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 …
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.
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.
Our leaders in
Ethical AI and Governance
Our leaders in Ethical AI and Governance
Ethical AI and Governance
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