Back to all Blog Posts

The AI Act is here – These are the risk classes you should know

  • Artificial Intelligence
  • Human-centered AI
  • Strategy
05. August 2024
·

Fabian Müller
COO

At the beginning of August, the AI Act of the European Union came into effect. The regulation aims to create a unified legal framework that governs the development and use of AI technologies in the EU. The world’s first comprehensive AI law is intended to ensure that AI systems are used safely in the EU and that risks are minimized. This brings extensive obligations for companies that develop and operate high-risk AI systems. We have compiled the most important information about the AI Act.

Legislation with a global impact

A unique feature of the law is the so-called market location principle: Accordingly, all companies that offer, operate, or have AI-generated output used within the EU are affected by the AI Act, regardless of their own location.

Artificial intelligence is defined as machine-based systems that can autonomously make predictions, recommendations or decisions and thus influence the physical and virtual environment. This applies, for example, to AI solutions that support the recruitment process, predictive maintenance solutions and chatbots such as ChatGPT. The legal requirements that different AI systems must fulfill vary greatly depending on their classification into risk classes.

Excluded from the regulation are AI systems developed for research or military purposes, made available as open-source systems, or used by authorities in law enforcement or the judiciary. Additionally, the use of AI systems for purely private purposes is exempt from the law.

The risk class determines the legal requirements

At the core of the law is the classification of AI systems into four risk categories. The higher the risk category, the greater the legal requirements that must be met.

The risk categories include:

  • low,
  • limited,
  • high,
  • and unacceptable risk.

These classes reflect the extent to which artificial intelligence jeopardizes European values and fundamental rights. AI systems that belong to the “unacceptable risk” category are prohibited by the AI Act.

Particularly comprehensive requirements apply to high-risk systems, which are divided into requirements for “Providers” (suppliers) and “Deployers” (users), “Distributors” (dealers), and “Importers” (importers).

We will explain which AI systems fall into which risk category and the associated requirements below.

Ban on social scoring and biometric remote identification

Some AI systems have a significant potential to violate human rights and fundamental principles, which is why they are categorized as “unacceptable risk”. These include:

  • Real-time based remote biometric identification systems in publicly accessible spaces (exception: law enforcement agencies may use them to prosecute serious crimes);
  • Biometric remote identification systems in retrospect (exception: law enforcement authorities may use them to prosecute serious crimes);
  • Biometric categorization systems that use sensitive characteristics such as gender, ethnicity or religion;
  • Predictive policing based on so-called profiling – i.e. profiling based on skin color, suspected religious affiliation and similarly sensitive characteristics – geographical location or previous criminal behavior;
  • Systems for emotion recognition in the workplace and educational institutions, except for medical and safety reasons;
  • Arbitrary extraction of biometric data from social media or video surveillance footage to create facial recognition databases;
  • Social scoring leading to disadvantage in social contexts;
  • AI systems that exploit the vulnerabilities of a specific group of people due to their age, disability, or a particular social or economic situation, which can lead to behaviors causing physical or psychological harm;
  • AI systems that use manipulative, deceptive, and subliminal techniques to maliciously influence decisions.

These AI systems will be banned on the European market under the AI Act with a deadline until February 2025.

Numerous requirements for AI with risks to health, safety and fundamental rights

The “high risk” category includes all AI systems that are not explicitly prohibited but nevertheless pose a high risk to health, safety or fundamental rights. The following areas of application and use are explicitly mentioned:

  • Biometric and biometric-based systems that do not fall into the “unacceptable risk” risk class;
  • Management and operation of critical infrastructure;
  • Education and training;
  • Access and entitlement to basic private and public services and benefits;
  • Employment, human resource management and access to self-employment;
  • Law enforcement;
  • Migration, asylum and border control;
  • Administration of justice and democratic processes

However, an exception applies to these systems if either the system is intended to improve or correct the outcome of a previously completed human activity, or if it is designed to perform a very narrowly defined procedural task. This justification must be documented and made available to authorities upon request.

AI systems that fall under the EU’s product safety regulations listed in Annex I of the AI Act are also considered high-risk systems. This includes, for example, AI systems used as safety components in aviation, toys, medical devices, or elevators.

