Trustworthy AI
Artificial intelligence (AI) has become one of the key technologies of this century and plays an increasingly essential role in answering the challenges we face. AI will impact our daily lives across all sectors of the economy. However, to achieve the promise of AI, we must be ready to trust in its results. We need AI models that satisfy a number of criteria and thus earn our trust.
What makes AI trustworthy?
Artificial Intelligence (AI) has long fascinated both computer scientists and the public since the term was coined in the 1950s. Since then, the sensationalist scaremongering about runaway AIs gradually gave way to a grounded, realistic view: AI is a sophisticated technology – or set of technologies – with the potential to deliver significant economic, scientific and societal advantages. It is an immensely powerful tool with wide-ranging potential. Over the next 10 years, experts expect an incremental economic impact of AI worldwide between $12 and $16 billion.
It's time for Trustworthy AI
Implemented properly, AI enables us to become leaner & faster, smarter, more personalized. With AI, we can examine and learn from data at a speed and scale that took our predecessors generations. Proper implementation is not automatic – it requires skills, experience and discipline. Open source toolkits have effectively “democratized” software development and led to a rapid proliferation in AI-based tools – from experts and debutants alike. This dynamic introduces both opportunities and risks. For example, AI models can be easily re-trained on new data sets, keeping them relevant and up-to-date.
On the flipside: model quality varies widely, use-cases can be questionable... and the AI models themselves cannot be held accountable for erroneous outcomes. These realities present several governance issues, recognized by researchers, practitioners, business leaders, and by regulators. The regulation of AI as proposed by the European Commission (see inset text) recognizes these risks. It addresses the need for data quality, transparency, fairness, safety, robustness - and above all ethics in application of AI. Where the regulation focuses on “what”, our aim is to guide you on “how.”
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EU Act of Artificial Intelligence
Implementation of AI/ML to Corporate Governannce processes
AI/ML Model Risk Management
Digital / AI Controls / Algorithms (DAICA)
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Relevant for the entire life cycle
Strategize
Set the stage, business objectives, values… the measuring stick against which teams, products, services, processes, tools will be judged. Ensure proper governance & control infrastructure. Capture use case ideas that align with the strategic objectives and core values.
Build
Define targets, delineate scope, identify constraints, assess feasibility and risk. Join developers with stakeholders, determine basic architecture, functionalities, refine data requirements, respect explainability, fairness, privacy considerations.
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Integrate
With concepts proven, minimal viable products built, the solution is put into production. This brings with it new considerations – uptime reliability, load balancing & scalability, data interfaces and compatibility with other systems in the ecosystem, defenses against cyber attack. Everything must be high performance and sufficiently resilient for operational stresses & scenarios.
Assure
Verify & validate along performance criteria – accuracy, fairness, transparency, others. Test reliability & reproducibility, challenge design decisions throughout. Periodically re-train to uphold performance expectations and guard against model drift, test controls to limit impact of potential failure modes.
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