The adoption of artificial intelligence (AI) systems has become incredibly widespread, leaving AI teams with the critical responsibility of creating responsible, defensible systems that mitigate bias, ensure accountability, and model transparency.
Why does this all matter, and what are some proactive steps AI teams can take now to ensure responsible AI system development and deployment? Let's dive in.
As AI continues to become a part of our everyday lives, the need for defensible and responsible AI also increases.
From a legal standpoint, responsible AI protects businesses against claims of bias or non-compliance, which can build trust with the public and improve overall brand awareness.
Meanwhile, the responsible use and integration of AI centers around values-driven decision-making, where key principles like fairness, transparency, and accountability form the foundation of all decisions made.
Defensible AI systems are also centered around privacy, giving users peace of mind in knowing that their data is secure and only used in ways that are legally compliant.
When creating AI systems with a privacy focus in mind, a "privacy by design" approach ensures that data is only collected and processed for specific and transparent purposes (data minimization). Likewise, privacy by design AI requires:
In addition to privacy by design best practices, defensible AI systems should optimize security with such technical safeguards as:
Bias is an inherent concern in the design of any AI system; as one publication by the National Institute of Standards and Technology describes, “Bias in AI can harm humans. AI can make decisions that affect whether a person is admitted into a school, authorized for a bank loan, or accepted as a rental applicant.”
The good news? With bias mitigation techniques in place, it is possible to build fair and transparent AI systems.
When evaluating AI systems for potential bias, some specific things to look for include:
While there may not be a way to 100% eliminate bias in the development of AI systems, there are some proactive measures that AI teams can take to mitigate it. Some specific best practices here include:
Aside from recognizing and mitigating bias as much as possible in AI, teams must also work to ensure a sense of transparency and explainability in their design of AI systems. In doing so, it is possible to build trust, hold systems accountable, and ensure regulatory compliance AI.
Transparency in AI occurs when information about a system's development and functions are made readily available and accessible to users and stakeholders alike. This can be achieved by carefully documenting data sources, models, and algorithms, and by implementing open-source development.
Explainability is a related concept that is especially relevant in discussions of AI because it focuses on the ability to describe why an AI model yielded a particular outcome. Being able to explain AI system behavior in an understandable, accessible way is key to building trust in decision-making while ensuring fairness.
Systematic evaluations and audits of AI systems are meant to ensure that these systems are operating ethically and safely — and there are some specific types of documentation that AI teams can focus on to pass these audits with flying colors.
Model cards or system cards are essential for audit documentation. Essentially, these serve as templates for AI systems that improve consistency and provide key information about its architecture, intended use, and potential limitations.
In addition to model cards, datasheets for datasets may also be used to provide more comprehensive and in-depth information about a system. Both model cards and data sheets must be updated at all times for the most accurate reporting.
In AI system documentation, both decision and access logs are also essential to keeping a detailed record of all decisions made during the development of a system. These logs and transparency reports provide exact details regarding who has accessed the system (and when), who has made key decisions about the system, the rationale behind these decisions, and related information.
Another critical aspect of responsible AI governance is having an operating model or set of principles for responsible development and deployment.
In an AI project, individual roles should be defined by the Responsible, Accountable, Consulted, Informed (RACI) matrix, which is meant to improve collaboration, communication, and decision-making within interdisciplinary teams.
Meanwhile, stage gates provide teams with a series of practical checkpoints, allowing for periodic review of a project's progress before moving forward.
In machine learning, the use of MLOps security protocols and ongoing monitoring provides additional protection against threats while maintaining data integrity.
Specifically, all stages of an AI system should have secure-by-default controls built in as a proactive measure. The idea here is that by implementing these measures from the beginning of a project, it is possible to increase system reliability and overall efficiency. Some important components of a secure-by-default pipeline include:
Throughout the development of AI systems, teams should also practice continuous monitoring of models as a means of identifying and resolving problems early on. Some specific measures teams can take to implement continuous monitoring include:
As with all AI systems, the use of third-party and generative AI platforms requires a sense of diligence as well — particularly as it relates to fairness, transparency, privacy, and accountability.
Before drawing upon a third-party AI or generative AI system, team members should carefully and deliberately evaluate its security, data handling, ethics, and compliance practices as a means of conducting due diligence. This means going beyond "typical" questions and using specific techniques (like prompt injection and model inversion) to test systems for issues in real-time.
Teams should also be prepared to implement and utilize controls to ensure ongoing oversight of third-party and generative AI platforms. This can be done through the use of transparent contracts that disclose data use policies and require vendors to implement certain security measures for added protection and peace of mind.
Inevitably, AI systems are imperfect and may be vulnerable to security threats or other issues over time. This is precisely why AI teams need to have plans in place to handle incidents as they occur and mitigate damage as much as possible.
An important aspect of preparing for potential incidents is conducting pre-production tests on AI systems, where teams test these systems in simulated environments and practice the measures that would be taken in the event of different emergency situations. These simulations and tests can be a great way to reveal weaknesses and improve incident response plans.
When incidents do occur, mitigating damage is critical. Through Red Teaming AI exercises and the use of an incident response playbook, teams can be proactive in their approach and restore systems to their original state as efficiently as possible.
In the creation of any AI system, teams must also take regular steps back to assess the overall impact of the system on its users and ensure that system output is in alignment with long-term goals and objectives.
Through value and harm balancing, for example, trams can ensure that the net benefits of an AI system outweigh any potential drawbacks.
At the end of the day, AI systems and the processes used to create them must be fair and transparent to build trust with the public. This means maintaining a sense of human oversight or human in the loop (HITL) in all decisions made while aligning system development with ethical frameworks and best practices.
To be audit-ready, it is recommended that you log all data lineage, model/version parameters, input/output hashes or samples, human overrides, and any decisions sent downstream each day.
One way to test whether a model is biased is to test subgroup performance and fairness metrics, such as demographic parity difference and equalized odds. From there, you should investigate any material gaps and document any mitigations or trade-offs made to offset biases.
It is possible to provide explanations while ensuring IP and PII protection. Consider, for example, using tiered explanations that include plain-language rationale to users. You might also rely on feature-level summaries for business owners and full SHAP audits for internal reviewers. No matter which route you take, the most important thing to remember is that you should never expose raw PII.
Generally, differential privacy should be used when training or reporting on sensitive data where re-identification risk exists. In these cases, it is best to start with privacy threat modeling before applying dynamic programming (DP) to aggregates or training and recording the epsilon budget in the model card.
Any high-risk AI launch should require a stage-gate approval process that includes the product owner, machine learning lead, privacy officer, security team, and legal/ethics reviewer. With this approach, no single function can green-light, and all decisions of dissent are recorded and documented in the registry.
This really depends on the model, but in general, risk-based intervals of six to 12 months are appropriate for high-risk models. For medium-risk models, an interval of 12 to 18 months may be appropriate. Likewise, additional recertification may be recommended any time material data/models change, or incidents are triggered.
If a vendor refuses to share information about a model, the best course of action is usually to demand documentation in the form of security/privacy posture, evaluation metrics, and failure modes. If this proves insufficient, consider sandboxing the model behind guardrails, limiting the scope of the project, or even changing vendors. Be sure to document any residual risk throughout the process.
The past several years have seen incredible advancements in AI technology — but with such significant innovation, it's important to take a step back and ask ourselves, "How can we contribute to privacy, transparency, and accountability?"
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