Deep learning has achieved tremendous success. However, unlike SVMs, which provide direct decision criteria and can be trained with a small dataset, it still has significant weaknesses due to its requirement for massive datasets during training and the black-box characteristics on decision criteria.

Deep learning has achieved tremendous success. However, unlike SVMs, which provide direct decision criteria and can be trained with a small dataset, it still has significant weaknesses due to its requirement for massive datasets during training and the black-box characteristics on decision criteria. To this end, we propose the DeepKKT condition, an adaptation of the traditional Karush-Kuhn-Tucker (KKT) condition for deep learning models, and confirm that generated Deep Support Vectors (DSVs) using this condition exhibit properties similar to traditional support vectors. This allows us to apply our method to few-shot dataset distillation problems and alleviate the black-box characteristics of deep learning models. Additionally, we demonstrate that the DeepKKT condition can transform conventional classification models into generative models with high fidelity, particularly as latent generation models using class labels as latent variables.

This disproportion often leads to biased model training, making the classifier inclined towards predicting the majority class in the inference phase[1, 2]. The class imbalance problem cannot be readily overlooked, as many real-world datasets related to critical tasks, such as those used in the medical field for disease identification, the finance sector for fraud detection, and network intrusion datasets used in cyber security, exhibit such asymmetric class distributions [3, 4, 5]. Existing machine learning and deep learning approaches primarily utilize resampling techniques to tackle class imbalance which involves adjustment techniques to balance the class distribution in datasets [6, 7]. Among diverse resampling techniques, Oversampling approaches are commonly preferred for addressing class imbalance mainly due to their inherent ability to equalize the class distribution while preserving data semantics and achieving superior performance. There has been a plethora of different oversampling techniques proposed in the literature, ranging from traditional approaches [8, 9, 10, 11, 12] to those based on deep learning [13, 14, 15].

AI prevails in financial fraud detection and decision making. Yet, due to concerns about biased automated decision making or profiling, regulations mandate that final decisions are made by humans. Financial fraud investigators face the challenge of manually synthesizing vast amounts of unstructured information, including AI alerts, transaction histories, social media insights, and governmental laws. Current Visual Analytics (VA) systems primarily support isolated aspects of this process, such as explaining binary AI alerts and visualizing transaction patterns, thus adding yet another layer of information to the overall complexity. In this work, we propose a framework where the VA system supports decision makers throughout all stages of financial fraud investigation, including data collection, information synthesis, and human criteria iteration. We illustrate how VA can claim a central role in AI-aided decision making, ensuring that human judgment remains in control while minimizing potential biases and labor-intensive tasks.

Exploring bias in AI systems, and what we can do to prevent it. For business and non-profit leaders trying to understand AI, it can be surprisingly difficult to find good information in the sweet spot between high-level overview and technical jargon. The AI Clarified series attempts to fill this void and answer some of the most commonly asked AI questions with practical, easy-to-follow explanations. Question: Is AI more biased than humans, or less? I've heard both and am not sure which side to believe. Indeed it's hard to know what to believe about bias in Artificial Intelligence (AI) systems when just reading articles online -- there is plenty of support in both directions. With the growth of AI and the widespread adaption of AI models, there is a lot of noise on both sides, especially for high-stakes use cases like those affecting humans. Let's take hiring as an example.

Maybe you've heard this one: Guy walks into a bar, and the bartender says "What'll you have?" Without missing a beat, the guy says, "Based on the last ten people you served, you tell me!" OK, we may not be served by automated bartenders yet, but it doesn't seem too far-fetched if you believe the hype. AI, or artificial intelligence, is the solution du jour to just about everything these days, but the promise of business automation is still just a bit ahead of the proof: while almost 70% of high-performing companies are actively looking for ways to use AI, just over half (57%) have a "completely defined plan" to actually use and benefit from it. What are AI and its companion ML (machine learning)? Think of the pair as a way for a system to respond to a request (that's the AI part) and to improve that response over time based on the outcomes it "observes" (that's the ML part.)

Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives. However, this may be insufficient in more realistic human domains. This work uses imitation learning to enable an agent in a constrained environment to concurrently reason about both its internal learning goals and environmental constraints externally imposed, all within its objective function. Experiments are conducted on a concept learning task to test generalization of the proposed algorithm to different environmental conditions and analyze how time and resource constraints impact efficacy of solving the learning problem. Our findings show the environmentally-aware learning agent is able to statistically outperform all other active learners explored under most of the constrained conditions. A key implication is adaptation for active learning agents to more realistic human environments, where constraints are often externally imposed on the learner.

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.

However, both are equally important concepts of data science. Having said that, there are several dissimilarities between the two concepts also. In case of regression, as we all know the predicted outcome is a numeric variable and that too continuous. For a classification task, the predicted outcome is not numeric at all and represents categorical classes or factors i.e. the outcome variable in such a task has to be assuming limited number of values which may be binary in nature (dichotomous) or multinomial (having more than 2 classes). We in our analysis are motivated to work only on the'classification' scheme of tasks from a predictive analysis domain keeping our focus not on regression trees but only on classification trees, as the name suggests'Classification and Regression Trees'.

This report describes progress in the development of an interactive computer program, termed MYCIN, that uses the clinical decision criteria of experts to advise physicans who request advice regarding selection of appropriate antimicrobial therapy for hospital patients with bacterial infections. Since patients with infectious diseases often require therapy before complete information about the organism becomes available, infectious disease experts have identified clinical and historical criteria that aid in the early selection of antimicrobial therapy. MYCIN gives advice in this area by means of three subprograms: (1) A Consultation System that uses information provided by the physician, together with its own knowledge base, to choose an appropriate drug or combination of drugs; (2) An Explanation System that understands simple English questions and answers them in order to justify its decisions or instruct the user; and (3) A Rule Acquisition System that acquires decision criteria during interactions with an expert and codes them for use during future consultation sessions. A variety of human engineering capabilities have been included to heighten the program's acceptability to the physicians who will use it. Early experience indicates that a sample knowledge base of 200 decision criteria can be used by MYCIN to give appropriate advice for many patients with bacteremia.