Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.

Visual Entity Linking (VEL) is a task to link regions of images with their corresponding entities in Knowledge Bases (KBs), which is beneficial for many computer vision tasks such as image retrieval, image caption, and visual question answering. While existing tasks in VEL either rely on textual data to complement a multi-modal linking or only link objects with general entities, which fails to perform named entity linking on large amounts of image data. In this paper, we consider a purely Visual-based Named Entity Linking (VNEL) task, where the input only consists of an image. The task is to identify objects of interest (i.e., visual entity mentions) in images and link them to corresponding named entities in KBs. Since each entity often contains rich visual and textual information in KBs, we thus propose three different sub-tasks, i.e., visual to visual entity linking (V2VEL), visual to textual entity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL). In addition, we present a high-quality human-annotated visual person linking dataset, named WIKIPerson. Based on WIKIPerson, we establish a series of baseline algorithms for the solution of each sub-task, and conduct experiments to verify the quality of proposed datasets and the effectiveness of baseline methods. We envision this work to be helpful for soliciting more works regarding VNEL in the future. The codes and datasets are publicly available at https://github.com/ict-bigdatalab/VNEL.

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We first unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Then, we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training, allowing us to limit the annotation imbalance and improve binding predictions for novel proteins and ligands. We illustrate the value of AI-Bind by predicting drugs and natural compounds with binding affinity to SARS-CoV-2 viral proteins and the associated human proteins. We also validate these predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. Overall, AI-Bind offers a powerful high-throughput approach to identify drug-target combinations, with the potential of becoming a powerful tool in drug discovery.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

Artificial intelligence (AI) made a grand entrance into cyberspace with a promise to enhance how people interact with data. More so, it raises hopes of providing a stronger cybersecurity framework. Several years down the line, people are yet to get the most out of AI technology in cybersecurity. In some instances, it complicates data management and raises some security concerns. Artificial intelligence hardly goes unmentioned in discussions about cybersecurity.

Chinese employers have recently advertised for nearly a million employees with technical AI skills, according to an analysis from US think tank the Center for Security and Emerging Technology (CSET). The think tank sought to better understand China's AI workforce, to better comprehend the industry landscape, and to make sense of what China's needs mean for US demand for AI talent. "The AI workforce is global and in high demand, and a large share of top-tier technical talent in the United States is foreign-born. Given China is a major producer of AI-skilled talent, understanding its AI workforce could provide US policymakers with important insight," states a CSET Issue Brief titled "China's AI Workforce Assessing Demand for AI Talent". The document explains that such assessments are not easy given limited and opaque information streams from sources like Chinese ministries, state-sponsored media and anecdotal reporting.

We can't manage what we don't measure, goes the old business adage. This rings true more than ever today as the world faces a triple planetary crisis of climate change, nature and biodiversity loss, pollution, and waste. More climate data is available than ever before, but how that data is accessed, interpreted and acted on is crucial to managing these crises. One technology that is central to this is Artificial Intelligence (AI). So, what exactly does AI mean?

Just as Twitter starts banning accounts that impersonate its new owner Elon Musk, it seems the social media platform is also rejecting ads that criticize the Chief Twit's Tesla. The Dawn Project, an anti-Tesla advocacy group, recently took out a full-page advertisement in the New York Times that claims the carmaker's Full Self-Driving system'presents a life-threatening danger to child pedestrians.' The group attempted to promote the ad on Twitter, but received a notification that it was not approved due to being'political.' However, its founder posted the advertisement to his account and it has yet to be taken down. The advertisement discusses testing conducted by the group in October, which claims to show the system does not register or stop for small mannequins crossing a road.

Artificial Intelligence as a tech category has become so broad that it's nearly lost all meaning. It encompasses everything from chatbots to autonomous vehicles to scenes from Terminator 2. This ambiguity impacts AI's adoption across many businesses and increases the desire for data privacy protections and greater accountability in AI. Recently, President Biden unveiled an AI Bill of Rights designed to set an AI framework and new standards for AI in government. So how can agency leaders move AI forward responsibly and with confidence? Demystifying and defining what AI can do, or can't do, is the first step in the process.

Scientists from the University of Texas have developed a new AI model that can scan brains and read minds. It was developed with a hardship of over 7-years with an aim to help read the minds of people who cannot speak. The technology behind this new mode of communication decoding is called Functional Magnetic Resonance Imaging (fMRI) that conceptualizes arbitrary stimuli that a person's brain is grasping or analyzing as a natural language in real-time. In simple terms, scientists can scan three parts of the brain and feed that data scan to ML algorithms to analyze the natural language circulating in a person's mind. This can be achieved with the help of electrodes that are planted on the forehead or the shaved head of a person to read a subject's thoughts.