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AI Bias Audit

Independent bias audit of your AI projects to achieve regulatory compliance and reduce unfair discrimination.

Why work with us:

At Holistic AI we have a huge depth of experience in auditing AI systems across the five key risk verticals, including: Bias, Efficacy, Robustness, Explainability, and Privacy.

We provide a comprehensive audit solution within weeks of data receipt.
Identifying bias concerns against protected characteristics within your models.
Suggest mitigation techniques that can be implemented to reduce bias.

What does an AI Bias Audit involve?

Algorithm auditing is an ongoing process that requires a holistic understanding of the AI system, including the context in which it is used, what it was designed to do, and the type of technology used. The process of an AI Audit can be divided into 4 distinct steps:

During the initial stage of an audit, the system is documented, and processes are assigned an inherent risk level ranging from high-risk to low-risk. The risk level given to a system depends on factors, including the context that it is used in and the type of AI that it utilises.

Triage

Assessment

During the assessment phase, the system undergoes a comprehensive AI audit to evaluate its current state across five key verticals: Bias, Efficacy, Robustness, Explainability, and Privacy.

For a bias audit, the focus is solely on assessing the system's bias risk.

The bias vertical examines how the system treats individuals impartially, regardless of subgroup membership. It investigates whether the system demonstrates differential performance across various groups based on factors like age, gender, and ethnicity.

Addressing bias ensures fair treatment, prevents discrimination, and promotes compliance with equal opportunity laws.

The outcomes of the assessment are used to inform the residual risk of the system. Based on this, actions to lower this risk are suggested. These can be technical, addressing the system itself, or non-technical, addressing issues such as system governance, accountability, and documentation.

For example, bias can be mitigated by debiasing the data the model is trained on, amending the model to make it fairer across groups, or amending the outputs of the model to make the predictions fairer, depending on the source of the bias.

Mitigation

Assurance

Assurance is the process of declaring that a system conforms to predetermined standards, practices, or regulations. Assurance can also be given on a conditional basis, with mitigation actions still outstanding for higher risk processes.

Speak to an Expert

We conduct Bias Audit of your AI systems

Our methodology includes regular assessments of your automated tool's algorithmic performance, ensuring transparency, efficiency, and compliance with relevant regulations.

Collaborate with all relevant parties

We start the process with a clear project briefing between Holistic AI and relevant parties such as the vendor of the tools and employers deploying the AI tools. We maintain an open communication channel throughout the process to ensure transparency and collection of in-depth information to inform a holistic understanding of the system.

Once we complete the Bias Audit, a comprehensive auditing report is generated. This report includes specific information about the audited system(s), the method and results of the adverse impact analysis, and tailored recommendations for mitigating identified issues or maintaining compliance going forward.

Prepare an independent third-party Bias Audit report

Provide continuous support and insights

Our team of professionals, consisting of machine learning engineers, data scientists, business psychologists, and legal and policy experts, is prepared to provide continuous training and expert insights. This proactive strategy in conducting Bias Audits ensures that your automated tools remain adaptable and compliant with evolving regulatory requirements and emerging best practices.

In addition to conducting an independent third-party Bias Audit, we are also capable of assisting you in communicating these findings to your clients and stakeholders. Our skilled team can translate the technical results of the audit into easily understandable and concise insights that resonate with non-technical audiences. This approach ensures that the implications of the audit are comprehensible to individuals with different interests and encourages transparency within your AI inventory.

Assistance with client and stakeholder correspondence

FAQs about the AI Bias Audits

1. What is AI bias?

2. Why is AI bias a concern?

3. What is AI bias audit and what are the common tools?

4. What are some metrics for bias detection?

5. What are the challenges in conducting an AI bias audit?

Relevant articles about Bias Audit

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