Feature Engineering for Machine Learning
Feature engineering is the process of selecting, modifying, or creating new features to improve model performance. The quality of the features you provide to your model greatly influences its predictive power. The goal of feature engineering is to make the raw data more useful for machine learning algorithms by creating features that highlight the most […]
Feature Sensitivity Analysis
In machine learning, features are the inputs that are fed into a model to make predictions. However, not all features contribute equally to the model’s accuracy. Feature Sensitivity Analysis helps quantify the impact of each feature on the model’s output and allows for better-informed feature selection, which is critical for building efficient, accurate models. Feature […]
Dataset Fairness
Dataset fairness is a cornerstone of building equitable and responsible AI systems. As AI permeates critical decision-making domains, the risks of biased datasets can lead to significant societal harm. Understanding and addressing biases in datasets is not merely a technical challenge but also a socio-ethical imperative. Challenges in Achieving Dataset Fairness Lack of Representation: […]
Fair Sampling Strategies for AI
Artificial Intelligence (AI) systems can inadvertently replicate and amplify societal biases, especially when trained on skewed or unrepresentative datasets. Addressing fairness in AI begins with how we sample data. For example: A recruitment algorithm trained on predominantly male candidate profiles might favor men over women. A facial recognition system with underrepresented samples from specific ethnic […]
AI Fairness – Legal and Ethical Compliance
There is increasing scrutiny around the use of artificial intelligence (AI) and algorithms, along with growing awareness of how these technologies can amplify certain consumer risks. Fairness requires handling personal data in ways that align with people’s reasonable expectations, avoiding actions that result in unjustified negative impacts on them. Using AI to process personal data […]
AI Fairness Metrics Selection
AI fairness is context-sensitive, with different domains and applications requiring tailored approaches. Majumder’s (2023) research highlights the existence of numerous fairness metrics, many of which are highly correlated, measuring similar aspects of fairness. For example, 26 classification fairness metrics were grouped into seven clusters based on their similarity. This clustering simplifies the fairness evaluation process […]
Understanding AI Fairness
AI fairness and Bias are becoming essential across ethical, social, commercial, and legal spheres. Fairness in AI encompasses a range of contexts, including avoiding discrimination based on protected characteristics like race and gender, adhering to fair processes, and ensuring equitable or consistent outcomes depending on the situation. It also involves preventing the exploitation of personal […]
Stakeholder Identification for Machine Learning
Understanding Stakeholders Key stakeholders must be involved in value creation and economic profit creation throughout the design, development, and deployment of the AI system. It is, therefore, wise to begin AI design by identifying any group or individuals who can affect or are affected by the AI system. In this stakeholder identification guide, we will […]
Aggregation Bias in Machine Learning
Understanding Aggregation Bias Machine learning plays a more significant role in shaping decisions that directly affect people’s lives, from determining job opportunities to influencing law enforcement practices. These algorithms learn patterns from past data and apply them to new situations. However, when there are flaws in the data or the way the model is built, […]
The Framing Trap in Machine Learning
Understanding Framing Bias In recent years, the spotlight on responsible AI and human-centered AI has intensified—driven by the failures of AI systems and their profound impact on individuals, organisations, and society. One significant underlying issue is what’s known as The Framing Trap in machine learning solutions. This trap arises when: The humans—whether internal teams or […]