The manner in which different racial and gender groups are portrayed in news coverage plays a large role in shaping public opinion. As such, understanding how such groups are portrayed in news media is of notable societal value, and has thus been a significant endeavour in both the computer and social sciences. Yet, the literature still lacks a longitudinal study examining both the frequency of appearance of different racial and gender groups in online news articles, as well as the context in which such groups are discussed. To fill this gap, we propose two machine learning classifiers to detect the race and age of a given subject. Next, we compile a dataset of 123,337 images and 441,321 online news articles from New York Times (NYT) and Fox News (Fox), and examine representation through two computational approaches. Firstly, we examine the frequency and prominence of appearance of racial and gender groups in images embedded in news articles, revealing that racial and gender minorities are largely under-represented, and when they do appear, they are featured less prominently compared to majority groups. Furthermore, we find that NYT largely features more images of racial minority groups compared to Fox. Secondly, we examine both the frequency and context with which racial minority groups are presented in article text. This reveals the narrow scope in which certain racial groups are covered and the frequency with which different groups are presented as victims and/or perpetrators in a given conflict. Taken together, our analysis contributes to the literature by providing two novel open-source classifiers to detect race and age from images, and shedding light on the racial and gender biases in news articles from venues on opposite ends of the American political spectrum.

Machines don't have an IQ. This is a common mistake that some commentators make, i.e., the machine IQ will exceed human IQ at some point of time. A trivial example is how the Google Search Engine remembers everything, but still can't plan its way out of a paper bag. Turing's 1950 paper, "Computing Machinery and Intelligence" is one of the stepping stones for AI, which introduced many of the core ideas of AI, including Machine Learning (ML). The paper also proposed what we now call the Turing Test as a thought experiment, and it demolished several standard objections to the very possibility of machine intelligence.

For more than a decade, journalists and researchers have been writing about the dangers of relying on algorithms to make weighty decisions: who gets locked up, who gets a job, who gets a loan--even who has priority for COVID-19 vaccines. Rather than remove bias, one algorithm after another has codified and perpetuated it, as companies have simultaneously continued to more or less shield their algorithms from public scrutiny. The big question ever since: How do we solve this problem? Lawmakers and researchers have advocated for algorithmic audits, which would dissect and stress-test algorithms to see how they work and whether they're performing their stated goals or producing biased outcomes. And there is a growing field of private auditing firms that purport to do just that.

In the light of the Black Lives Matter protests, AI and facial recognition vendors and users are taking notice of concerns over racial bias and privacy, reports Ron Alalouff. The use of artificial intelligence (AI) has come under the spotlight recently, especially how algorithms can be biased against people of colour or women. And most recently, in the wake of the Black Lives Matter campaigns following the death of George Floyd in May, tech giants such as Amazon and IBM have suspended or withdrawn their facial recognition technologies which are based on AI algorithms. In the United States the issue of bias in AI is most explosive. Miriam Vogel, President and CEO of Equal AI, believes that while racism has its historical roots, "AI now plays a role in creating, exacerbating and hiding these disparities behind the facade of a seemingly neutral, scientific machine".

Artificial intelligence has moved into the commercial mainstream thanks to the growing prowess of machine learning algorithms that enable computers to train themselves to do things like drive cars, control robots or automate decision-making. Go to the web site to view the video. As robots, self-driving cars and other intelligent machines weave AI into everyday life, a new way of designing algorithms can help machine-learning developers build in safeguards against specific, undesirable outcomes like racial and gender bias, to help earn societal trust. But as AI starts handling sensitive tasks, such as helping pick which prisoners get bail, policy makers are insisting that computer scientists offer assurances that automated systems have been designed to minimize, if not completely avoid, unwanted outcomes such as excessive risk or racial and gender bias. A team led by researchers at Stanford and the University of Massachusetts Amherst published a paper Nov. 22 in Science suggesting how to provide such assurances.

Her research has uncovered racial and gender bias in facial analysis tools sold by companies such as Amazon that have a hard time recognizing certain faces, especially darker-skinned women. Buolamwini holds a white mask she had to use so that software could detect her face. Facial recognition technology was already seeping into everyday life -- from your photos on Facebook to police scans of mugshots -- when Joy Buolamwini noticed a serious glitch: Some of the software couldn't detect dark-skinned faces like hers. That revelation sparked the Massachusetts Institute of Technology researcher to launch a project that's having an outsize influence on the debate over how artificial intelligence should be deployed in the real world. Her tests on software created by brand-name tech firms such as Amazon uncovered much higher error rates in classifying the gender of darker-skinned women than for lighter-skinned men.

Reported by the New York Times, new tests of facial recognition technology suggest that Amazon's system has more difficulty identifying the gender of female and darker-skinned faces compared with similar facial recognition technology services provided by IBM and Microsoft. Amazon's Rekognition is a software application that sets out to identify specific facial features by comparing similarities in a large volume of photographs. The study is of importance, given that Amazon has been marketing its facial recognition technology to police departments and federal agencies, presenting the technology as an additional tool to aid those tasked with law enforcement to identify suspects more rapidly. This tendency has been challenged by the American Civil Liberties Union (See: "Orlando begins testing Amazon's facial recognition in public"). The new study comes from Inioluwa Deborah Raji (University of Toronto) and Joy Buolamwini (Massachusetts Institute of Technology) and it is titled "Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products."

Researchers from MIT and Stanford University found that that three different facial analysis programs demonstrate both gender and skin color biases. The full article will be presented at the Conference on Fairness, Accountability, and Transparency later this month. Specifically, the team looked at the accuracy rates of facial recognition as broken down by gender and race. "Researchers at a major U.S. technology company claimed an accuracy rate of more than 97 percent for a face-recognition system they'd designed. But the data set used to assess its performance was more than 77 percent male and more than 83 percent white."