Ethics

Current Affairs

The UN AI body advises; it does not regulate

The un secretary-general launched an international advisory body on artificial intelligence. The source bounds the event: an advisory body produces analysis and recommendations; it does not legislate, regulate companies or by itself represent every state. The story teaches how to distinguish mandate, members, expected output, authority, consultation and legal effect.

2 min 29 Oct. 2023

AI Fundamentals

Machine-learning bias: evaluating fairness without false recipes

Eight foundational papers show how to define harms, measure group errors, face incompatible metrics and document data and models without confusing one number with justice.

4 min 9 May 2023

General Artificial Intelligence (AGI)

AGI ethics: governing a capability we still cannot measure

The ethics of artificial general intelligence becomes useful when it stops being a list of values and answers four testable questions: what the system can do, how much autonomy it gets, who is accountable, and how harm can be remedied.

4 min 9 May 2023

Language Models

Bias in Language Models: From a Label to Measurable Harm

There is no single fairness meter. A protocol for defining harms, measuring them, mitigating mechanisms, and monitoring the full system.

5 min 9 May 2023

Artificial Intelligence Glossary

Video Analysis

Video analysis applies computer vision to image sequences over time. We separate its tasks (classification, detection, tracking, segmentation, temporal understanding), survey the models from 3D convolutions to video transformers and video-language systems, and its risks of bias and surveillance.

16 Apr. 2023

Artificial Intelligence Glossary

Semi-Supervised Learning

Semi-supervised learning combines few labels with much unlabeled data. We explain when it works (smoothness, cluster and manifold assumptions), its methods (pseudo-labeling, consistency, graphs), why it is not the same as self-supervised learning, and its risk of confirmation bias.

8 Apr. 2023

Artificial Intelligence Glossary

Deep Learning

Deep learning uses many-layered networks to learn hierarchical representations of data, without hand-designed features. We review its fundamentals (backpropagation, representation learning), its architectures and scaling, and its limits: data, compute, robustness, interpretability, hallucinations and bias.

5 min 6 Apr. 2023

Artificial Intelligence Glossary

Ethics in Artificial Intelligence

Ethics in artificial intelligence studies the moral implications of designing, developing and deploying AI systems, and proposes principles so they are beneficial and respect people's rights. We explain those recurring principles, the major international frameworks (UNESCO, EU, OECD), the concrete problems they address and why it is an open debate.

5 min 31 Jan. 2023

Artificial Intelligence Glossary

Bias

«Bias» names at least four distinct concepts in AI: a neuron's intercept, an estimator's bias, the bias of the bias-variance tradeoff, and algorithmic bias. This entry pulls them apart one by one.

4 min 13 Jan. 2023