H2O.ai seeks to educate data science practitioners on how to train artificial intelligence models that follow ethical practices and avoid bias. A well-crafted AI governance framework improves efficiency within an organization through the guided creation and deployment of AI systems. Beyond the effective use of AI, AI governance becomes a necessary component to fulfill compliance, risk management, and ethical AI practices.
What is AI Governance?
AI governance is a company’s regulatory framework for artificial intelligence. An effective framework must ensure that the AI takes in data that is bias-free and produces results and models that promote human equality.
The potential risks associated with machine learning (ML) need to be quelled before they affect the quality of models and algorithms. If left unmonitored, AI may produce undesirable or harmful results.
Emerging AI Governance
In the early 2010s, AI popularity was rising because of the development of the convolutional neural network (CNN). CNN advancements led to breakthroughs in facial recognition. Facial recognition accuracy overcame a 5% failure rate and jumped to over an 80% success rate.
Industries adopted the technology to track eye movement for marketing purposes and match facial features to confirm identities. However, there were no regulations to keep AI in line. Privacy and security were unprotected and at risk.
Companies started employing their own ethics boards to build trust with users. This started an industry-wide conversation about setting policies and criteria to improve the culture of AI. As a result, AI governance became more crucial in the public eye and across the tech landscape.
Organizational AI Governance Structure
Because AI governance is vital to creating ethically viable AI, it must be precisely outlined for each organization. The stewardship of AI governance is most effective when given to a committee. The committee should contain individuals, including:
The Audit Committee and board members, who work together to assess and control data audits.
The General Council, who oversees legal matters.
The CEO/corporate leader, who mitigates responsibilities among teams and maintains project charters.
The CFO, who owns AI governance within financial management.
The CDO, who implements changes and reviews any new framework revisions.
Various leadership and/or IT, who can be brought on the committee for general AI governance upkeep.
Together, an AI governance committee defines proper AI lifecycles, communicates, and enforces the standards throughout the company.
IT governance (ITG) is a framework leveraging IT labor and resources. The framework organizes the physical assets available to promote and maintain products and materials and protect a company’s IT investments. ITG and AI governance work hand-in-hand. AI governance reviews and maintains a company’s AI investments. An ITG strategy must be established to have an effective AI governance framework.
Data governance (DG) is the filtration of data. Data needs to be assessed for usability, transparency, safety, and more to meet a company’s DG criteria. DG is accomplished through a committee that consists of a governance team, a steering committee, and data stewards.
Artificial intelligence relies on collectible, trustworthy data. Bias is a huge pain point within the AI industry. Data governance helps prevent bias from arising within AI. Using a DG framework with proper AI governance decreases risk and increases the success rate of moving and keeping AI use cases in production.
MLOps + AI Governance
AI governance is the framework for using ML models, while machine learning operations (MLOps) are the standard for data management. MLOps is also the observance and maintenance of end-to-end machine learning models. It documents and logs the progress made from the past to the current model iterations. MLOps tracks what AI needs to meet predetermined benchmarks.
Responsible AI + AI Governance
Responsible AI incorporates best practices for people and processes as well as technology. Beyond regulatory compliance, companies deploying machine learning in production have real business interests in understanding their models and trusting that models will perform as expected and that models are not discriminatory.
Model governance (MG) is a framework determined by a company’s board, financial consultants, business experts, and IT teams. An MG committee’s purpose is to track model results, apply appropriate changes, audit data quality, and organize access to company AI models. This framework controls model access, including permission levels, progression tracking, implementation policy, and model results. This adds an extra layer of security that will protect company investments.
AI Governance Outcomes
The goal of AI governance is to provide clear, practical, and trustworthy AI systems that advance the quality of human life. Some ways AI has already improved human life include:
Reducing risk through improved cyber threat detection
Reducing hospital readmission rates through predictive AI
Rapidly identifying customer behavior to take action on potentially fraudulent activity
Predicting flu encounters through infectious disease forecasting
How Does AI Governance Impact Me?
AI and machine learning can improve efficiency by automating repetitive tasks that consume valuable human resources. This can free up the workforce to focus on more technical and creative aspects of their field. Organizations must take preventative measures using AI governance to ensure that AI operates within government regulations and basic human rights concerns while simultaneously benefiting the company.
AI Governance and Government
AI governance legislation began in 2016 with the need to regulate fast-growing AI practices. Currently, the National Institute of Standards and Technology is working to send out the Artificial Intelligence Risk Management Framework notice in the US. This aims to provide a voluntary standardized AI governance framework for the year 2023. The US government also recognizes the value of AI technology in improving efficiency.
AI Governance and Startups
Artificial intelligence alleviates many obstacles startups encounter while also providing the data necessary for effective problem-solving. To more effectively integrate AI into company strategy, startups should establish an AI governance framework. This will enable them to plan two steps ahead of biased data and keep their AI system clean and efficient.
Why does AI Governance Matter?
AI governance is the boundary that protects companies from crossing into harmful practices. A company’s AI governance committee enacts policies that should work to support government regulation and organizational needs. Setting boundaries for artificial intelligence helps protect the company from legal risks and can deepen its users’ conviction and trust in AI-based products. In short, AI governance is an investment that develops company-wide safety nets. Artificial intelligence, if cultivated properly, has the potential to solidify the future of an organization.
Artificial intelligence has been surpassing technological expectations for years. Most industries are looking to AI as the future. As data sources and regulations transform, having a solid foundation of AI governance is becoming a necessity. Every company that hopes to integrate AI must be aware of its AI governance framework needs and decide what strategies are necessary to boost ethical AI practices.
If you would like a useful recap of this content, check out H2O.ai’s AI Governance Wiki page.