difficult." This is just one of many problems with the increasing use of artificial intelligence in hiring, contends the new documentary "Persona: The Dark Truth Behind Personality Tests," premiering Thursday on HBO Max. The film, from director Tim Travers Hawkins, begins with the origins of the Myers-Briggs Type Indicator personality test.

In many ways, cybersecurity has always been a contest; vendors race to develop security products that can identify and mitigate any threats, while cybercriminals aim to develop malware and exploits capable of bypassing protections. With the emergence of artificial intelligence (AI), however, this combative exchange between attackers and defenders is about to become more complex and increasingly ferocious. According to Max Heinemeyer, Director of Threat Hunting at AI security firm Darktrace, it is only a matter of time before AI is co-opted by malicious actors to automate attacks and expedite the discovery of vulnerabilities. "We don't know precisely when offensive AI will begin to emerge, but it could already be happening behind closed doors," he told TechRadar Pro. "If we are able to [build complex AI products] here in our labs with a few researchers, imagine what nation states that invest heavily in cyberwar could be capable of." When this trend starts to play out, as seems inevitable, Heinemeyer says cybersecurity will become a "battle of the algorithms", with AI pitted against AI.