With the rising influence of machine learning algorithms on many important aspects of our daily lives, there are growing concerns that biases inherent in data can lead the behavior of these algorithms to discriminate against certain populations [1, 2, 4, 6, 8, 28, 29, 15]. In recent years, substantial research effort has been devoted to the development of mathematical definitions of bias, or its opposite, fairness, in algorithms and in data [15, 18, 26, 23, 19, 32]. In this work, we focus on the fairness scenario where there are multiple protected attributes that we aim to ensure fairness for, and which may potentially overlap with each other, such as gender, race, and sexual orientation. Our guiding principle is intersectionality, the core theoretical framework underlying the thirdwave feminist movement [13]. The principle of intersectionality states that racism, sexism, and other social systems which harm marginalized groups are interlocking in their effects, such that the lived experience of, e.g., black women, is very different than that of, e.g., white women. Intersectionality was defined by Kimberlé Crenshaw in the 1980's [13] and popularized in the 1990's, e.g. by Patricia Hill Collins [10], although the ideas are much older [11, 35]. In the context of machine learning and fairness, intersectionality was recently considered by [7], who studied the impact of the intersection of gender and skin color on computer vision performance, and by [23, 19], who aimed to protect certain subgroups in order to prevent "fairness gerrymandering."

For a growing number of fashion companies artificial Intelligence is already transforming the methods used to predict trends create products and interact with suppliers and customers.

Earlier this month, the Intercept broke the news that Google was building a secret program called Dragonfly, a censored search engine the company was potentially hoping to bring to China. Unlike the Google search that's used in the rest of the world, this search engine would block websites that are banned by the Chinese government and would not answer certain questions that the Chinese government has blacklisted. To learn more about Dragonfly, I recently spoke with Ryan Gallagher, a U.K.-based investigative journalist who reports on digital security and state surveillance issues for the Intercept. In our conversation, part of this week's episode of Slate's technology podcast If Then, we discussed why Google wants so desperately to enter China, why many of its employees oppose the plan, and how even a co-founder of the company may have been kept in the dark as Dragonfly was developed. Gallagher detailed how Google CEO Sundar Pichai has had several highly secretive meetings with the Chinese government about bringing Google to China over the past year--and the timeline of Google's plans to deploy the project once Beijing gave the green light.

Translating between human languages is something which artificial intelligence – specifically machine learning – has proven to be very competent at. So much so that the CEO of one of the world's largest employers of human translators has warned that many of them should be facing up to the stark reality of losing their job to a machine. One Hour Translation CEO Ofer Shoshan told me that within one to three years, neural machine technology (NMT) translators will carry out more than 50% of the work handled by the $40 billion market. His words stand in stark contrast to the often-repeated maxim that, in the near future at least, artificial intelligence will primarily augment, rather than replace, human professionals. Shoshan told me that the quality of machine translation has improved by leaps and bounds in recent years, to the point where half a million human translators and 21,000 agencies could soon find themselves out of work.

Attorney involvement has been steadily driving up workers' compensation claims costs over the past decade, and research reveals the problem has never been worse. For example, in 2014, the California Workers' Compensation Institute (CWCI) published results of a five-year study that showed when a single injured party brought in a lawyer, the associated costs per claim went up by an average of $40,000 for permanent disability payments and $25,000 for temporary total disability benefits -- even if the case never went to court. In Florida from 2016-2017, legal fees related to workers' comp claims totaled nearly $440 million -- approximately $254 million of that came from employers defending claims while injured workers themselves were responsible for $186 million (an increase of 36 percent in just one year). And then there is the national scale. In a newly released study from CLARA analytics, data collected from a single national payer from injury years 2007-2017 was focused on closed indemnity claims across multiple states.

For those looking to take advantage of emerging technologies coming down the pipeline, figuring out what artificial intelligence means for your business might be a good place to start. In its latest look at technologies that could drive competitive advantage, Gartner predicts about 80% of emerging tech will incorporate AI within the next two years. CIO Journal's Sara Castellanos has more. AI is becoming easier for enterprises to access via the cloud and integrate with other technologies such as edge computing, Gartner says. The use of virtual assistants and deep neural networks, which roughly try to mimic the operations of the human brain, are predicted to become mainstream within two to five years.

Machine learning researchers have stirred controversy by claiming our faces may reveal our sexual orientation and intelligence.a Using a database of prisoners' faces, some have even developed stereotyped images of criminal features.b A start-up now claims it can deploy facial recognition to identify pedophiles and terrorists.c Facial inferences via machine learning are deeply troubling. When such methods of pattern recognition are used to classify persons, they overstep a fundamental boundary between objective analysis and moral judgment. When such moral judgments are made, people deserve a chance to understand and contest them.

In a world constantly disrupted by technological advances, how can we make sure we're the ones making change, and that change isn't, in a sense, making us? That was a question posited by keynote speaker and futurist Anab Jain at the International Association of Business Communicators' (IABC) World Conference on June 4 in Montréal. Jain, a filmmaker, designer and educator, focuses her work on imagining many possible eventualities. Jain's design firm Superflux creates films, public art and prototypes that aim to "trace threads to the future." Communicators and creators, Jain told IABC attendees, are responsible for considering multiple future contexts for the things we build. "It feels as if the world around us is increasing in technological complexity, but it's becoming harder for us to comprehend," Jain said.

Artificial intelligence (AI) is not just a new technology that requires regulation. It is a powerful force that is reshaping daily practices, personal and professional interactions, and environments. For the well-being of humanity it is crucial that this power is used as a force of good. Ethics plays a key role in this process by ensuring that regulations of AI harness its potential while mitigating its risks. AI may be defined in many ways.

Donald Trump has a lot more to worry about than just Robert Mueller. That much has been clear since April, when details began to emerge from public court filings regarding the FBI raid on Trump's personal attorney, Michael Cohen, who pleaded guilty on Tuesday to a number of criminal charges, including some stemming from his work for Trump. Instead, it was carried out by FBI agents acting in coordination with Robert S. Khuzami, a deputy U.S. attorney in the Southern District of New York. Mueller had referred the Cohen case to Khuzami's office, but that was as far as his involvement apparently went. As I wrote at the time, the distribution of the investigation to a second office served to "potentially inoculate [it] from Trump's attacks against Mueller and potential meddling in the broader Russia investigation." Samuel W. Buell, the former lead Enron prosecutor, told me that would make it much more difficult to kill the investigation with a Saturday Night Massacre–style firing spree.