Providers of high-risk AI systems are subject to comprehensive legal requirements that must be implemented before commissioning and adhered to throughout the entire AI lifecycle:

  • Assessment of risks and effects on fundamental and human rights
  • Quality and risk management
  • Data governance structures
  • Quality requirements for training, test and validation data
  • Technical documentation and record-keeping obligations
  • Fulfillment of transparency and provision obligations
  • Human supervision, robustness, cyber security and accuracy
  • Declaration of conformity incl. CE marking obligation
  • Registration in an EU-wide database
  • Instructions for use for downstream deployers

In contrast to providers, who develop and market AI systems, deployers are generally operators who use third-party systems commercially. Deployers are subject to less stringent regulations than providers: They must use the high-risk AI system according to the provided instructions, carefully monitor input data, oversee the system’s operation, and keep logs.

Importers and dealers of high-risk AI systems must ensure that the provider has met all the measures required by the AI Act and recall the system if necessary. It is also important to note that any deployer, dealer, or importer is considered a provider under the AI Act if they market or operate the system under their own name or brand. This also applies if significant changes are made to the system.

AI with limited risk must comply with transparency obligations

AI systems that interact directly with humans fall into the “limited risk” category. This includes emotion recognition systems, biometric categorization systems, as well as AI-generated or altered audio, image, video, or text content. For these systems, which include, for example, chatbots, the AI Act mandates that consumers be informed about the use of artificial intelligence and that AI-generated output be declared as such.

No legal requirements for AI with low risk – but AI education is mandatory for everyone

Many AI systems, such as predictive maintenance or spam filters, fall into the “low risk” category. These systems are not subject to specific regulations under the AI Act.

For all providers and deployers of AI systems, regardless of their risk category, the EU dedicates an entire article to the promotion of AI competencies: Article 4 mandates regular AI training and further education for individuals who come into contact with AI systems.

GPAI models are regulated separately

The regulation for General Purpose AI models, which can perform a wide range of different tasks, was included in the AI Act in response to the emergence of AI models such as GPT-4. It concerns the developers of these models, such as OpenAI, Google, or Meta. Depending on whether a model is assessed as a “Systemic Risk” and whether it is open source and freely accessible, developers are subject to varying degrees of stringent obligations. Large models trained with over 10^25 FLOP computational power must meet numerous and strict requirements, such as technical documentation and risk evaluations, as they are classified as a “Systemic Risk.”

These rules also apply to the latest generation of AI models, including GPT-4o, Llama 3.1, or Claude 3.5.

Companies should start preparing for the AI Act now

Companies now have up to three years to comply with EU regulations. However, the ban on systems with unacceptable risk and the obligation for AI education will come into effect in just six months. To ensure that processes and AI systems in your company are compliant with the law, the first step is to assess the risk class of each individual system. If you are not yet sure which risk classes your AI systems fall into, we recommend our free AI Act Quick Check. It helps you to assess the risk class. If you have any further questions about the AI Act, please feel free to contact us at any time.

More information:

Sources:

Linkedin Logo
Marcel Plaschke
Head of Strategy, Sales & Marketing
schedule a consultation
Zugehörige Leistungen
No items found.

