Enterprise AI Analysis
Opening the Scope of Openness in AI
Tamara Paris, AJung Moon, and Jin L.C. Guo
The concept of openness in AI has so far been heavily inspired by the definition and community practice of open source software. This positions openness in AI as having positive connotations; it introduces assumptions of certain advantages, such as collaborative innovation and transparency. However, the practices and benefits of open source software are not fully transferable to AI, which has its own challenges. Framing a notion of openness tailored to AI is crucial to addressing its growing societal implications, risks, and capabilities. We argue that considering the fundamental scope of openness in different disciplines will broaden discussions, introduce important perspectives, and reflect on what openness in AI should mean. Toward this goal, we qualitatively analyze 98 concepts of openness discovered from topic modeling, through which we develop a taxonomy of openness. Using this taxonomy as an instrument, we situate the current discussion on AI openness, identify gaps and highlight links with other disciplines. Our work contributes to the recent efforts in framing openness in AI by reflecting principles and practices of openness beyond open source software and calls for a more holistic view of openness in terms of actions, system properties, and ethical objectives.
Quantifying Openness Research Impact
Our analysis, drawn from a vast academic corpus, reveals key metrics that underscore the depth and breadth of openness studies in AI and beyond.
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Openness Concepts Identified
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Distinct Research Topics
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Academic Records Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Interactivity
Freedom
Inclusiveness
Our study utilized a robust methodology to identify and categorize openness concepts from a vast academic corpus. Beginning with a broad collection of article abstracts, we applied advanced topic modeling techniques to uncover diverse concepts. These were then meticulously analyzed to build a comprehensive taxonomy, serving as a critical instrument for understanding AI openness.
The Interactivity theme emphasizes the dynamic exchange between an open entity and external agents. This includes aspects like Access to internal elements (e.g., public data, source code), the ability for external entities to Inspect internal workings (facilitating transparency and auditability), mechanisms for Distribution of resources and knowledge (sharing, redistribution), the potential for Reuse of components or ideas (promoting innovation and interoperability), and the fostering of Collaboration through participatory practices.
The Freedom theme characterizes openness as the reduction of constraints, allowing for organic evolution and increased possibilities. Key aspects include the removal of No Obstacles (e.g., financial barriers, formal requirements), allowing for Organic evolution without external interferences (neutrality in use), a state of Non-isolation where boundaries are permeable (exchange with external environments), the concept of Broader Boundaries (spatial or conceptual expansion), an Undetermined nature allowing for adaptability and novel outcomes, and enabling Autonomy for actors to operate by their own rules.
The Inclusiveness theme highlights the importance of fair and responsive engagement for all individuals. This encompasses ensuring Fairness through equality, equity, and impartiality (non-discriminatory access), promoting Diversity by actively seeking varied perspectives and adaptability to different needs, and fostering Democratization through the redistribution of power, knowledge, and resources across the population, empowering end-users to be producers rather than just consumers.
Taxonomy Derivation Process
Collection of article abstracts
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Topic modeling (LDA)
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Openness concepts extraction
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Concepts analysis (reflexive thematic analysis)
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Taxonomy of Openness
| Criterion | Traditional OSS | Open Source AI |
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| Core Components |
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| Freedom of Use |
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| Transferability of Benefits |
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| Societal Risks |
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98
Distinct Openness Concepts Identified
Our topic modeling and qualitative analysis revealed 98 unique concepts of openness across various disciplines, forming the basis of our comprehensive taxonomy.
BigScience Workshop: A Model for Collaborative AI Openness
The BigScience Workshop, an open research collaboration initiative, exemplifies a proactive approach to AI openness. It successfully brought together a wide and diverse audience to freely collaborate in creating and maintaining AI systems, demonstrating effectiveness in building large datasets and models.
This initiative counters the traditional model where control over modifications remains with a closed entity, fostering true collective advancement, and showcased how open collaboration can lower barriers to participation, allowing for more diverse perspectives and accelerating progress in developing powerful AI tools, while also addressing challenges like data resource concentration.
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Your Roadmap to Open AI Integration
A phased approach ensures a smooth transition and maximum benefit from openness in your AI development lifecycle.
Phase 1: Openness Assessment & Strategy
Evaluate current AI systems and practices against the taxonomy of openness (Interactivity, Freedom, Inclusiveness). Define a tailored openness strategy, identifying key components for disclosure and collaboration.
Phase 2: Technical Implementation & Standards
Adopt technical standards for data, model weights, and code documentation. Implement secure access mechanisms and version control, ensuring inspectability and responsible distribution. This includes considering Responsible AI Licenses (RAIL).
Phase 3: Community Engagement & Governance
Foster a culture of internal and external collaboration. Establish governance frameworks that balance openness with risk mitigation, including processes for addressing misuse and promoting diversity in development.
Phase 4: Monitoring, Feedback & Iteration
Continuously monitor the impact of openness on innovation, security, and ethical outcomes. Gather feedback from stakeholders and iterate on policies and practices to ensure ongoing alignment with desired effects like fairness and democratization.
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