Knowing the Difference*: Artificial Intelligence Governance
*This article is based on a Fortune magazine article I read recently and highlights the need for Artificial Intelligence Governance.
Headlines have been blaming 'Aggressive and riskier' AI systems for data analytics failures. There are numerous data analytic systems that are making decisions users don't understand. Although failure is sometimes attributed to dirty input data, the AI surprise element is on top of many people's minds and doesn't fade quickly. The answer to this is Artificial Intelligence Governance, something that has been looked at in detail globally.
The world of data science and artificial intelligence (AI) has been evolving rapidly, enabling us to make data-driven decisions and uncover previously hidden insights. In the realm of AI, Move 37 refers to an unexpected and creative move made by the AI-powered Go player AlphaGo, which ultimately led to its victory against a world-champion human player. Achieving such breakthroughs in decision-making requires a combination of AI governance, data management, and a robust analytics framework. This article will explore how these elements can help us distinguish between outcomes that should be ignored, those that can guide us, and those that allow us to make our own Move 37s.
AI in Finance
In November 2019, various incidents in how credit limits were assigned to male versus female applicants raised questions of gender profiling and biases in credit scoring. This led to a high-profile Twitter engagement, with Bloomberg asking 'What's in the black box?', illustrating how it is difficult for users, and sometimes even those offering the service, to explain how decisions are being made.
A Surprising Move
Not all AI surprises have been negative though. In 2016, AlphaGo, a computer Go program developed by Google Deepmind, played world champion Go player Lee Sodol in a series of widely publicised matches.
In the 37th move of the second game, AlphaGo played a move that caught even the world's best Go players by complete surprise. It was so out of the ordinary, that many thought it was actually a mistake. AlphaGo went on to win the game, and Move 37 has defined the way that AI is now seen by businesses. It is these seemingly hidden opportunities that, when correctly predicted by AI, can change the course of a company. These are the ultimate moves we aim for in using AI and data science.
Helping to Solve the Problem with Artificial Intelligence Governance
The obvious question is then, how do we know to trust Move 37s, while avoiding those which can lead to reputational damage or worse. For me, I think that AI and model building governance has a role to play here.
AI governance, when done correctly, aims to enable data analytics models to be trusted throughout the organisation by ensuring models and the inputs used for models are built and used in accordance with a defined framework. These frameworks should be put in place to mitigate risk and identify potential errors while providing guidance on how to build and deploy models. One such error that should be carefully addressed is faulty input data.
Knowing the Difference
When data scientists combine proper AI governance, data management and governance and a defined analytics framework to build, deploy and manage models, we will be one step closer to being able to tell the difference between outcomes that should ignored, those that should give us guidance, and those that will enable us to make our own Move 37s.
AI Governance, Data Management, and Analytics Frameworks for Better Decision Making
AI governance is essential for ensuring that AI systems are designed and operated responsibly, ethically, and transparently. It involves establishing policies, processes, and controls to manage the development, deployment, and usage of AI models. By implementing AI governance, data scientists can ensure that their models are aligned with organizational goals, adhere to ethical standards, and mitigate potential risks.
Key components of AI governance include:
- Defining AI Strategy and objectives
- Implementing ethical guidelines and principles
- Ensuring data privacy and security
- Regularly monitoring and auditing AI models for performance, fairness, and transparency.
- Establishing an organizational structure for AI governance, such as a dedicated AI ethics committee
Analytics Framework
A well-defined analytics framework provides a systematic approach to building, deploying, and managing AI models. It guides data scientists in selecting the appropriate methods and techniques, validating model performance, and ensuring that AI solutions are scalable and maintainable.
Key elements of an analytics framework include:
- Defining the analytics problem and objectives
- Selecting appropriate data sources, methods, and tools
- Establishing model validation and performance metrics
- Implementing model deployment and monitoring processes
- Facilitating collaboration and knowledge sharing among data scientists and stakeholders
Conclusion
In conclusion, the integration of AI governance, data management and governance, and a robust analytics framework is critical for achieving optimal decision-making in the age of AI. By incorporating these elements, data scientists can create AI models that not only produce valuable insights but also adhere to ethical and regulatory standards. Ultimately, this will enable organizations to harness the full potential of AI and make their own Move 37s, leading to innovative and transformative outcomes.