More Blog Posts

  • Artificial Intelligence
AI Trends Report 2025: All 16 Trends at a Glance
Tarik Ashry
05. February 2025
Read more
  • Artificial Intelligence
  • Data Science
  • Human-centered AI
Explainable AI in practice: Finding the right method to open the Black Box
Jonas Wacker
15. November 2024
Read more
  • Artificial Intelligence
  • Data Science
  • GenAI
How a CustomGPT Enhances Efficiency and Creativity at hagebau
Tarik Ashry
06. November 2024
Read more
  • Artificial Intelligence
  • Data Culture
  • Data Science
  • Deep Learning
  • GenAI
  • Machine Learning
AI Trends Report 2024: statworx COO Fabian Müller Takes Stock
Tarik Ashry
05. September 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Back to the Future: The Story of Generative AI (Episode 4)
Tarik Ashry
31. July 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Back to the Future: The Story of Generative AI (Episode 3)
Tarik Ashry
24. July 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Back to the Future: The Story of Generative AI (Episode 2)
Tarik Ashry
04. July 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Back to the Future: The Story of Generative AI (Episode 1)
Tarik Ashry
10. July 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Generative AI as a Thinking Machine? A Media Theory Perspective
Tarik Ashry
13. June 2024
Read more
  • Artificial Intelligence
  • GenAI
  • statworx
Custom AI Chatbots: Combining Strong Performance and Rapid Integration
Tarik Ashry
10. April 2024
Read more
  • Artificial Intelligence
  • Data Culture
  • Human-centered AI
How managers can strengthen the data culture in the company
Tarik Ashry
21. February 2024
Read more
  • Artificial Intelligence
  • Data Culture
  • Human-centered AI
AI in the Workplace: How We Turn Skepticism into Confidence
Tarik Ashry
08. February 2024
Read more
  • Artificial Intelligence
  • Data Science
  • GenAI
The Future of Customer Service: Generative AI as a Success Factor
Tarik Ashry
25. October 2023
Read more
  • Artificial Intelligence
  • Data Science
How we developed a chatbot with real knowledge for Microsoft
Isabel Hermes
27. September 2023
Read more
  • Data Science
  • Data Visualization
  • Frontend Solution
Why Frontend Development is Useful in Data Science Applications
Jakob Gepp
30. August 2023
Read more
  • Artificial Intelligence
  • Human-centered AI
  • statworx
the byte - How We Built an AI-Powered Pop-Up Restaurant
Sebastian Heinz
14. June 2023
Read more
  • Artificial Intelligence
  • Recap
  • statworx
Big Data & AI World 2023 Recap
Team statworx
24. May 2023
Read more
  • Data Science
  • Human-centered AI
  • Statistics & Methods
Unlocking the Black Box – 3 Explainable AI Methods to Prepare for the AI Act
Team statworx
17. May 2023
Read more
  • Artificial Intelligence
  • Human-centered AI
  • Strategy
How the AI Act will change the AI industry: Everything you need to know about it now
Team statworx
11. May 2023
Read more
  • Artificial Intelligence
  • Human-centered AI
  • Machine Learning
Gender Representation in AI – Part 2: Automating the Generation of Gender-Neutral Versions of Face Images
Team statworx
03. May 2023
Read more
  • Artificial Intelligence
  • Data Science
  • Statistics & Methods
A first look into our Forecasting Recommender Tool
Team statworx
26. April 2023
Read more
  • Artificial Intelligence
  • Data Science
On Can, Do, and Want – Why Data Culture and Death Metal have a lot in common
David Schlepps
19. April 2023
Read more
  • Artificial Intelligence
  • Human-centered AI
  • Machine Learning
GPT-4 - A categorisation of the most important innovations
Mareike Flögel
17. March 2023
Read more
  • Artificial Intelligence
  • Data Science
  • Strategy
Decoding the secret of Data Culture: These factors truly influence the culture and success of businesses
Team statworx
16. March 2023
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
How to create AI-generated avatars using Stable Diffusion and Textual Inversion
Team statworx
08. March 2023
Read more
  • Artificial Intelligence
  • Human-centered AI
  • Strategy
Knowledge Management with NLP: How to easily process emails with AI
Team statworx
02. March 2023
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
3 specific use cases of how ChatGPT will revolutionize communication in companies
Ingo Marquart
16. February 2023
Read more
  • Recap
  • statworx
Ho ho ho – Christmas Kitchen Party
Julius Heinz
22. December 2022
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
Real-Time Computer Vision: Face Recognition with a Robot
Sarah Sester
30. November 2022
Read more
  • Data Engineering
  • Tutorial
Data Engineering – From Zero to Hero
Thomas Alcock
23. November 2022
Read more
  • Recap
  • statworx
statworx @ UXDX Conf 2022
Markus Berroth
18. November 2022
Read more
  • Artificial Intelligence
  • Machine Learning
  • Tutorial
Paradigm Shift in NLP: 5 Approaches to Write Better Prompts
Team statworx
26. October 2022
Read more
  • Recap
  • statworx
statworx @ vuejs.de Conf 2022
