Ethical Considerations in AI
Expert-defined terms from the Professional Certificate in Artificial Intelligence for Quality Management Pioneers course at London School of Planning and Management. Free to read, free to share, paired with a globally recognised certification pathway.
**Algorithmic Bias** #
**Algorithmic Bias**
Algorithmic bias refers to the presence of prejudice or unfairness in machine le… #
This bias can stem from various sources, including biased data, biased algorithm design, or biased decision-making processes. Addressing algorithmic bias is crucial to ensuring ethical AI development and deployment.
**Artificial Intelligence (AI)** #
**Artificial Intelligence (AI)**
AI is a branch of computer science focused on creating intelligent machines that… #
AI can be categorized into several subfields, including machine learning, deep learning, neural networks, and natural language processing. AI has the potential to revolutionize various industries, including quality management, by automating decision-making processes, identifying patterns, and predicting future trends.
**Bias** #
**Bias**
Bias refers to a systematic prejudice or inclination, often leading to unfair or… #
Bias can be intentional or unintentional and can manifest in various aspects of AI development and deployment, including data collection, algorithm design, and decision-making processes. Addressing bias is essential to ensuring ethical AI development and deployment.
**Data Augmentation** #
**Data Augmentation**
Data augmentation is a technique used to increase the size and diversity of a tr… #
This technique can help improve model performance, reduce overfitting, and address data scarcity. However, data augmentation must be applied carefully, as it can also introduce new sources of bias or noise.
**Data Quality** #
**Data Quality**
Data quality refers to the accuracy, completeness, and relevance of data used in… #
Ensuring data quality is crucial for building accurate and reliable models, as poor data quality can lead to biased, inaccurate, or unreliable outcomes. Addressing data quality issues involves techniques such as data preprocessing, data augmentation, and bias mitigation.
**Deep Learning** #
**Deep Learning**
Deep learning is a subset of machine learning and a type of artificial neural ne… #
Deep learning models can learn and extract complex features from large datasets, making them particularly well-suited for tasks such as image recognition, natural language processing, and speech recognition.
**Discrimination** #
**Discrimination**
Discrimination refers to the unfair or unjust treatment of individuals or groups… #
Discrimination can result from algorithmic bias, biased data, or biased decision-making processes. Addressing discrimination is essential to ensuring ethical AI development and deployment.
**Edge Cases** #
**Edge Cases**