Trustworthy AI has been saved
For many organisations ‘becoming digital’ has involved large-scale automation of repetitive processes, and Artificial Intelligence (AI) has played a role to increase the breadth of coverage. However, it brings new risks and governance challenges that continue to act as a barrier to scaling, and prompted many to question the legitimacy of the role of technology in organisations and society at large. How can we find the balance between innovation, control and sustainability?
In order to navigate these risks you must:
- Understand your current ‘AI risk’ exposure,
- Ensure AI outcomes are validated for both efficacy and ethics,
- Put in place governance that supports the maintenance of these outcomes,
- Ensure you are ready for crises when they occur.
Explore our AI Risk Management Framework
How can we help you?
We pride ourselves on our hands-on experience at all levels of our data science capability, but we are not content to just follow industry trends. Instead we have collaborated with academic and public policy forums, pushing the AI risk conversation forwards. While our suite of accelerators expedite our AI risk engagement’s execution and prove out internal research endeavors, ensuring our solutions remain both practical and current.
Effective AI Operating Models
Strong governance practices around AI enable organisation to innovate with confidence whilst reducing the risk of complex technology. This is a crucial step for businesses looking to build, deploy and maintain trustworthy AI, consistently and at scale.
The absence of a universally agreed definition of AI in the enterprise makes targeted application of risk management activities a significant challenge. Services include:
- Buidling a consistent definition of AI to be applied across the organization.
- Capturing AI model usage throughout the enterprise.
Risk and Control Governance Review
Engagement with risk and control functions should happen early in the AI lifecycle to help ensure potential issues are identified and addressed up-front and a ‘control by design’ approach taken. Services include:
- Identification of the potential impact on existing risks and controls.
- Development of a framework to cover factors such as accountability, reliability, fairness and ethics.
Operating Model Design & Implementation
Robust operating models are essential for the governance of AI models with the impact of AI pervasive and requiring input from multiple stakeholders (Technology, Operation, Model, etc.). Governance requirements should be commensurate with the level of risk for each use case, to encourage innovation and direct focus to higher risk areas.
Automation is required to scale AI services, ensuring appropriate processes and controls are in place. Services include:
- Design and implementation of ongoing model management tooling as part of model validation and control execution to give comfort around models that are in the live environment.
AI Model Validation
Model risk is traditionally considered in financial use cases where a firm's model may produce an incorrect estimate, resulting in an inadequate performance or losses.
The breadth of AI systems application extended significantly beyond the standard scope, surfacing a variety of new risks to be managed.
As a response to this risk, the model validation seeks to ensure that a model behaves predictably, as expected, and solves the business problem posed to it.
Independent Model Validation
Independent model validation leveraging established framework and experience in model design, deployment and validation. Scorecard model expresses validation results to business audience, with more detailed findings documented and communicated in an iterative fashion. Services include:
- Shallow model validation, where primary burden of evidence is on the model owners.
- In depth model validation, where Deloitte owned analysis of code data etc. provides independent evidence generation.
- T-shaped model validation, where a mixture of in depth and shallow review is performed, based on risk appetite and areas of concern.
Model Validation Training
Training of existing AI/validation teams in our model validation approach. Services include:
- ‘Chauffeured’ model validation to support hands-on real-world learning of AI model validation techniques.
- Training workshops for senior leaders and AI practitioners with initial assessment of the current state of maturity of internal AI models and validation process.
- Privacy training to augment existing training packages with training solutions, ‘train the trainer’ packages.
Model Validation Governance
Setup support for a Model Validation Centre of Excellence, providing organisational structure, strategy, governance and management of AI environments. Services include:
- Top down design with Oversight, Framework, Operating Model and Tools, Data and Technology.
- Operating model components include Stakeholder Buy-In, Effective Prioritisation, Benefits Realisation, Controlled Development, Risk and Compliance, Delivery at Scale.
Ethical & Responsible AI
As data-driven techniques are increasingly applied across industries, AI systems have an unprecedented scale of impact on our lives with often unforeseen or unintended societal and individual implications.
The objective is to ensure that the use of AI does not lead to biased or unfair outcomes, is well-governed, and works as intended, in the interest of consumers and markets.
AI Ethics Framework
Robust model ethics require a well understood and measurable definition of a firm’s ethical position. Services include:
- Confirmation of the prioritised ethical principles and values of the organisation (e.g. equal opportunity in employment).
- Benchmarking with Deloitte’s ethics framework (based on existing regulatory environment, cultural environment etc.)
Operating models must reflect a firm’s ethical policy if the framework is to be effective. Services include:
- Organisation structure setup (e.g., AI ethics CoE), strategy, governance and management of AI environments.
- Redesign of top-down structure with ethical focus of Oversight, Framework, Operating Model and Tools, Data and Technology.
Independent Ethical Validation
Existing models should be validated against applicable regulations and a firm’s ethical principles. Services include:
- Identification of any issues or shortcomings in the AI products with respect to the ethical principles.
- Reporting any ethical trade-offs (e.g. bank branch safety vs. privacy), assess acceptability from key stakeholders, including customers, board, leadership, and employees.
AI Ethics Awareness and Training
Establishing an ethical culture requires understanding of the key risks and issues presented by AI. Services include:
- Roundtable workshops to discuss emerging topics and issues with Deloitte SMEs.
- Bespoke training workshops for senior leaders, analysing ethical trade-offs and framework considerations.
- AI practitioner engagement, focusing on quantitative/technical ramifications of ethical considerations.
AI Crisis Management
Instantiate processes into the development and live environment to manage the impact of potential erroneous outcomes, including reputation and crisis management.
Crises are an inevitable part of business operation, and organisation’s must be ready to respond to the diverse challenges that an AI crisis presents. Services include:
- Execution of fire drill scenarios within clients, simulating a crisis event within AI, for example the emergence of an unethical or poorly performing model.
- Engagement models include rapid model validation tests on simulated data, communications and decisioning.
Reputation monitoring is both supported by AI innovations, and more important because of AI innovation. Ensuring organisations are continually analysing their media footprint is critical to maintaining brand value. Services include:
- Monitoring newsfeeds, twitter, and other media for changes in a firm’s brand sentiment, searching for critical events, for example the emergence of unethical behaviour in a chatbot model.
Should crises occur, expert resources are not always available to analyse root causes and closely related issues at the speed that the market demands. Services include:
- Operating Model Redesign.
- AI Model Validation.
- Ethical and Responsible Validation.
AI Regulation: EMEA Centre for Regulatory Strategy
The speed at which AI solutions and their associated business processes can change makes auditability and traceability challenging,. This can result in errors that manifest on a scale and timespan that has previously been unprecedented.
Through the EMEA Centre for Regulatory Strategy (ECRS) Deloitte continues to make a leading investment in the area of regulatory change, which continues to pose a major challenge for the financial services industry.
By drawing together regulatory specialists with practitioners from Deloitte’s Risk Advisory, Strategy Consulting and other relevant areas to understand and advise on regulatory change, we focus on the strategic, business model and aggregate impacts of regulation.
We maintain strong relationships with regulators, central banks, standard setters, finance ministries and major industry trade bodies, allowing the ECRS to provide insights from the forefront of regulation.
While the focus of the EMEA group is on local regulation the ECRS works closely with its Deloitte counterparts in centres in US and Asia Pacific, tapping into the regulatory and remediation trends across the globe.