Introduction
The World Economic Forum (WEF) convened for its 54th annual meeting at Davos-Klosters from 15th to 19th January 2024. The agenda included several critical items such as medical preparedness, global sustainable economic growth, and of course, artificial intelligence (AI).
The meeting, attended by entrepreneurs, journalists, domain experts, and hundreds of representatives of national governments and businesses, yielded a plethora of insights. In the midst of all this, the AI Governance Alliance (AIGA) presented the Presidio AI Framework.
Developed based on the Presidio Recommendations from the Responsible AI Leadership Summit in April 2023, AIGA developed a series of Briefing Papers that present the ideas, insights, and recommendations from various stakeholders involved in responsible AI development.
The Presidio AI Framework is the culmination of the Safe Systems and Technology Track working in tandem with IBM Consulting. The Framework emphasizes the need for a standard methodology that standardizes Generative AI models’ lifecycle management while also highlighting the need for multistakeholder governance and transparent communication.
More importantly, the Presidio AI Framework is intended to be a comprehensive guide for businesses aiming to leverage AI capabilities within their existing operations. The roadmap this framework presents provides organizations with the necessary tools and philosophies needed to navigate the ethical, regulatory, and safety challenges posed by AI.
In other words, the framework proffers not just a strategy but a vision that can help organizations prepare for an AI-driven world grounded in common ethical principles and values.
Key Tenets of the Presidio AI Framework
Here are the three critical tenets of the Presidio AI Framework:
Expanded AI Lifecycle
The Expanded AI Lifecycle proposed by the Presidio Framework recommends a radical new approach to AI development that extends beyond the conventional stages of design, build, and deployment. Per this recommendation, the newly expanded lifecycle would cover a broader range of activities, including an initial concept evaluation, ethical impact assessments, stakeholder assessment, and engagement, as well as rigorous post-deployment monitoring and feedback.
For organizations involved in the development of AI models and systems, adopting the aforementioned lifecycle approach would require a combination of comprehensive planning and visionary foresight.
Concept & Design
The initial stages would require a thorough evaluation of the direct and indirect impact of the AI models and systems being developed, in addition to their immediate impact on the various stakeholders involved. This can help in appropriately assessing whether such developments align with the ethical guidelines, regulatory requirements, and operational aspirations of all the stakeholders.
Data Collection & Curation
Any and all data collected to train AI models and systems in the development phase should be screened via an extensive process to ensure their quality, diversity, and appropriateness in terms of representation. By doing so, an organization can avoid having to deal with issues such as bias and unfair outcomes that stem from inefficiencies in the data collection process.
Testing & Validation
Once the development process does begin, organizations will need to develop and implement mechanisms that enable a cycle of continuous risk assessment and appropriate mitigation. Furthermore, these mechanisms will need to be developed while ensuring robust data governance practices that continue to maintain the privacy and quality of data being used to train the AI models.
Model Development & Training
For organizations to truly adopt and, more importantly, to truly benefit from the expanded lifecycle approach, resolute investment in appropriate training, resources, and processes will be crucial. This involves the adoption of appropriate techniques that lend greater explainability to how particular AI models and systems generate specific outputs.
It is important to note that, in most cases, this will translate into higher operational costs while also elevating the overall complexity of the AI projects. However, such an approach will yield significant long-term benefits. Some of these benefits include enhanced customer trust, minimal risk of regulatory non-compliance and subsequent regulatory offenses, as well as better alignment of the organization’s AI development efforts with societal values.
Deployment & Monitoring
The post-deployment phase is just as critical as it requires a similarly rigorous mechanism in place that can proactively monitor the developed AI models and systems for any biases that may emerge. The Framework recommends an open channel of communication between the users and all major stakeholders involved at this juncture to ensure timely feedback that facilitates proactive interventions and immediate mitigation of the identified issues.
More importantly, organizations that successfully embrace this expanded lifecycle approach will further solidify and consolidate their claims as leaders in responsible AI development. As a result, such organizations can gain a competitive advantage in a market increasingly concerned about the various ethical implications of AI development.
The Presidio AI Framework encourages organizations to move towards a more ethical, transparent, and accountable AI development process that emphasizes proactive engagement with multiple stakeholders within the context of social and ethical considerations. Organizations that do adopt such a shift can not only expect to mitigate the associated risks and higher likelihood of regulatory compliance but also an opportunity to be a leader in the development and implementation of responsible AI.
Comprehensive Risk-Guardrails
With the comprehensive risk guardrails, the Presidio Framework aims to ensure that all AI models and systems are designed to be safe, ethical, and perfectly aligned with societal values by instilling these aspects at the development phase.
