Autodesk Trust Center

Trusted AI at Autodesk

Autodesk delivers trusted AI that transforms how our customers make a better world. We prioritize customer needs and protect customer data through our Trust principles for AI.

Trust principles for AI

Autodesk is committed to responsible, ethical, and secure AI development, deployment, and use. We adhere to strict governance processes to protect our customers’ personal data and intellectual property. We implement responsible testing and monitoring practices throughout the AI lifecycle to mitigate or avoid instances where our AI might perpetuate biases, amplify social challenges, or lead to new avenues of risk.

Responsible

We adhere to high standards in acquiring and managing data, and in training and delivering fair and safe AI models.

Transparent

We are forthcoming about the design, development, and intended use of AI systems and data.

Accountable

We respect our customers’ choices and align to laws and regulations. 

Reliable

We are rigorous in building AI systems that strive to provide accuracy, validity, and consistency.

Safe and secure

We are committed to protecting data, intellectual property, and privacy, and producing safe outcomes.

Transparency in AI

As part of our ongoing commitment to delivering trusted AI, we have developed AI transparency cards to disclose information about the AI features used in our products. These cards provide details on feature functionality, data sources, and the privacy and security safeguards in place.

Autodesk AI transparency cards

Autodesk Fusion

AutoConstrain

 

The Fusion AutoConstrain feature analyzes sketches and suggests the constraints and dimensions to fully constrain sketches. 

 

Autodesk Fusion

Fastener Classification for Drawing Automation

 

The Fusion Fastener Classification for Drawing Automation feature detects, classifies, and omits fasteners from drawings to improve efficiency of drawing creation.

 

Autodesk Maya

Machine Learning Deformer

 

The Maya Machine Learning Deformer feature approximates complex character deformation with something fast and interactive.

 

Autodesk Revit

Generative Design in Revit

 

The Revit Generative Design in Revit feature works with one or more outputs in tension to evolve a Design study by providing a series of results that are optimal but have trade-offs.

 

Dynamo

ML Node Autocomplete

 

The Dynamo ML Mode Autocomplete feature takes a node input and recommends upstream or downstream nodes in a hierarchically ranked set of results.

 

Autodesk Research

Project Bernini Research

 

The Autodesk Research Project Bernini Research model generates functional 3D shapes from a variety of inputs including 2D images, text, voxels, and point clouds.

 

Skyline view of Munich with the Theatine Church of St. Cajetan (Theatinerkirche St. Kajetan) and Odeonplatz.

European Union Commission AI Pact

Autodesk has voluntarily committed to the EU AI Pact, which encourages and supports organizations to plan ahead for the implementation of AI Act measures across the EU.

Trusted AI program initiatives

Autodesk’s Trusted AI program is responsible for our Trusted AI principles and practices, as well as our collaboration with government and industry groups working toward responsible AI.

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Governance, risk, and compliance

Autodesk’s Trust Organization, led by the Chief Trust Officer, implements and continuously reviews guidelines and processes to evaluate and mitigate AI risks. We regularly assess industry practices, standards, and emerging trends to foster responsible AI development and use in alignment with global AI, intellectual property, data protection, and privacy laws and regulations.

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NIST US AI Safety Institute Consortium (AISIC)

Autodesk is collaborating with the National Institute of Standards and Technology (NIST) in the Artificial Intelligence Safety Institute Consortium to develop science-based and empirically backed guidelines and standards for AI measurement and policy, laying the foundation for AI safety across the world.

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Content Authenticity Initiative

Autodesk is an active member of the Content Authenticity Initiative (CAI), which works to create a secure system for digital content provenance and media transparency. The work we do within CAI is fully compliant with the technical specifications released in 2022 by the Coalition for Content Provenance and Authenticity (C2PA) or C2PA Content Credentials.

Customer feedback

Autodesk regularly solicits customer feedback to understand current sentiments on AI, including concerns, challenges, expectations, and requirements around the ethics and use of AI within Autodesk’s platform. See what they have to say.

Guide to AI transparency cards

Get details on all the information we share about our AI features.

What is an AI transparency card?

Our AI transparency cards provide details on functionality, data sources, and the privacy and security safeguards in place for the artificial intelligence features used in our products.

What information is in the card title?

The name of the Autodesk product and the name of the AI feature are presented at the top of the card, below the verbiage “AI Transparency Card.”

  • Autodesk product name (e.g., Autodesk Forma)
  • AI feature in the product (e.g., Embodied carbon analysis)

What does the description convey?

The card description summarizes the actions the AI feature is expected to perform when used within the product.

What does feature functionality describe?

