Advances in AI and computer graphics over the last several years are now being harnessed to create, modify, and disseminate modified or fabricated images, audio, and video content, often referred to broadly as synthetic media. These new content generation and modification capabilities have significant, global implications for the legitimacy of information online, the quality of public discourse, the safeguarding of human rights and civil liberties, and the health of democratic institutions--especially given that some of these techniques may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. The ability to create synthetic or manipulated content that is difficult to discern from real events frames the urgent need for developing new capabilities for detecting such content, and for authenticating trusted media and news sources. AI techniques are being developed to detect and defend against synthetic and modified content. However, further investment and collaboration will be required for the advancement and application of these techniques, and for strengthening capacity in organizations and communities affected by these developments.

How are you supposed to react when a robot calls you a "gook"? At first glance, ImageNet Roulette seems like just another viral selfie app – those irresistible 21st-century magic mirrors that offer a simulacrum of insight in exchange for a photograph of your face. Want to know what you will look like in 30 years? If you were a dog what breed would you be? That one went viral in 2016.

The goal of the article is to help you find a dataset from public data that you can use for your machine learning pipeline, whether it be for a machine learning demo, proof-of-concept, or research project. It may not always be possible to collect your own data, but by using public data, you can create machine learning pipelines that can be useful for a large number of applications. Without data you cannot be sure a machine learning model works. However, the data you need may not always be readily available. Data may not have been collected or labeled yet or may not be readily available for machine learning model development because of technological, budgetary, privacy, or security concerns. Especially in a business contexts, stakeholders want to see how a machine learning system will work before investing the time and money in collecting, labeling, and moving data into such a system. This makes finding substitute data necessary. This article wants to provide some light into how to find and use public data for various machine learning applications such as machine learning demos, proofs-of-concept, or research projects.

For several years, the satellite and commercial space sector has sought ways to automate equipment construction, foster innovation, and boost profitability. Artificial intelligence (AI) can support these efforts. More specifically, the technology can change the way global satellite operators and space agencies process data and transform how the sector operates across four key areas: manufacturing, imaging, telemetry, and spectrum usage. AI has the potential to significantly improve the satellite manufacturing process, particularly when meticulous engineering is required to assemble multiple pieces. Newly developed AI technologies can perform tedious, time-consuming yet necessary tasks, such as cleaning satellite parts.

We evaluate the folk wisdom that algorithms trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so bias arises due to selection into the training data. In our baseline model, the more biased the decision-maker is toward a group, the more the algorithm favors that group. We refer to this phenomenon as "algorithmic affirmative action." We then clarify the conditions that give rise to algorithmic affirmative action. Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training. We illustrate our main theoretical results in a simulation study applied to the New York City Stop, Question and Frisk dataset.

Recidivism prediction provides decision makers with an assessment of the likelihood that a criminal defendant will reoffend that can be used in pre-trial decision-making. It can also be used for prediction of locations where crimes most occur, profiles that are more likely to commit violent crimes. While such instruments are gaining increasing popularity, their use is controversial as they may present potential discriminatory bias in the risk assessment. In this paper we propose a new fair-by-design approach to predict recidivism. It is prototype-based, learns locally and extracts empirically the data distribution. The results show that the proposed method is able to reduce the bias and provide human interpretable rules to assist specialists in the explanation of the given results.

In this work, we present causal directed acyclic graphs (DAGs) as a unifying framework for the recent literature on fairness in dynamical systems. We advocate for the use of causal DAGs as a tool in both designing equitable policies and estimating their impacts. By visualizing models of dynamic unfairness graphically, we expose implicit causal assumptions which can then be more easily interpreted and scrutinized by domain experts. We demonstrate that this method of reinterpretation can be used to critique the robustness of an existing model/policy, or uncover new policy evaluation questions. Causal models also enable a rich set of options for evaluating a new candidate policy without incurring the risk of implementing the policy in the real world. We close the paper with causal analyses of several models from the recent literature, and provide an in-depth case study to demonstrate the utility of causal DAGs for modeling fairness in dynamical systems.

This 2018 study found that realization rates average 81%, and collection rates average only 85%. This means law firms are missing out on 31.2% of hours expended. Let's say a firm has 1000 hours logged in its time tracking system for a given month. At $200 per hour the firm would be owed $200,000. But the average realization rate of 81% means that only 810 hours actually get billed. Then with an average collection rate of 85%, the law firm only collects 688.5 hours.

Thanks to artificial intelligence and facial recognition, you can unlock your phone merely by showing your face to your screen. The technology is impressive but what's less understood, however, is just how AI classifies you behind the scenes through its algorithms. Now you can find this out thanks to ImageNet Roulette, where you can upload images of yourself and be tagged as a specific type of person and can grasp an understanding of how AI categorizes us. The results are entertaining at times but sometimes they're rude and borderline racist. Created as part of an art exhibition -- Training Humans -- at the Prada Foundation museum in Milan, ImageNet Roulette was made to show us how we as humans are classified by computer systems or machine learning systems.

An artificial-intelligence art project has been criticised for using racist and sexist tags to classify its users. When they share a selfie with ImageNet Roulette, the web app matches it to the ones it most closely resembles from an enormous library of profile photos. It then reveals the most popular tag, assigned to the matching pictures by human workers using data set WordNet. These include racial slurs, "first offender", "rape suspect", "spree killer", "newsreader", and "Batman". Those responsible for assigning the tags to the library pictures were recruited via a service offered by Amazon, called Mechanical Turk, which pays workers around the world pennies to perform small, monotonous tasks.