Featured
Public standard profiling algorithms
Based on our case-based experience with auditing risk profiling systems, Algorithm Audit publishes a public standard providing qualitative and qualitative safeguards for responsible use of these type of algorithms. Read more...
Recent activities
Masterclass 'From data to decision', Jantina Tammes School of Digital Society, Technology and AI University of Groningen
Panel discussion Dutch Data Protection Authority
Guest lecture ethical AI Utrecht University
Featured
Public standard profiling algorithms
Based on our case-based experience with auditing risk profiling systems, Algorithm Audit publishes a public standard providing qualitative and qualitative safeguards for responsible use of these type of algorithms. Read more...
Recent activities
Masterclass 'From data to decision', Jantina Tammes School of Digital Society, Technology and AI University of Groningen
Panel discussion Dutch Data Protection Authority
Guest lecture ethical AI Utrecht University
Collaborating with
Building
AI auditing
capacity
from a
not-for-profit
perspective
Building
AI auditing
capacity
from a
not-for-profit
perspective
Distinctive in
Independence
By working nonprofit and under explicit terms and conditions, we ensure the independence and quality of our audits and normative advice
Normative advice
Mindful of societal impact our commissions provide normative advice on ethical issues that arise in algorithmic use cases
Public knowledge
Audits and corresponding advice (algoprudence) are made publicly available, increasing collective knowledge how to deploy and use algorithms in an responsible way
Independence
By working nonprofit and under explicit terms and conditions, we ensure the independence and quality of our audits and normative advice
Normative advice
Mindful of societal impact our commissions provide normative advice on ethical issues that arise in algorithmic use cases
Public knowledge
Audits and corresponding advice (algoprudence) are made publicly available, increasing collective knowledge how to deploy and use algorithms in an responsible way
AI expertise
Algorithms for decision support
Auditing data-analysis methods and algorithms used for decision support. Among others by checking organizational checks and balances, and assessing the quantitative dimension
AI Act standards
As Algorithm Audit is part of Dutch and Europen standardization organisations NEN and CEN-CENELEC, AI systems are audited according to the latest standards. See also our public knowledge base on standardization
Profiling
Auditing rule-based and ML-driven profiling, e.g., differentiation policies, selection criteria, Z-testing, model validation and organizational aspects
FP-FN balancing
Context-dependent review of ML and DL confusion matrix-based evaluation metrics, such as False Positives (FPs) and False Negatives (FNs)
Ranking
Recommender systems are everywhere. With the new Digital Services Act (DSA) that came into force last summer, auditing ranking systems is highly relevant
Generative AI
Auditing training process of foundation models, among others selection of training data, human feedback for reinforcement learning and risk management, according to AI Act standards
Algorithms for decision support
Auditing data-analysis methods and algorithms used for decision support. Among others by checking organizational checks and balances, and assessing the quantitative dimension
AI Act standards
As Algorithm Audit is part of Dutch and Europen standardization organisations NEN and CEN-CENELEC, AI systems are audited according to the latest standards. See also our public knowledge base on standardization
Profiling
Auditing rule-based and ML-driven profiling, e.g., differentiation policies, selection criteria, Z-testing, model validation and organizational aspects
FP-FN balancing
Context-dependent review of ML and DL confusion matrix-based evaluation metrics, such as False Positives (FPs) and False Negatives (FNs)
Ranking
Recommender systems are everywhere. With the new Digital Services Act (DSA) that came into force last summer, auditing ranking systems is highly relevant
Generative AI
Auditing training process of foundation models, among others selection of training data, human feedback for reinforcement learning and risk management, according to AI Act standards
Recent audits
Risk Profiling Social Welfare Re-examination
Normative advice commission provides rationales why these variables are eligible or not as a profiling selection criterion for a xgboost algorithm
Technical audit indirect discrimination
Assessment of risk distributions through Z-tests and bias test for various steps in algorithmic-driven decision-making process
Risk Profiling Social Welfare Re-examination
Normative advice commission provides rationales why these variables are eligible or not as a profiling selection criterion for a xgboost algorithm
Technical audit indirect discrimination
Assessment of risk distributions through Z-tests and bias test for various steps in algorithmic-driven decision-making process
Building algoprudence
Step 1
Identifying issue
Identifying a concrete ethical issue in a real algorithm or data-analysis tool
Step 2
Problem statement
Describe ethical issue, legal aspects and hear stakeholders and affected groups
Step 3
Advice commission
Deliberative conversation on ethical issue by diverse and inclusive advice commission
Step 4
Public advice
Advice of commission is published together with problem statement on our website. Publicly sharing the problem statement and normative advice is called algoprudence
Step 1 – Identifying issue
Identifying a concrete ethical issue in a real algorithm or data-analysis tool
Step 2 – Problem statement
Describe ethical issue, legal aspects and hear stakeholders and affected groups
Step 3 – Advice commission
Deliberative conversation on ethical issue by diverse and inclusive advice commission
Step 4 – Public advice
Advice of commission is published together with problem statement on our website. Publicly sharing the problem statement and normative advice is called algoprudence
Advantages of algoprudence
Jurisprudence for algorithms