Feature functionality describes the AI and/or machine learning (ML) technology capabilities that enhance the AI feature using one of the following three terms:

  • Automate: Autodesk AI reduces repetitive tasks by automating steps that have traditionally required manual work or significant overhead, minimizing error and freeing up more time for creative work and innovation.
  • Analyze: Autodesk AI provides actionable insights to end users when faced with overwhelming amounts of complex data, helping them understand what is most important in real time to create the smartest solutions.
  • Augment: Autodesk AI augments creative exploration and problem-solving by improving speed, quality, and breadth of thinking through contextual understanding.  

What is a model source?

The model source describes the source type from which the model was developed to power the AI feature:

  • Proprietary: The AI/ML model was developed internally by Autodesk.
  • Open source: Autodesk uses AI/ML model that was developed by a third party, who made it available to the public.
  • Licensed: Autodesk has a license to use the AI/ML model that was developed by a third party.
  • Combination: Part of the AI/ML model was developed internally by Autodesk, and the other part(s) were developed by a third party (open source and/or licensed).

What does the primary technique mean?

The models behind each AI feature use methods, approaches, and techniques to learn from data, find patterns, perform tasks, and produce outcomes. We use techniques that will improve the quality and value of our products for customers. Techniques are constantly evolving, and in some cases multiple techniques are used, some of which may not be listed here. This field describes the primary technique used to develop the AI feature:

  • Transformer: A machine learning technique designed to process and understand data to perform sequential tasks more efficiently, such as language translation.
  • Encoding: A process of converting data into a specific format that can be efficiently processed by machine learning models.
  • Classification: A supervised learning technique that assigns items into predefined categories and predicts the category of new observations based on historical data.
  • Feed forward neural network (NN): A deep learning technique where information flows in one direction, from input to output, without any cycles or loops.
  • Predictor: AI technique that learns from data to make informed predictions about future events or outcomes based on historical data and patterns, such as forecast results, make decisions, and provide insights.
  • Genetic Algorithm: a method for solving both constrained and unconstrained optimization problems that is based on natural selection concepts.
    • Constrained optimization problems use logical limits or conditions that a solution to a problem must consider. They reflect real-world limits on things like production capacity, inventory, available space, and so on.
    • Unconstrained optimization problems deal with situations where there are no predefined limits or conditions for a solution to consider.
  • Transformer diffusion: A transformer technique (see Transformer above) that creates data by reversing a diffusion process by gradually adding noise to the data.

What is a user-directed feature?

Indicated with a "yes" or "no" designation, this describes whether the generated output can be reviewed and/or further updated by the user before any final action is being taken. This is otherwise known as "human in the loop."

What does the personal data information show?

This section indicates whether personal data is present in the training, testing, or validation datasets used for the development of this feature. 

What are the data sources?

The data source designation listed in the card identifies the types of data sources used for the development of this feature. This includes the data that was used to train the model that powers the AI feature. The types of sources are categorized as follows:

  • Open source:  Data that is freely available for use, modification, and distribution under an open license.
  • Customer content: Data that the customer or their authorized users submit or upload to the product, as further defined in Autodesk’s Terms of Use as “Your Content.”
  • Synthetic data: Data generated by a system or model that can mimic and resemble the structure and statistical properties of real data.
  • Commercial: Data that is purchased and/or acquired from a third party under a restrictive license.
  • Mix: More than one data source category was used.
  • Customer trained: The customer performed training and used their own proprietary data.

What is the choice format?

Choice formats are indicated as Opt-in/Opt-out, No, or N/A. These labels identify the form of choice available to customers and/or their users when their data is used for the AI feature's development/improvement.

  • Opt-in/Opt-out: The customer can choose whether to opt in or out of the use of their data for feature development/improvement.
  • No: A choice is not offered.
  • N/A: A choice is not applicable, as no customer content is used for feature development/improvement.

What is the encryption information shown?

We provide information about two types of encryption: encryption at rest, and encryption in transit. Both of these are shown with Yes or No designations.

  • Encryption at rest: Indicates whether the data is encrypted in the database(s) where the data is maintained. All encryption at rest uses the Advanced Encryption Standard (AES) 256-bit key length, otherwise known as AES-256.
  • Encryption in transit: Indicates whether the data is encrypted as it's transmitted from one point to another. Autodesk enforces encryption in transit via HTTPS standard encryption, RSA with AES-256, using TLS 1.2 at minimum.

What are the other safeguards referenced?

This section of the card indicates, as applicable, what other notable mechanisms are employed to preserve the confidentiality and protection of the data in addition to our standard security mechanisms. These safeguards apply to both personal data and company data.

  • Tokenization: Sequences of information in data are broken down into smaller units called “tokens.”
  • De-identification: Identifiers are removed from data and replaced with placeholder values.
  • Anonymization: The dataset does not contain any identifiable information and there is no way to link the information back to identifiable information.
See more card information

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