Ethical AI and Governance

Build trust through fair, accountable and transparent systems

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As AI is increasingly used to improve decision-making, it’s more important than ever to be aware of possible unintended consequences for you and your clients.

Modern AI governance and risk management approaches consider:

Privacy and confidentiality
Do you protect your customer interests when collecting and using data?

Accountability and responsibility
How do you achieve and maintain accountability?

Bias and fairness
Is there inherent bias in your data and how are you addressing this in your AI tool design?
Do you consider what ‘fair’ means to your various stakeholders?
Are you using AI appropriately for its purpose?

Transparency and interpretability
Do you know how and why a model produces its results?
How do those results affect decision-making?

Robust AI and modelling governance will ensure your ethical strategy is focused, relevant and integrated in AI tools that support:

Rigorous decision-making, facilitated by removal of inherent human bias
A customer-centric approach, consistent with consumer expectations and your values
Prudent management of regulatory and reputational risks.

How we help protect your interests and your clients’ interests

A robust AI and modelling governance structure is the foundation of reputation and trust in your organisation. It also ensures your values are embedded in the way you treat your customers. We’ll help you design, implement and monitor a structure that’s simple, safe and valuable.

What does safe and effective AI look like?

Strengthened by social licence

Use of ethical AI and machine learning must benefit all parties. A strong focus on design and application assists credibility with your customers and supports your operational decision-making.

Disciplined by strong governance

A solid AI governance structure considers all your processes, from data source to how you present model output to end users, shining a light on each step and facilitating timely reviews.

Unbiased and fair

Careful consideration of potential bias and measures of fairness will ensure your AI tools reduce bias in data. Achieve enduring success with a culture that willingly identifies and remediates related issues.

Interpretable, to facilitate human oversight

Model interpretability, or transparency – how and why an AI tool produces its output – will support human oversight to avoid erroneous and/or unfair AI decision-making.

Let us assist you to produce meaningful world-class AI tools through …

AI ethics

We can help you:

Work through your unique ethical considerations
Develop algorithmic measures to address bias and fairness
Prioritise your AI tools based on reputational risk
Build interpretable AI tools
Remediate ethical issues present in your existing AI tools.

Regulatory compliance

Regulators are increasingly focused on fair and unbiased AI application – unfamiliar territory for many organisations. We can help you:

Build compliance analytics and reporting
Review your existing end-to-end modelling process
Review your existing models relative to their purpose and objectives
Remediate issues present in your existing models
Redesign and replace complex, ‘black box’ models with interpretable ‘glass box’ models.

AI governance and risk management

Failings can almost always be traced back to governance deficiencies. Progressive organisations think of governance as an enabler. We can help you:

Develop a structure that supports safe and effective AI use
Design guidelines for developing new AI tools
Review your AI system infrastructure for silent failings and missed opportunities.