What are the latest evaluation methods for AI algorithms?

?? A past example of algorithm failure is the Knight Capital Group incident back in 2012. I researched this incident for a school project. Knight was a global financial service involved with market making, electronic trading and institutional sales. They deployed an untested software to a production environment that had a dormant algorithm that sent out mass inaccurate unintentional orders resulting in a $440 MM loss. The algorithm didn’t have an issue with speed as it bought and sold millions of shares in under 45 minutes ?? but it sure lacked accuracy. It was supposed to replace old code but instead it interacted with it + unpredictable. Algorithms can be destructive if not properly vetted. Accuracy is a priority above speed.

In many practical applications, computing a response in a short period of time is required. In autonomous driving systems, as an example, an object needs to be identified, or a decision needs to be made, in a fraction of a second, especially at high speeds. Naturally, accuracy cannot be scarified. Hence, selecting suitable algorithms, evaluation methods and metrics is of great importance.

AI is like a cooking robot. "Accuracy" means how often it cooks the right meal. If it often makes mistakes, that's bad. "Speed" is how fast it cooks. If it's super slow, that's frustrating. Balancing both is tricky. Imagine your robot as a translator. If it's very fast but often wrong, you won't understand. That's bad. If it's super accurate but slow, you get frustrated waiting. So, finding the right balance is the challenge. For your cooking robot, you'd want tasty meals quickly. And for a translator AI, it should be fast but also get translations right, so you can understand. That's how accuracy and speed work in AI.

$100 BILLION, that's what inaccuracy in #AI can cost you. Earlier this year Google's stocks dropped 9% when their AI Chatbot BARD gave inaccurate answers. The responses went viral on internet causing a chain reaction resulting in loss of more than a BILLION dollars.

Evaluating AI algorithms is crucial for ensuring their reliability and efficiency. Some important methods include: 1. Explainability tools like LIME or SHAP to interpret algorithmic decisions, enhancing transparency. 2. Adversarial testing, exposing models to complex scenarios to check robustness. 3. Fairness assessment tools evaluating biases, promoting ethical AI practices. 4. Automated machine learning (AutoML) systems for algorithm selection, hyperparameter tuning, and model assessment, increasing optimization. 5. Reinforcement learning evaluation, employing cumulative rewards for gauging decision-making algorithms. 6. Real-time monitoring with continuous feedback loops, adjusting deployed models dynamically.

The importance of robustness and generalization in AI algorithms for a project cannot be overstated. Robust algorithms can handle complex real-world scenarios, reducing the risk of errors. Generalization is vital as it enables algorithms to perform effectively on unseen data, a common occurrence. Robust models are less prone to overfitting, resulting in more reliable and cost-effective solutions. They enhance user satisfaction, scalability, and long-term viability while ensuring safety, security, and ethical considerations in critical applications like autonomous vehicles and healthcare. In summary, robustness and generalization are fundamental for the success, reliability, and ethical soundness of AI projects.

In order for your AI pipeline to be profitable, your models must be robust and generalize well to your domain. Robustness is achieved by deliberately including low-quality or even adversarial samples in the model training set. Generalization is achieved by balancing the variance and bias of an ML model. It is often useful to build a powerful model augmented with regularization in order to achieve better performance.

In AI research, robustness measures how well algorithms handle challenging inputs, while generalization assesses their ability to adapt to new data. These qualities are crucial for AI systems to function effectively in dynamic environments. Researchers evaluate them using techniques like cross-validation, diverse dataset testing, and introducing input perturbations.

The assessment of AI algorithms robustness and generalization is fundamental to guarantee their adaptability across diverse scenarios. Robustness evaluates an algorithm's capability to handle challenging inputs, including outliers and malicious attacks, while generalization assesses its performance on previously unseen data. For instance, in the context of autonomous driving AI, robustness examines the system's response to unexpected road conditions, like heavy snow, while generalization tests its competence in navigating unfamiliar routes. Techniques such as cross-validation and testing with multiple datasets play a vital role in evaluating these crucial aspects, ensuring that AI systems excel in dynamic, unpredictable environments.

Assessing the robustness and generalization of AI algorithms is paramount. It's not just about excelling in controlled conditions but being resilient in the face of real-world challenges and unpredictable scenarios. This is vital for building trustworthy and adaptable AI systems that can truly make a positive impact across diverse environments and applications.

There are two primary families of ML explainability. Global explainers (for example XGBoost feature importances) show which features the model generally uses to make decisions across all observations. Local explainers (for example, LIME), evaluate each individual observation and explain why the model made the prediction it did.

In AI assessment, explainability gauges an algorithm's ability to provide understandable reasons for its outputs, like justifications, visualizations, or counterfactuals. Transparency assesses how well an algorithm can reveal its inner workings, including formulas, diagrams, or reports. These factors are crucial for building trust, debugging, and improving AI systems. Evaluation methods encompass user surveys, expert reviews, and metrics rooted in information theory or logic.