Jakob Gepp
14. October 2022
Read more
  • Data Engineering
  • Data Science
Application and Infrastructure Monitoring and Logging: metrics and (event) logs
Team statworx
29. September 2022
Read more
  • Coding
  • Data Science
  • Machine Learning
Zero-Shot Text Classification
Fabian Müller
29. September 2022
Read more
  • Cloud Technology
  • Data Engineering
  • Data Science
How to Get Your Data Science Project Ready for the Cloud
Alexander Broska
14. September 2022
Read more
  • Artificial Intelligence
  • Human-centered AI
  • Machine Learning
Gender Repre­sentation in AI – Part 1: Utilizing StyleGAN to Explore Gender Directions in Face Image Editing
Isabel Hermes
18. August 2022
Read more
  • Artificial Intelligence
  • Human-centered AI
statworx AI Principles: Why We Started Developing Our Own AI Guidelines
Team statworx
04. August 2022
Read more
  • Data Engineering
  • Data Science
  • Python
How to Scan Your Code and Dependencies in Python
Thomas Alcock
21. July 2022
Read more
  • Data Engineering
  • Data Science
  • Machine Learning
Data-Centric AI: From Model-First to Data-First AI Processes
Team statworx
13. July 2022
Read more
  • Artificial Intelligence
  • Deep Learning
  • Human-centered AI
  • Machine Learning
DALL-E 2: Why Discrimination in AI Development Cannot Be Ignored
Team statworx
28. June 2022
Read more
  • R
The helfRlein package – A collection of useful functions
Jakob Gepp
23. June 2022
Read more
  • Recap
  • statworx
Unfold 2022 in Bern – by Cleverclip
Team statworx
11. May 2022
Read more
  • Artificial Intelligence
  • Data Science
  • Human-centered AI
  • Machine Learning
Break the Bias in AI
Team statworx
08. March 2022
Read more
  • Artificial Intelligence
  • Cloud Technology
  • Data Science
  • Sustainable AI
How to Reduce the AI Carbon Footprint as a Data Scientist
Team statworx
02. February 2022
Read more
  • Recap
  • statworx
2022 and the rise of statworx next
Sebastian Heinz
06. January 2022
Read more
  • Recap
  • statworx
5 highlights from the Zurich Digital Festival 2021
Team statworx
25. November 2021
Read more
  • Data Science
  • Human-centered AI
  • Machine Learning
  • Strategy
Why Data Science and AI Initiatives Fail – A Reflection on Non-Technical Factors
Team statworx
22. September 2021
Read more
  • Artificial Intelligence
  • Data Science
  • Human-centered AI
  • Machine Learning
  • statworx
Column: Human and machine side by side
Sebastian Heinz
03. September 2021
Read more
  • Coding
  • Data Science
  • Python
How to Automatically Create Project Graphs With Call Graph
Team statworx
25. August 2021
Read more
  • Coding
  • Python
  • Tutorial
statworx Cheatsheets – Python Basics Cheatsheet for Data Science
Team statworx
13. August 2021
Read more
  • Data Science
  • statworx
  • Strategy
STATWORX meets DHBW – Data Science Real-World Use Cases
Team statworx
04. August 2021
Read more
  • Data Engineering
  • Data Science
  • Machine Learning
Deploy and Scale Machine Learning Models with Kubernetes
Team statworx
29. July 2021
Read more
  • Cloud Technology
  • Data Engineering
  • Machine Learning
3 Scenarios for Deploying Machine Learning Workflows Using MLflow
Team statworx
30. June 2021
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
Car Model Classification III: Explainability of Deep Learning Models With Grad-CAM
Team statworx
19. May 2021
Read more
  • Artificial Intelligence
  • Coding
  • Deep Learning
Car Model Classification II: Deploying TensorFlow Models in Docker Using TensorFlow Serving
No items found.
12. May 2021
Read more
  • Coding
  • Deep Learning
Car Model Classification I: Transfer Learning with ResNet
Team statworx
05. May 2021
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
Car Model Classification IV: Integrating Deep Learning Models With Dash
Dominique Lade
05. May 2021
Read more
  • AI Act
Potential Not Yet Fully Tapped – A Commentary on the EU’s Proposed AI Regulation
Team statworx
28. April 2021
Read more
  • Artificial Intelligence
  • Deep Learning
  • statworx
Creaition – revolutionizing the design process with machine learning
Team statworx
31. March 2021
Read more
  • Artificial Intelligence
  • Data Science
  • Machine Learning
5 Types of Machine Learning Algorithms With Use Cases
Team statworx
24. March 2021
Read more
  • Recaps
  • statworx
2020 – A Year in Review for Me and GPT-3
Sebastian Heinz
23. Dezember 2020
Read more
  • Artificial Intelligence
  • Deep Learning
  • Machine Learning
5 Practical Examples of NLP Use Cases
Team statworx
12. November 2020
Read more
  • Data Science
  • Deep Learning
The 5 Most Important Use Cases for Computer Vision
Team statworx
11. November 2020
Read more
  • Data Science
  • Deep Learning
New Trends in Natural Language Processing – How NLP Becomes Suitable for the Mass-Market
Dominique Lade
29. October 2020
Read more
  • Data Engineering
5 Technologies That Every Data Engineer Should Know
Team statworx
22. October 2020
Read more
  • Artificial Intelligence
  • Data Science
  • Machine Learning

