AI FUNDAMENTALS

Bias (Prejudice)

AI Fundamentals

Definition

Bias in AI refers to systematic distortions in a model's results, often caused by unrepresentative training data. For example, a CV screening system trained on historical data might unconsciously discriminate. Recognizing and mitigating bias is essential for ethical and reliable use of AI in business, especially in HR and customer service.

Related terms

Business & Strategy

AI Ethics

AI ethics is concerned with ensuring that AI systems are fair, transparent, non-discriminatory, and respectful of human rights. It includes avoiding bias, ensuring privacy, maintaining human control over critical decisions, and ensuring the explainability of results. The EU leads in AI ethical regulation with the AI Act.

Business & Strategy

Responsible AI

Responsible AI is the approach to developing and using AI that prioritizes safety, fairness, transparency, and accountability. It includes testing models for bias, documenting design decisions, monitoring results in production, and having plans to manage errors. For SMEs, adopting responsible AI means building trust with customers, employees, and regulators.

AI Fundamentals

Dataset

A dataset is a structured collection of data used to train, validate, or test an AI model. Dataset quality is directly proportional to the quality of results: incomplete, imbalanced, or erroneous data produce unreliable models. For SMEs, organizing and cleaning their business data is often the first step toward AI adoption.

EXPLORE

More terms in AI Fundamentals

Artificial Intelligence

Artificial intelligence is the computer science discipline that develops systems capable of performing tasks that normally require human intelligence: understanding language, recognizing images, making decisions. For Italian SMEs, AI represents a concrete opportunity to automate repetitive processes and achieve measurable competitive advantages.

Machine Learning

Machine learning is a subfield of AI in which systems automatically learn from data without being explicitly programmed for every scenario. ML algorithms analyze patterns in historical data to make predictions on new data. In business practice, it is used to forecast demand, classify documents, and optimize production processes.

Deep Learning

Deep learning is an advanced machine learning technique that uses neural networks with many layers (hence 'deep') to learn complex data representations. It underpins modern speech recognition, machine translation, and text generation systems. It requires large amounts of data but produces extraordinarily accurate results.

Neural Network

An artificial neural network is a computational model inspired by the workings of the human brain, composed of nodes (neurons) organized in interconnected layers. Each connection has a weight that is adjusted during training. Neural networks underpin nearly all modern AI systems, from image recognition to text generation.

Language Model

A language model is an AI system trained on enormous amounts of text to understand and generate natural language. Large language models (LLMs) such as GPT and Claude can write texts, answer questions, translate, and reason about complex problems. For businesses, they represent versatile assistants for any text-based task.

GPT

GPT (Generative Pre-trained Transformer) is the architecture behind ChatGPT, developed by OpenAI. It is a language model pre-trained on vast text corpora that can be used to generate content, answer questions, and perform complex text tasks. GPT-4 and subsequent versions have demonstrated increasingly advanced reasoning capabilities.

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