Within healthcare, AI's role in diagnosing diseases necessitates clear, understandable explanations for both patient and healthcare provider confidence. Assessment methods encompass user surveys, expert appraisals, and metrics rooted in information theory or logic. This contemporary evaluation challenge centers on AI's capacity for explainability, ensuring comprehensible justifications for outputs, thus nurturing trust and facilitating troubleshooting. Additionally, transparency evaluates the AI's ability to unveil its internal workings, further bolstering trust and aiding stakeholders in grasping the AI's underlying processes.

Explainablility is of great importance in safety-critical systems. In the safety-critical systems, output is supposed to be in a pre-defined range or to adhere to particular paradigms for certain inputs. As an example, one can consider autonomous driving vehicles. Explainablity is important for the designer to ensure the safety of each distinct AI model used in AV and identify the root cause in the case something goes wrong after deployment.

Explainability involves assessing an algorithm's ability to provide comprehensible reasons for its output, fostering user trust and understanding. Transparency, on the other hand, focuses on revealing an algorithm's inner workings and parameters, promoting better debugging and refinement. For instance, let's consider a medical diagnosis AI. An explainable and transparent system can not only provide a diagnosis but also explain the reasoning behind it, assuring both doctors and patients. Evaluating these aspects can be done through techniques like user surveys, expert reviews, or metrics based on information theory or logic. #AI #EvaluationMethods #Explainability #Transparency #ArtificialIntelligence

I’m part of a study involving algorithmic bias. It’s fascinating… The study involves human behavior, perception, race, ethnicity + nationality. With facial recognition we can detect facial features. However one’s features may have nothing to do with their geographical influence nor with what behavior he or she may have. As culture is a factor in human behavior. Appearance may determine how we are treated in some cases but it is one dimensional to classify behavior in this manner. A hypothetical person with African features may be from the US. Culturally the behavioral patterns may differ from those who live in Africa. Ethics is a crucial part of combating algorithmic biases. Evaluation methods should be multidimensional.

Fairness in AI ensures no discrimination based on attributes like race, gender, or socioeconomic status. It seeks to eliminate bias and create equitable outcomes, often by addressing bias in data and model training. Ethics in AI considers the societal and moral impact, encompassing privacy, security, and individual rights. It involves aligning AI choices with societal values and following ethical guidelines and responsible AI principles.

Imagine an AI-driven loan approval system that can explain why a loan was denied by highlighting factors such as credit history and income. This enhances user trust, allows stakeholders to scrutinize decisions, and aids in debugging and fine-tuning algorithms. Techniques for evaluating these aspects include user surveys, expert reviews, and information theory or logic-based metrics. Furthermore, the assessment of fairness and ethics is paramount in the current AI landscape. Fairness ensures algorithms don't propagate bias, while ethics ensures alignment with societal values. By using statistical tests, impact assessments, or ethical frameworks, we can ascertain #FairnessInAI. #AI #EthicsInAI #AIAssessment

Evaluating ethics requires an interdisciplinary approach spanning law, moral philosophy, political theory, social sciences and public policy. There exist inherent tensions between Optimization of Accuracy and fairness/ethics that necessitate deliberation around appropriate trade-offs. But these goals are often compatible with thoughtful system design. Transparency and explainability mechanisms foster accountability and provide tools for bias detection - but fairness must be baked into the system from the start. Like accuracy and robustness, fairness metrics quantify properties of the system, while ethics provides normative guidance on what properties we should pursue.

My final thought as a summary of the importance of evaluation methods. I gave an example earlier how faulty algorithms that have not been vetted for their performance, reliability as well as accuracy can be devastating to the health and wellness of a business. In this scenario algorithms that have not been vetted can cause extreme financial loss and put a dent in the reputation of a company. We must minimize error, biases, and any inefficiencies overall. This is essential to make sure that your algorithms meet high quality standards and requirements towards its intended use. With a lack of accuracy and speed as well as efficiency it defeats the purpose of using algorithms. Thus these steps should not be overlooked.

Fairness in AI aims to prevent discrimination and bias in AI systems, ensuring equitable outcomes. Ethics in AI focuses on the societal and moral consequences of AI, aligning choices with societal values and norms. Both principles are crucial for responsible and equitable AI development.

é importante avaliar os métodos de IA também pelo impacto que causam no mundo real, sob ponto de vista de como afetam a sociedade, o meio ambiente e a economia, por exemplo. Essa avalia??o do impacto no mundo real ajuda a garantir que a IA seja desenvolvida e usada de maneira responsável e que seus benefícios superem quaisquer riscos potenciais.

Recently, a healthcare AI system was found making predictions based on the color of a patient's file binder, rather than the critical health data inside. This underlines a profound point: no matter how technologically advanced our evaluation metrics become, there's no substitute for human oversight. In our pursuit of explainability and transparency, let's remember that AI is a tool. It's our responsibility, as its stewards, to ensure its proper application, challenging our models, and constantly refining them. This iterative process, where technology meets human intuition, is where true innovation thrives.

Always be careful of AI responses to AI questions. Look at the "Explainability" section. Notice how both responses start with dictionary style answers? That's because they're AI generated. Evaluating your AI also factors in in evaluating responses. I'm not saying that they should pass the Turing test but they should certainly be a cut above a generic ChatGPT styled answer.