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Introduction and agenda >
AI governance ensures there is an organizational and legal framework for researching and developing machine learning (ML) technologies. It aims to address issues surrounding information rights and potential violations, and bridges the gap between responsibility and ethics.
With the growing impact of AI in fields like healthcare, transportation, education, and public safety, governance is becoming more important. Its goal is to ensure adoption of AI systems by humans in a way that is fair, safe, and equitable.
This is part of a series of articles about MLOps.
By developing a robust AI governance program, organizations using AI/ML can avoid reputational damage, wasted investment in inherently biased models, and poor or inaccurate results.
Here are some of the key principles of AI governance:
The methods, techniques, and results of AI solutions (such as classification) must be expressed in human language that can be understood. Humans need to understand what AI models take as input, and do with those inputs, to make decisions or produce results.
It is critical for humans to understand these behaviors, to enable validation of existing knowledge and adjust assumptions to mitigate bias. XAI requires white-box machine learning models that generate results in a way that domain experts can easily understand.
Fairness is the ability of a model to meet society’s demands for fairness, and the ethical requirements of those who have to contend with the consequences of AI decisions. Transparency is the ability of an AI model designer to describe, in plain language, the parameters used to extract information from training data and the processes applied to that data.
Accountability is the ability to hold AI systems and their creators accountable for what they produce.
Data privacy is a common concern with AI models that use consumer data. AI projects and automated technologies often require sensitive data to ensure data quality and accurate predictions, so protecting its privacy is crucial. Effective data privacy and management practices are integral to an organization’s security and business processes.
Large databases may include personally identifiable and otherwise sensitive data. Usually, training algorithms does not require knowing to whom each piece of information belongs. Private data may be unavoidable for some algorithms, but there are ways to use it securely.
Related content: Read our guide to MLOps architecture (coming soon)
At level 0, each AI development team uses its own tools and there is no centralized strategy for developing or deploying AI. This approach offers a lot of flexibility and is common for organizations just getting started with AI.
However, there are potential risks when models are deployed to production. Without a standardized framework, it is difficult to assess risk. It might be difficult to scale up activities by hiring more data scientists, because it will be difficult for new employees to build on previous ML research efforts.
At this level, organizations are able to evaluate models by defining a standard set of acceptable metrics and monitoring tools. This not only provides consistency across the AI team, but also allows metrics to be compared across different development lifecycles. Based on these models, they can define policies for model adoption, efficiency, and safety.
A common monitoring framework is established to track these metrics so that everyone in the organization can interpret them in the same way. This reduces the level of risk and increases transparency, making policy decisions easier, and resolving stability and resilience issues. Companies at this level of maturity typically have a central model validation team that supports policy definition and enforcement.
At this level, the organization can use metadata generated at Level 1 to make all assets and data quality insights, across the entire model lifecycle, available in an enterprise catalog. With a single data and AI catalog, businesses can track the entire lineage of data, models, lifecycle metrics, and code pipelines.
This lays the foundation for linking between different versions of a model for full auditability. It also provides leadership with a single view of AI/ML projects for comprehensive risk assessment. Organizations at this level can articulate AI-related risks and gain a holistic view of the success of their AI strategies.
This level introduces automation into the process, capturing and processing information from the AI lifecycle without human intervention. This information greatly eases the burden on data scientists and other role-players by eliminating the need to manually record actions, measurements, and decisions.
With this information, model validation teams can make decisions about AI models and leverage AI-powered recommendations. Businesses can significantly reduce their operational workload by documenting the lifecycle of their data and models. This eliminates the risk of incorrect metrics, metadata, or data versions throughout the lifecycle. This makes it possible for the organization to implement AI models quickly and consistently.
Finally, with automation implemented across all AI initiatives, it becomes possible to apply enterprise-wide policies to AI models. A governance framework ensures that these policies are applied consistently throughout the lifecycle of all models. This provides adequate transparency and builds trust among regulators, customers, and end-users.
An AI governance framework can provide many benefits. However, implementations might encounter critical challenges, including:
AI requires quality data to produce accurate, consistent, and reliable results. However, it can be difficult to achieve appropriate data collection, cleaning, and analysis. This challenge can prove especially difficult when validating input data.
This occurs when the data used to train AI models is not diverse enough, does not focus on the appropriate metrics, has inaccurate labels, or fails to appropriately cover important edge cases. All these can lead to discriminatory outcomes. AI bias typically results from datasets that do not provide sufficiently inclusive information.
Regulatory entities and industry standards require organizations to meet certain requirements to protect various data types. Highly-regulated industries, like finance and healthcare, must comply with PCI DSS and HIPAA requirements to avoid penalties, reputational damages, and fines. Many organizations also add their own unique policies to meet best practices unique to their industry and scenarios.
Legal requirements for transparency can pose a significant challenge for AI systems based on machine learning (ML). These systems are not inherently transparent, and many AI professionals claim they do not know how their systems operate. Experts explain this issue by saying AI systems operate like a black box, which means there is often no way to explain how an AI system generates results or why it creates a certain output.
AI governance requires monitoring to achieve the visibility needed to identify policy violations, diagnose issues, apply remediation, and record information for auditing purposes. However, the volume of AI systems deployed in production can rapidly scale beyond the abilities of human operators.
Organizations can overcome these challenges by implementing a privacy-by-design approach that enables AI and data privacy to coexist as two components of the AI lifecycle. Data anonymization is essential to this approach, helping preserve data privacy without losing its usefulness in AI systems.
Here are common techniques that can help solve AI governance challenges:
Model training occurs iteratively using different decentralized devices rather than centralized data aggregation. While this is not an anonymization technique, it can still help improve privacy by ensuring organizations can train AI models without allowing others to see or touch the data. It enables organizations to leverage more information to feed new AI applications without compromising the privacy of this data.
Differential privacy enables publicly sharing information about a certain dataset. It involves describing patterns of groups while withholding information about the individuals whose information is stored in the dataset. Organizations implement differential privacy when they need to meet strict privacy requirements and use sensitive data.
Common techniques include adding synthetic data as noise to the dataset while keeping the data’s useful predictive characteristics (signals). It can help infer the inputs to an AI model by analyzing its outputs and curb data leaks.
This data anonymization technique pools all data that can identify an individual into a set of data consisting of other individuals with similar attributes. K-anonymization is commonly referred to as ‘hiding in the crowd’, because it does not allow linking any record to an individual. For example, you can replace an individual’s income with an income bracket. In other cases, you might be able to drop specific attributes.
This technique involves masking sensitive attributes and personal identifiers with non-sensitive placeholder values. You can use various masking rules. For example, you can hide the first two digits of an individual’s social security number and show only the last digits. A more complex technique may include using random tokenization to replace the original value with an unrelated string.
Aporia is a full-stack, customizable machine learning observability platform that empowers data science and ML teams to trust their AI and act on Responsible AI principles. When a machine learning model starts interacting with the real world, making real predictions for real people and businesses, there are various triggers – like drift and model degradation – that can send your model spiraling out of control. Aporia is the best solution to ensure your ML models are optimized, working as intended, and showcasing value for the business.
Aporia fits naturally into your existing workflow and seamlessly integrates with your existing ML infrastructure. Aporia delivers key features and tools for data science teams, ML teams, and business stakeholders to visualize, centralize, and improve their models in production:
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