Timnit Gebru is a research scientist at Google on the ethical A.I. team and a co-founder of Black in AI, which promotes people of color in the field. Dr. Gebru has been instrumental in moving a major international A.I. conference, the International Conference on Learning Representations, to Ethiopia next year after more than half of the Black in AI speakers could not get visas to Canada for a conference in 2018. She talked about the foundational origins of bias and the larger challenge of changing the scientific culture. Their comments have been edited and condensed. You could mean bias in the sense of racial bias, gender bias.

We can already see the potential for artificial intelligence (AI) in international development: the seemingly endless possibilities to enhance productivity and innovation across healthcare, agriculture, education, transportation, and governance. Yet it is also becoming abundantly clear that AI could have negative repercussions as well, particularly in countries with weaker institutional capacity and legal protections. AI has the potential to threaten democratic processes, employment, human rights and -- because of the weaponization of AI tools -- privacy, policing, and defense. Apart from these potential benefits and threats, the transformative potential of AI for both good and harm will be magnified in the Global South, where existing gender and socio-economic inequalities could either be tempered or exacerbated. Given the opportunities and potential consequences of new automation and mechanization techniques and advanced analysis through machine learning and neural networks, IDRC is investing in applied research across a number of domains to advance the public good with the use of artificial intelligence for development (AI4D).

Earlier this year new health tech start-up EQL unveiled its debut product at the Google Campus in London to an audience of industry leaders in the healthcare and insurance markets. But how did they turn a dream into reality? Improving healthcare through the use of smart technology had long been a passion for EQL founders Jason Ward and Pete Grinbergs. Jason had seen his mum, a nurse and his step-dad, a GP, struggle with the inefficiency in the NHS and work hard to ensure that their patients received the best care. He started life working in finance in the City and in 2015 set up a digital primary care start-up in 2015 which sadly didn't make the grade.

Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque CONCURRENT system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable MODULAR algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.

Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model--namely, Hierarchy-A ware Knowledge Graph E mbedding (HAKE)-- which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task. 1 Introduction Knowledge graphs are usually collections of factual triples--(head entity, relation, tail entity), which represent human knowledge in a structured way. In the past few years, we have witnessed the great achievement of knowledge graphs in many areas, such as natural language processing (Zhang et al. 2019), question answering (Huang et al. 2019), and recommendation systems (Wang et al. 2018). Although commonly used knowledge graphs contain billions of triples, they still suffer from the incompleteness problem that a lot of valid triples are missing, as it is impractical to find all valid triples manually. Therefore, knowledge graph completion, also known as link prediction in knowledge graphs, has attracted much attention recently. Link prediction aims to automatically predict missing links between entities based on known links. It is a challenging task as we Equal contribution. Inspired by word embeddings (Mikolov et al. 2013) that can well capture semantic meaning of words, researchers turn to distributed representations of knowledge graphs (aka, knowledge graph embeddings) to deal with the link prediction problem.

Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is one of the most successful and powerful algorithms for those tasks. However, its performance depends directly from the choice of the kernel function and their hyperparameters. The traditional choice of them, actually, can be computationally expensive to do the kernel choice and the tuning processes. In this article, it is proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time. The data study was performed in simulated data and over 27 real benchmarking datasets.

While most of humanity is suddenly on the net, the value of this singularity is hampered by the lack of credible digital identities: Social networking, person-to-person transactions, democratic conduct, cooperation and philanthropy are all hampered by the profound presence of fake identities, as illustrated by Facebook's removal of 5.4Bn fake accounts since the beginning of 2019. Here, we introduce the fundamental notion of a \emph{genuine personal identifier}---a globally unique and singular identifier of a person---and present a foundation for a decentralized, grassroots, bottom-up process in which every human being may create, own, and protect the privacy of a genuine personal identifier. The solution employs mutual sureties among owners of personal identifiers, resulting in a mutual-surety graph reminiscent of a web-of-trust. Importantly, this approach is designed for a distributed realization, possibly using distributed ledger technology, and does not depend on the use or storage of biometric properties. For the solution to be complete, additional components are needed, notably a mechanism that encourages honest behavior and a sybil-resilient governance system.

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI)--companies such as Intel, IBM and Google--to apply advanced computer algorithms to problems in space science. Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy.

CAIML #9 took place on November 14 at factor-a – part of Dept, demonstrating how AI can be used for social good and to address societal challenges. "Aid organizations and governments are applying great effort in resolving the negative impacts of food insecurity induced crisis like famines or mass migration. One of the most limiting resources these actors face is the lack of preparation time for consistent and sustainable planning for emergency relief like setting refugee camps or securing supply with food and energy. Hence, increasing the lead time for preparation is an essential step and will result in saving many lives. The aim of this research is to increase the lead time by developing a ML based mathematical prediction model that is able to compute the probability for food insecure areas by learning from historical data. For performing such computations, our prediction model is developed and trained on historic open access data for the Horn of Africa (2009-2018). We used precipitation and vegetation data derived by remote sensing, as well as socio-economic, medical, armed conflict and disaster data. To overcome spatial inconsistencies in the input data and to meet the requirements of spatially homogenous input for neural networks, all data has been converted to geo-referenced raster maps. Disaster and armed conflict data has been fitted to districts while local food market prices have been interpolated. The IPC has been used as the food security label. In order to find a prediction model, deep learning methods have been used. Several analyses were applied on the collected data such as multicollinearity checks and principal component analyses. Preliminary cross-validated results have encouraged us to further investigate the detection of food insecure areas using open access data."

Researchers at Carnegie Mellon University have developed a system that can accurately locate a shooter based on video recordings from as few as three smartphones. When demonstrated using three video recordings from the 2017 mass shooting in Las Vegas that left 58 people dead and hundreds wounded, the system correctly estimated the shooter's actual location--the north wing of the Mandalay Bay hotel. The estimate was based on three gunshots fired within the first minute of what would be a prolonged massacre. Alexander Hauptmann, research professor in CMU's Language Technologies Institute, said the system, called Video Event Reconstruction and Analysis (VERA), won't necessarily replace the commercial microphone arrays for locating shooters that public safety officials already use, although it may be a useful supplement for public safety when commercial arrays aren't available. One key motivation for assembling VERA was to create a tool that could be used by human rights workers and journalists who investigate war crimes, terrorist acts and human rights violations, Hauptmann said.