A growing number of tech companies are placing their bets on algorithms to reinvent talent acquisition and create a more inclusive workforce. In some cases, this might mean entirely removing traditional aspects of the hiring process. Introduced in the nineties, applicant tracking systems (ATS), were created to help HR professionals organize the surge of applications that resulted from the growing use of the internet. Over the last several decades, ATS became increasingly advanced, using algorithms to sift through thousands of resumes based on various data. The promise was efficiency and blind hiring, but the algorithms have proven to perpetuate structural inequities in hiring.

The need to pursue racial justice is more urgent than ever, especially in the technology industry. The far-reaching scope and power of machine learning (ML) and artificial intelligence (AI) means that any gender and racial bias at the source is multiplied to the nth power in businesses and out in the world. The impact those technology biases have on society as a whole can't be underestimated. When decision-makers in tech companies simply don't reflect the diversity of the general population, it profoundly affects how AI/ML products are conceived, developed, and implemented. Evolve, presented by VentureBeat on December 8th, is a 90-minute event exploring bias, racism, and the lack of diversity across AI product development and management, and why these issues can't be ignored.

Facebook says it's using AI to prioritize potentially problematic posts for human moderators to review as it works to more quickly remove content that violates its community guidelines. The social media giant previously leveraged machine learning models to proactively take down low-priority content and left high-priority content reported by users to human reviewers. But Facebook claims it now combines content identified by users and models into a single collection before filtering, ranking, and deduplicating it and handing it off to thousands of moderators, many of whom are contract employees. Facebook's continued investment in moderation comes as reports suggest the company is failing to stem the spread of misinformation, disinformation, and hate speech on its platform. Reuters recently found over three dozen pages and groups that featured discriminatory language about Rohingya refugees and undocumented migrants.

In "How to Make a Racist AI Without Really Trying," Robyn Speer shows how to build a simple sentiment analysis system, using standard, well-known sources for word embeddings (GloVe and word2vec), and a widely used sentiment lexicon. Her program assigns "negative" sentiment to names and phrases associated with minorities, and "positive" sentiment to names and phrases associated with Europeans. Even a sentence like "Let's go get Mexican food" gets a much lower sentiment score than "Let's go get Italian food." That result isn't surprising, nor are Speer's conclusions: if you take a simplistic approach to sentiment analysis, you shouldn't be surprised when you get a program that embodies racist, discriminatory values. It's possible to minimize algorithmic racism (though possibly not eliminate it entirely), and Speer discusses several strategies for doing so.

Tech is often idealized, if not outright fetishized, as a great leveling force in society. It is assumed that the algorithms at the foundation of the digital sphere are impartial and purely objective. In truth, though, every program will in some way reflect the prejudices of the humans who write them. The new documentary Coded Bias explores how racism is written into the structures of contemporary life. Director Shalini Kantayya follows MIT researcher Joy Buolamwini, founder of the Algorithmic Justice League, who uncovered how facial scanning systems have difficulty recognizing nonmale and especially nonwhite faces.

Future building, it has to be said, is tough–really tough. Especially when the aim is to create a future that's better than the past, and not just one that's different. The irony is that we live in a time when there is so much incredible potential to build a better future. And yet, we have more ways of destroying, or at least seriously diminishing, what lies in front of us, than ever before. On the one hand there are the in-your-face planetary threats–the charismatic megafauna of the global threats world–threats like climate change, environmental pollution and loss of biodiversity; all of them having their roots in our myopic profligacy as a species.