Generative Adversarial Networks: How Data Can Be Generated With Neural Networks
Team statworx
10. October 2020
Read more
  • Coding
  • Data Science
  • Deep Learning
Fine-tuning Tesseract OCR for German Invoices
Team statworx
08. October 2020
Read more
  • Artificial Intelligence
  • Machine Learning
Whitepaper: A Maturity Model for Artificial Intelligence
Team statworx
06. October 2020
Read more
  • Data Engineering
  • Data Science
  • Machine Learning
How to Provide Machine Learning Models With the Help Of Docker Containers
Thomas Alcock
01. October 2020
Read more
  • Recap
  • statworx
STATWORX 2.0 – Opening of the New Headquarters in Frankfurt
Julius Heinz
24. September 2020
Read more
  • Machine Learning
  • Python
  • Tutorial
How to Build a Machine Learning API with Python and Flask
Team statworx
29. July 2020
Read more
  • Data Science
  • Statistics & Methods
Model Regularization – The Bayesian Way
Thomas Alcock
15. July 2020
Read more
  • Recap
  • statworx
Off To New Adventures: STATWORX Office Soft Opening
Team statworx
14. July 2020
Read more
  • Data Engineering
  • R
  • Tutorial
How To Dockerize ShinyApps
Team statworx
15. May 2020
Read more
  • Coding
  • Python
Making Of: A Free API For COVID-19 Data
Sebastian Heinz
01. April 2020
Read more
  • Frontend
  • Python
  • Tutorial
How To Build A Dashboard In Python – Plotly Dash Step-by-Step Tutorial
Alexander Blaufuss
26. March 2020
Read more
  • Coding
  • R
Why Is It Called That Way?! – Origin and Meaning of R Package Names
Team statworx
19. March 2020
Read more
  • Data Visualization
  • R
Community Detection with Louvain and Infomap
Team statworx
04. March 2020
Read more
  • Coding
  • Data Engineering
  • Data Science
Testing REST APIs With Newman
Team statworx
26. February 2020
Read more
  • Coding
  • Frontend
  • R
Dynamic UI Elements in Shiny – Part 2
Team statworx
19. Febuary 2020
Read more
  • Coding
  • Data Visualization
  • R
Animated Plots using ggplot and gganimate
Team statworx
14. Febuary 2020
Read more
  • Machine Learning
Machine Learning Goes Causal II: Meet the Random Forest’s Causal Brother
Team statworx
05. February 2020
Read more
  • Artificial Intelligence
  • Machine Learning
  • Statistics & Methods
Machine Learning Goes Causal I: Why Causality Matters
Team statworx
29.01.2020
Read more
  • Data Engineering
  • R
  • Tutorial
How To Create REST APIs With R Plumber
Stephan Emmer
23. January 2020
Read more
  • Recaps
  • statworx
statworx 2019 – A Year in Review
Sebastian Heinz
20. Dezember 2019
Read more
  • Artificial Intelligence
  • Deep Learning
Deep Learning Overview and Getting Started
Team statworx
04. December 2019
Read more
  • Coding
  • Machine Learning
  • R
Tuning Random Forest on Time Series Data
Team statworx
21. November 2019
Read more
  • Data Science
  • R
Combining Price Elasticities and Sales Forecastings for Sales Improvement
Team statworx
06. November 2019
Read more
  • Data Engineering
  • Python
Access your Spark Cluster from Everywhere with Apache Livy
Team statworx
30. October 2019
Read more
  • Recap
  • statworx
STATWORX on Tour: Wine, Castles & Hiking!
Team statworx
18. October 2019
Read more
  • Data Science
  • R
  • Statistics & Methods
Evaluating Model Performance by Building Cross-Validation from Scratch
Team statworx
02. October 2019
Read more
  • Data Science
  • Machine Learning
  • R
Time Series Forecasting With Random Forest
Team statworx
25. September 2019
Read more
  • Coding
  • Frontend
  • R
Dynamic UI Elements in Shiny – Part 1
Team statworx
11. September 2019
Read more
  • Machine Learning
  • R
  • Statistics & Methods
What the Mape Is FALSELY Blamed For, Its TRUE Weaknesses and BETTER Alternatives!
Team statworx
16. August 2019
Read more
  • Coding
  • Python
Web Scraping 101 in Python with Requests & BeautifulSoup
Team statworx
31. July 2019
Read more
  • Coding
  • Frontend
  • R
Getting Started With Flexdashboards in R
Thomas Alcock
19. July 2019
Read more
  • Recap
  • statworx
statworx summer barbecue 2019
Team statworx
21. June 2019
Read more
  • Data Visualization
  • R
Interactive Network Visualization with R
Team statworx
12. June 2019
Read more
  • Deep Learning
  • Python
  • Tutorial
Using Reinforcement Learning to play Super Mario Bros on NES using TensorFlow
Sebastian Heinz
29. May 2019
Read more
This is some text inside of a div block.
This is some text inside of a div block.