It does so by promoting the establishment of effective and robust mechanisms that appropriately identify, assess, and mitigate all relevant risks across major considerations such as privacy, security, and transparency. These mechanisms act as the guardrails that enable the adoption of a systemic approach to risk management as well as its effective integration across various stages of the aforementioned expanded AI lifecycle.
These comprehensive guardrails can include but are not limited to the following:
- Technical Safeguards: These will be deployed to ensure the appropriate security and integrity of all AI models and systems being developed. These can include but are not limited to the use of adversarial training, which enables models to thwart and resist manipulation attempts, as well as data augmentation, which diversifies the training data to deliver better model generalization outputs.
- Algorithmic Bias Detection & Mitigation: This will focus on organizations deploying measures to appropriately identify and mitigate any identified biases within the developed AI models and systems. Exact measures can include but are not limited to the use of various fairness metrics and bias detection tools and techniques, which can be leveraged depending on organizational needs.
- Human Oversight & Control Mechanisms: This would involve organizations leveraging some form of a Human-in-the-loop (HITL) methodology, which ensures that any developed AI models and systems do not operate with absolute autonomy and allows for human intervention, especially in instances that involve critical decision-making processes. Further steps can be taken to clearly identify the roles and responsibilities related to the monitoring, management, and intervention in AI operations.
- Transparency & Explainability: This is more of an organizational philosophy driving the internal culture of the organization rather than an exact set of measures and safeguards. The purpose is to ensure that any developed AI models and systems are sufficiently understandable to humans. Hence, various mechanisms and measures can be leveraged that allow for an appropriate explanation of how the AI models and systems in use within the organizations work. These mechanisms and measures are vital for organizations in terms of building trust, fostering a culture of accountability, and facilitating the interpretation of AI outputs accurately.
Naturally, the guardrails themselves will have to go above and beyond the traditional safeguards utilized by organizations. Done properly, this can empower organizations to effectively and accurately anticipate potential risks and issues, both technical and non-technical, at the development stage.
Furthermore, the adoption of comprehensive risk guardrails symbolizes a grander commitment toward responsible AI development. Investment in the appropriate tools, capabilities, human resources, and infrastructure that can facilitate the adoption of such guardrails will need to be followed to ensure their rigorous monitoring for efficient risk mitigation and management.
Additionally, transparent reporting mechanisms must be developed simultaneously to enable timely demonstration of an organization’s adherence to ethical expectations and regulatory requirements while also instilling a reliable accountability structure within the organization.
Shift-Left Methodology
The Shift-Left Methodology within the Presidio AI Framework represents a proactive approach to AI development, emphasizing early integration of ethical, safety, and compliance considerations.
Originating from software engineering, "shift left" refers to the practice of incorporating tests and quality assurance (QA) early in the development cycle to identify and fix issues promptly, thus saving time and resources. This methodology necessitates a fundamental change in how AI development is traditionally conducted, advocating for a proactive rather than reactive risk management strategy. It encourages early and close collaboration among developers, testers, and operations teams and requires the inclusion of a diverse group of stakeholders, including ethicists and compliance experts, in AI project planning.
The adoption of the Shift-Left Methodology necessitates significant planning and execution changes, aiming to develop AI solutions that are technologically advanced while being socially responsible. Importantly, it addresses the increasing need for regulatory compliance in the face of stricter AI regulations worldwide, enabling organizations to mitigate non-compliance risks, stay ahead of regulatory changes, and enhance public trust in their AI development processes.
Future Trends & the Framework’s Adaptability
The path toward the development and wide-scale adoption of responsible AI will be a long and arduous journey. It will require a comprehensive, collaborative effort across multiple sectors and disciplines. This need for greater collaboration will gain extensive importance as AI technologies and capabilities continue to evolve at the current breakneck pace.
In addition to the speed of this evolution is the increasingly evident and necessary integration of AI in both quotidian tasks as well as critical infrastructure. This serves to highlight further the urgent necessity of a robust, ethical, and reliable framework that can help organizations adapt emerging technologies with a clear consideration for their impact on society.
In the face of this, the Presidio AI Framework’s emphasis on early adoption and implementation of risk guardrails and a comprehensive AI lifecycle approach empowers organizations to appropriately anticipate and mitigate possible threats before they pose significant risks to the organizations. More importantly, the Framework establishes a clear precedent within organizations for a proactive approach to AI governance rather than a reactive one.
Furthermore, owing to the complexity of the AI models and systems being developed, organizations must look to increasingly nuanced approaches that enable a grander and more expansive platform for various stakeholders such as industry leaders, policymakers, and regulators. Such an approach underscores the relevance of the Presidio Framework as a shared responsibility model that takes diverse perspectives and expertise into account, making it more adaptable to the intricacies of emerging AI technologies as well as their social implications,
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