Amid the turbulence of the 20th century's civil rights movement and sexual revolution, the philosophy of science was undergoing its own radical transformation. Suspecting that the scientific method was less straightforward than scientists claimed, philosophers had started challenging the idea that deductive logic was the best way to reveal truths about the world. Against Method (1975), by philosopher of science Paul Feyerabend, played a key role in bringing such arguments to maturity. Forty-five years after its publication, the book continues to offer valuable insights to scientists confused by the public's ambivalence toward hard scientific truths. Most modern scientists would agree that the scientific method provides the best route toward an ever more cohesive understanding of the world around us. Feyerabend did not think it was so simple. He believed that within the landscape of all discoverable knowledge, the scientific method offers a path leading to only a fraction of all knowable facts. This is because it encourages researchers to begin where well-established theories leave off, keeping them aligned with existing scientific paradigms. A path set forward by classical physics, he argued, will not lead to quantum mechanics. Feyerabend thought that new scientific paradigms could only be reached by radical methods. Indeed, given that new paradigms sit outside existing knowledge structures, there cannot be a predefined method on how to discover them. Anarchistic thinking, spiritualism, irrationality—all must remain on the table. As proof of principle, Feyerabend elegantly demonstrated that a strict adherence to the scientific method would have forced Galileo to give up his hypothesis that Earth orbits the Sun. Not only did the existing evidence support the idea that Earth was stationary, the practice of science in the 17th century was largely entrusted to human perception. This meant that not being able to feel Earth moving would have been considered by many to be sufficient to falsify Galileo's theory. Feyerabend asserted that Galileo needed to break the existing scientific paradigm by presenting a new one and that he only succeeded in doing so by going beyond what rational argument allowed, drawing upon, for example, ad hoc hypotheses and emotional language. Against Method was divisive. After publication, Feyerabend was called the “worst enemy of science” in Nature ([ 1 ][1]) and a “breath of fresh air” in Science ([ 2 ][2]). Many scientists thought that the book presented a type of philosophy that could be easily weaponized ([ 3 ][3]), not least because it provided a shield for nonexperts promoting unsubstantiated or malicious arguments. Some also worried that by weakening the boundaries of what counts as bona fide scientific research, science itself might come to be considered just another type of cultural practice. But this is not quite what Feyerabend sought to inspire. Instead, he wanted to generate curiosity about what happens when we try to live within the rules of our current scientific paradigms. Is it always desirable, for example, to treat mathematical harmonies and statistical abstractions as the best reflections of reality? Consider empirical research on the state of American democracy, which largely relies on random sampling and quantitative metrics. By discounting narratives of police injustice as anecdotal, some have argued that political scientists long remained blind to the extent of racial authoritarianism ([ 4 ][4]). Against Method hints that there must be a middle ground between one extreme, in which all views are equally valued, and the other, in which the limits of current scientific paradigms are never tested. This casts the role of today's scientist as more ambiguous than perhaps many would like, but such ambiguity could help scientists strengthen their relationship with the public. By loosening the framework within which scientists may operate, Feyerabend gives them permission to enter the political arena, a realm that many researchers have historically deemed outside their jurisdiction, but one in which their participation is sorely needed. 1. [↵][5]1. T. Theocharis, 2. M. Psimopoulos , Nature 329, 595 (1987). [OpenUrl][6][CrossRef][7][Web of Science][8] 2. [↵][9]1. W. J. Broad , Science 206, 534 (1979). [OpenUrl][10][FREE Full Text][11] 3. [↵][12]1. M. Kuntz , EMBO Rep. 13, 885 (2012). [OpenUrl][13][FREE Full Text][14] 4. [↵][15]1. V. M. Weaver, 2. G. Prowse , Science 369, 1176 (2020). 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The ever-increasing adoption of AI-powered systems in all areas of the economy could either lead to potential discrimination against women or open up new career prospects. Let's find out what needs to be done to achieve the second scenario. For years, there have been debates that artificial intelligence will change the labor market. Nowadays the focus has shifted from the inevitability of the future to an assessment of how and when the world will change and what it means for all of us. There have been several attempts to create AI, but Artificial General Intelligence – the one portrayed in films and books – is still a long way from being built.

One of the greatest barriers to adopting and scaling AI applications is the scarcity of varied, high-quality raw data. To overcome it, firms need to share their data. But the many regulatory restrictions and ethical issues surrounding data privacy pose a major obstacle to doing this. A novel solution that my firm is piloting that could solve this problem is a data trust: an independent organization that serves as a fiduciary for the data providers and governs their data's proper use. Research shows that companies are becoming increasingly aware of the value of sharing data and are exploring ways to do so with other players in their industry or across industries.

AI in healthcare has a bias problem. Last year, it came to light that six algorithms used on an estimated 60-100 million patients nationwide were prioritizing care coordination for white patients over black patients for the same level of illness.The reason? The algorithm was trained on costs in insurance claims data, predicting which patients would be expensive in the future based on who was expensive in the past. Historically, less is spent on black patients than white patients, so the algorithm ended up perpetuating existing bias in healthcare.Therein lies the danger of using narrow datasets in Artificial Intelligence: If the data is biased, the AI will be biased. That doesn't mean we should (or, now that the genie is out of the bottle, can) abandon AI.