Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.

Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows. This could quickly become problematic, particularly where guidance or regulations change and once-acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate. This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and contributors in the AI systems that they rely on. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated through models published at ModelHub and PyTorch Hub, popular archives for sharing science resources, and is found to be helpful in driving consideration of the contributions made to generating AI systems and approaches towards effective documentation and improving transparency in machine learning assets shared within scientific communities.

Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling such intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups based on race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at github.com/bhavyaghai/WordBias.

Algorithms are the heartbeat of applications, but they may not be perceived as entirely benign by their intended beneficiaries. Most educated people know that an algorithm is simply any stepwise computational procedure. Most computer programs are algorithms of one sort of another. Embedded in operational applications, algorithms make decisions, take actions, and deliver results continuously, reliably, and invisibly. But on the odd occasion that an algorithm stings -- encroaching on customer privacy, refusing them a home loan, or perhaps targeting them with a barrage of objectionable solicitation -- stakeholders' understandable reaction may be to swat back in anger, and possibly with legal action.

Just like In numerous enterprises, AI provides extraordinary benefits as well as risks for the legal industry. In the court framework, however, the stakes are uncommonly high. Utilizing a predictive algorithm to decide your kid's custody terms isn't exactly equivalent to Netflix recommending which film you should watch next. Most specialists in AI report that in the future AI will turn into a replacement for human jobs. Xiaofa stands in Beijing No 1 Intermediate People's Court, offering legal guidance and assisting general society with getting hold of legal terminology.

If you follow the latest AI news, you probably came across several stunning applications of the latest Language Model (LM) released by OpenAI: GPT-3. The applications that this LM can fuel reach from question answering to generating Python code. The list of use cases is growing daily. Check out the following youtube videos: GPT-3 demo and explanation, 14 cool GPT-3 apps and 14 more GPT-3 apps. GPT-3 is currently in beta and only a restricted number of people have access, but will be released to everybody on October 1st.

In 2018, William Frederick Keck III pleaded guilty in a court in Manassas, Virginia, to possession with intent to distribute cannabis. He served three months in prison, then began a three-year probation. He was required to wear a GPS ankle monitor before his trial and then to report for random drug tests after his release. Eventually, the state reduced his level of monitoring to scheduled meetings with his parole officer. Finally, after continued good behaviour, Keck's parole officer moved him to Virginia's lowest level of monitoring: an app on his smartphone.

Over the past few decades, artificial intelligence (AI) technology has been increasingly popularised and applied across various fields. Today, AI has a significant impact on our lives and is becoming a research hotspot all over the world. While AI is welcomed in many ways, there are some potential risks caused by the misuse of the technology. This includes criminal activities performed by using AI, and the violation of moral rules done by the unregulated collection of data when developing certain AI technologies. Besides the law, governmental regulations on AI are necessary to support its development and simultaneously prevent its misuse.

Technological advancements are significantly influencing the legal services landscape. At unprecedented rates, corporations, law firms, and state and federal enforcement agencies are accepting and adopting the use of advanced technology in legal matters, including automation, machine learning, and algorithm-driven data analytics. With respect to discovery, over the past decade, the expansion of technology-assisted review has been well documented and debated. The wide embrace of technology-assisted review – or "TAR" for short, has met with acclaim from clients and their counsel. It is essentially undisputed by now, for instance, that TAR has proven to help produce quality results, while also achieving quantifiable cost savings.

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI-HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domain-specific adaptations, and removed redundancy.