In this study, machine learning models were constructed to predict whether judgments made by the European Court of Human Rights (ECHR) would lead to a violation of an Article in the Convention on Human Rights. The problem is framed as a binary classification task where a judgment can lead to a "violation" or "non-violation" of a particular Article. Using auto-sklearn, an automated algorithm selection package, models were constructed for 12 Articles in the Convention. To train these models, textual features were obtained from the ECHR Judgment documents using N-grams, word embeddings and paragraph embeddings. Additional documents, from the ECHR, were incorporated into the models through the creation of a word embedding (echr2vec) and a doc2vec model. The features obtained using the echr2vec embedding provided the highest cross-validation accuracy for 5 of the Articles. The overall test accuracy, across the 12 Articles, was 68.83%. As far as we could tell, this is the first estimate of the accuracy of such machine learning models using a realistic test set. This provides an important benchmark for future work. As a baseline, a simple heuristic of always predicting the most common outcome in the past was used. The heuristic achieved an overall test accuracy of 86.68% which is 29.7% higher than the models. Again, this was seemingly the first study that included such a heuristic with which to compare model results. The higher accuracy achieved by the heuristic highlights the importance of including such a baseline.

Systems incorporating Artificial Intelligence (AI) and machine learning (ML) techniques are increasingly used to guide decision-making in the healthcare sector. While AI-based systems provide powerful and promising results with regard to their classification and prediction accuracy (e.g., in differentiating between different disorders in human gait), most share a central limitation, namely their black-box character. Understanding which features classification models learn, whether they are meaningful and consequently whether their decisions are trustworthy is difficult and often impossible to comprehend. This severely hampers their applicability as decision-support systems in clinical practice. There is a strong need for AI-based systems to provide transparency and justification of predictions, which are necessary also for ethical and legal compliance. As a consequence, in recent years the field of explainable AI (XAI) has gained increasing importance. The primary aim of this article is to investigate whether XAI methods can enhance transparency, explainability and interpretability of predictions in automated clinical gait classification. We utilize a dataset comprising bilateral three-dimensional ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls. In our experiments, we included several gait classification tasks, employed a representative set of classification methods, and a well-established XAI method - Layer-wise Relevance Propagation - to explain decisions at the signal (input) level. The presented approach exemplifies how XAI can be used to understand and interpret state-of-the-art ML models trained for gait classification tasks, and shows that the features that are considered relevant for machine learning models can be attributed to meaningful and clinically relevant biomechanical gait characteristics.

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A few short years ago, personal digital assistants like Amazon's Alexa, Apple's Siri and Google Assistant sounded futuristic. Now, the future is here and this future is embedded, augmented and ubiquitous. Digital assistants can be found in your office, home, car, hotel, phone and many other places. They have recently undergone massive transformation and run on operating systems that are fuelled by artificial intelligence (AI). They observe and collect data in real-time and have the capability to pull information from different sources such as smart devices and cloud services and put the information into context using AI to make sense of the situation.

Think lawsuits involving humans are tricky? Try taking an intelligent robot to court. While autonomous robots with humanlike, all-encompassing capabilities are still decades away, European lawmakers, legal experts and manufacturers are already locked in a high-stakes debate about their legal status: whether it's these machines or human beings who should bear ultimate responsibility for their actions. The battle goes back to a paragraph of text, buried deep in a European Parliament report from early 2017, which suggests that self-learning robots could be granted "electronic personalities." Such a status could allow robots to be insured individually and be held liable for damages if they go rogue and start hurting people or damaging property.

California's passage of their "GDPR-lite" caught people off guard. We think this is part of a trend we've studied for a long time. Much of the current analysis misses key points, so it seems worth explaining. About two years ago, we asked several thought leaders in the U.S. about the odds we'd see legislation like the E.U. GDPR provides clear rights to E.U citizens, controlling data captured on-line.

Detecting biases in artificial intelligence has become difficult because of the impenetrable nature of deep learning. The central difficulty is in relating unobservable phenomena deep inside models with observable, outside quantities that we can measure from inputs and outputs. For example, can we detect gendered perceptions of occupations (e.g., female librarian, male electrician) using questions to and answers from a word embedding-based system? Current techniques for detecting biases are often customized for a task, dataset, or method, affecting their generalization. In this work, we draw from Psychophysics in Experimental Psychology---meant to relate quantities from the real world (i.e., "Physics") into subjective measures in the mind (i.e., "Psyche")---to propose an intellectually coherent and generalizable framework to detect biases in AI. Specifically, we adapt the two-alternative forced choice task (2AFC) to estimate potential biases and the strength of those biases in black-box models. We successfully reproduce previously-known biased perceptions in word embeddings and sentiment analysis predictions. We discuss how concepts in experimental psychology can be naturally applied to understanding artificial mental phenomena, and how psychophysics can form a useful methodological foundation to study fairness in AI.

This is an easy prediction to make, because even Tesla isn't claiming that its eye-catching angular steel beast will be available for sale in 2020. The company's own pitch is that production won't even begin until 2021, with owners receiving their first shipments in 2022. But the gap is relevant to Tesla's future: where the company was once genuinely ahead of the curve, in making beautiful electric cars that people wanted to buy, it has increasingly relied on beating its competitors to announcements, rather than actually shipping. The list of Elon Musk's as-yet-unfulfilled promises grows every year – but the electric fleets of BMW, Ford, General Motors and others grow faster. One of the most impressive, and futuristic, products to have come from Google in recent years, Duplex is an AI assistant that can make calls to local businesses on your behalf to do things like book appointments and find out opening times.

And implicit bias can show up in other forms of artificial intelligence software. A ProPublica investigation found that software by Northpoint, a consulting and research firm, used to predict the likelihood that criminal defendants would become repeat offenders overestimated risk for Black people and underestimated risk for white people. Black defendants were "77 percent more likely to be pegged as at higher risk of committing a future violent crime" than white defendants, according to the organization's research.

A distinction has been drawn in fair machine learning research between'group' and'individual' fairness measures. Many tec hnical research papers assume that both are important, but conflict ing, and propose ways to minimise the tradeoffs between these mea - sures. This paper argues that this apparent conflict is based on a misconception. It draws on theoretical discussions from within the fair machine learning research, and from political and legal philosophy, to argue that individual and group fairness are not fun da-mentally in conflict. First, it outlines accounts of egalita rian fairness which encompass plausible motivations for both group a nd individual fairness, thereby suggesting that there need be no conflict in principle. Second, it considers the concept of individual justice, from legal philosophy and jurisprudence which seems similar but actually contradicts the notion of individual fairness as proposed in the fair machine learning literature. The conclusi on is that the apparent conflict between individual and group fair ness is more of an artefact of the blunt application of fairness measures, rather than a matter of conflicting principles. In practice, this conflict may be resolved by a nuanced consideration of the sources of'unfairness' in a particular deployment context, and the ca refully justified application of measures to mitigate it.