The EU has identified artificial intelligence (AI) as one of the most relevant technologies of the 21st century and highlighted 1 its importance on the strategy for EU’s digital transformation. Having a wide range of applications, AI can contribute in areas as disparate as helping in the treatment of chronic diseases, fighting climate change or anticipating cybersecurity threats.
- MISUNDERSTANDING: Correlation implies causality.
- Fact: Causality requires more than finding correlations.
- MISUNDERSTANDING: When developing machine learning systems, the greater the variety of data, the better.
- Fact: ML training datasets must meet accuracy and representativeness thresholds.
- MISUNDERSTANDING: ML needs completely error-free training datasets.
- Fact: Well-performing ML systems require training datasets above a certain quality threshold.
- MISUNDERSTANDING: The development of ML systems requires large repositories of data or the sharing of datasets from different sources.
- Fact: Federated learning allows the development of machine learning systems without sharing training data sets
- MISUNDERSTANDING: ML models automatically improve over time.
- Fact: Once deployed, ML models performance may deteriorate and will not improve unless it receives further training.
- MISUNDERSTANDING: Automatic decisions taken by ML algorithms cannot be explained.
- Fact: A well-designed ML model can produce decisions understandable to all relevant stakeholders.
- MISUNDERSTANDING: Transparency in ML violates intellectual property and is not understood by the user.
- Fact: It is possible to provide meaningful transparency to AI users without harming intellectual property.
- MISUNDERSTANDING: ML systems are less subject to human biases.
- Fact: ML systems are subjects to different types of biases and some of these come from human biases.
- MISUNDERSTANDING: ML can accurately predict the future.
- Fact: ML system predictions are only accurate when future events reproduce past trends.
- MISUNDERSTANDING: Individuals are able to anticipate the possible outcomes that ML systems can make of their data.
- Fact: The ability for ML to find nonevident correlations in data can end up with the discovery of new data, unknown to the data subject.