What if our phones turned out to be racist, misogynistic, homophobic pricks? Artificial intelligence assistants such as Siri and Alexa are great for convenience and offer a number of benefits. But they also have a lot of flaws and discrimination is one of them. Google, Microsoft, and Facebook have all admitted this. And they're trying to perfect this tech so we can have more ethical, humane AI in our lives.

Big Data is unique in its size and scale. Add machine learning and Data Science, and this sheer volume will make it possible to reach unprecedented levels of accuracy and scope in predictions. When dealing with Big Data, there's no need to worry about insufficient sample sizes or test group results--because the sample size is no less than everything. So it's easy to think that with the famous 3 Vs (or 5, 6, 7 Vs) every single piece of possible input is at your service and disposal, meaning total control over complex, foolproof, endlessly scalable systems and services. However, when such vast and all-encompassing data amounts are processed automatically, numerous issues are bound to surface.

San Francisco, CA, April 26, 2019 – The Partnership on AI (PAI) has today published a report gathering the views of the multidisciplinary artificial intelligence and machine learning research and ethics community which documents the serious shortcomings of algorithmic risk assessment tools in the U.S. criminal justice system. These kinds of AI tools for deciding on whether to detain or release defendants are in widespread use around the United States, and some legislatures have begun to mandate their use. Lessons drawn from the U.S. context have widespread applicability in other jurisdictions, too, as the international policymaking community considers the deployment of similar tools. While criminal justice risk assessment tools are often simpler than the deep neural networks used in many modern artificial intelligence systems, they are basic forms of AI. As such, they present a paradigmatic example of the high-stakes social and ethical consequences of automated AI decision-making.

Artificial intelligence–connected sensors, tracking wristbands, and data analytics: We've seen this type of tech pop up in smart homes, cars, classrooms, and workplaces. And now, we're seeing these types of networked systems show up in a new frontier--prisons. Specifically, China and Hong Kong have recently announced that their governments are rolling out new artificial intelligence (AI) technology aimed at monitoring inmates in some prisons every minute of every day. In Hong Kong, the government is testing Fitbit-like devices to monitor individuals' locations and activities, including their heart rates, at all times. Some prisons will also start using networked video surveillance systems programmed to identify abnormal behavior, such as self-harm or violence against others.

Rapper, producer and technology entrepreneur will.i.am "The investment that society has put in AI (artificial intelligence) surpasses the investment for HI, which is human intelligence," said Will.i.am during a panel at the Dell Technologies World 2019 Conference on Wednesday. "It's so lopsided that subconsciously we know that we haven't invested in our youth, in our communities. We haven't invested in humanity to keep up with intelligent machines." Will.i.am was joined by Dell Chief Marketing Officer Allison Dew as well as Brynn Putnam, founder and CEO of Mirror, which makes a full-length mirror that doubles as a screen for streaming home workout classes.

Digital innovations and advances in AI have produced a range of novel talent identification and assessment tools. Many of these technologies promise to help organizations improve their ability to find the right person for the right job, and screen out the wrong people for the wrong jobs, faster and cheaper than ever before. These tools put unprecedented power in the hands of organizations to pursue data-based human capital decisions. They also have the potential to democratize feedback, giving millions of job candidates data-driven insights on their strengths, development needs, and potential career and organizational fit. In particular, we have seen the rapid growth (and corresponding venture capital investment) in game-based assessments, bots for scraping social media postings, linguistic analysis of candidates' writing samples, and video-based interviews that utilize algorithms to analyze speech content, tone of voice, emotional states, nonverbal behaviors, and temperamental clues.

Australian bioethicist Julian Savulescu has a knack for provocation. He says most of us would readily accept it if it benefited us. As for eugenics--creating smarter, stronger, more beautiful babies--he believes we have an ethical obligation to use advanced technology to select the best possible children. A protégé of the philosopher Peter Singer, Savulescu is a prominent moral philosopher at the University of Oxford, where he directs the Uehiro Centre for Practical Ethics. He sees nothing wrong with doping to help cyclists climb those steep mountains in the Tour de France. Some elite athletes will always cheat to boost their performance, so instead of trying to enforce rules that will be broken, he claims we'd be better off with a system that allows low-dose doping. So does Savulescu just get off being outrageous? "I actually think of myself as the voice of common sense," he says, though he admits to receiving his share of hate mail.

Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification.

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning latent representations of individuals that are statistically indistinguishable between protected groups while sufficiently preserving other information for classification. To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the distributions across different protected groups are similar. Our framework provides a theoretical guarantee with respect to statistical parity and individual fairness. Empirical results on four real-world datasets also show that the learned representation can effectively be used for classification tasks such as credit risk prediction while obstructing information related to protected groups, especially when removing protected attributes is not sufficient for fair classification.