Being a Responsible AI Developer

Introduction

In today's rapidly evolving technological landscape, companies face a crucial choice: to genuinely embrace accountability or merely project an image of "responsible AI." At Bria, we believe that true accountability goes beyond rhetoric. It involves a deep commitment to understanding the implications of our actions on all stakeholders and engaging in open, meaningful dialogues. It means treating others as partners, sharing value, and ensuring that our innovations are safe.

For us, accountability is not about crafting superficial policies or marketing messages. It's about embodying our standards and owning our actions with integrity. This is the foundation upon which Bria has operated for years. We have now taken the time to document our practices and principles, which guide us on this remarkable journey. We have titled this document "Accountability Framework: Being an Responsible AI Developer." This title reflects our belief that accountability is not just a policy - it is a state of mind. At Bria, accountability is woven into our culture and embraced by every team member. It is a philosophy that permeates everything we do, even though we've never felt the need to display it on our walls.

Understanding the distinction between responsibility and accountability is key to our approach. Responsibility refers to the duties and tasks assigned to us, while accountability is about owning the outcomes of those duties and being answerable for the results. It is an intrinsic commitment driven by internal standards and a sense of integrity.

In this document, we share the core fundamentals that guide every decision we make at Bria. These principles are the backbone of our commitment to the highest standards of responsible AI practice and reflect our dedication to leading in the development of accountable AI technologies. We invite all stakeholders to join us on this journey towards accountable AI innovation.

Accountability Fundamentals and Core Principles

• 1.1  Accountability: We take full responsibility for our AI models and their impacts, providing clear terms of use and implementing robust accountability mechanisms.
• 1.2 Responsible Innovation: We are committed to advancing AI technology while adhering to principles that prioritize societal benefit and accountable development.
• 1.3 Intellectual Property Respect: We uphold the highest standards of copyright compliance and intellectual property protection in our AI development and deployment processes.
• 1.4 Privacy and Security: We implement robust security measures to protect user data and adhere strictly to privacy regulations.
• 1.5 Fairness and Non-Discrimination: We develop AI models that are fair and do not discriminate against individuals or groups based on protected characteristics.
• 1.6 Reliability and Safety: Our AI models are designed with reliability and safety as paramount concerns, incorporating measures to prevent harmful outputs or misuse.
• 1.7 Transparency and Attribution: We ensure transparency through our proprietary attribution technology, providing clear traceability from data sources to generated content.
• 1.8 Continuous Improvement: We regularly review and update our practices to ensure they align with evolving standards of responsible AI and technological advancements.

Data Procurement and Usage

• 2.1  Opt-In Data Policy: We use only data explicitly shared by partners on an opt-in basis. Bria does not engage in any internet scraping practices. No synthetic data is used for training our models.
• 2.2 Responsible Data Sourcing:  Training data is responsibly sourced, adhering to stringent copyrights and privacy standards. We implement a 100% ethical data procurement policy. All data used for training models is provided under a license explicitly permitting training of generative AI models. Data provided to Bria will never empower models that infringe on the rights of others.
• 2.3 Data Quality and Diversity: Bria does not acquire data that will not lead to model quality improvement and will only dilute the attribution to existing partners.
• 2.4 Dedicated Storage and Access: Training data for generally available models is stored in dedicated training accounts. Proprietary data provided by customers for training private models is segregated from general models’ training data
• 2.5 Dedicated Storage and Access: Training data for generally available models is stored in dedicated training accounts. Proprietary data provided by customers for training private models is segregated from general models’ training data accounts. Access to proprietary data provided by customers for training private models and models informed by customers’ proprietary data is restricted to such customers and authorized Bria support for maintenance functions only with rigorous permissions system. Access to models is subject to periodical penetration tests.
• 2.6 Asset Cataloging: An automated pipeline records each asset in a dedicated catalog, maintaining a clear link to its origin.

Intellectual Property and Copyright Compliance

• 3.1  Licensed Data Use: We use only commercially licensed data explicitly authorized for training of generative AI models. We do not accept any AI-generated data into our catalog.
• 3.2 Creator Empowerment: We respect intellectual property rights, employing measures to prevent IP infringement. Bria will never commingle responsibly procured data with unethically procured data. Contributors’ data will never be used to empower models infringing upon the rights of other creators.
• 3.3 Reshaping Policy and Regulatory Landscape: By successfully building high-quality models through sustainable data partnerships, Bria demonstrates that responsible innovation and respect for intellectual property are not only possible, but commercially viable. This evidence is crucial for policymakers, regulators and courts, showing there is no need to choose between fostering AI progress and protecting creators.

Privacy and Data Protection

• 4.1  Privacy Protection: We place strong emphasis on privacy protection for individuals. We use human likenesses only with explicit releases for commercial use. We strictly prohibit the use of public figures' data in our models.
• 4.2 Data Protection: We implement robust data protection measures to safeguard personal information and comply with relevant privacy and data protection regulations. Specifically:
  - We encrypt all data at rest and in transit.
  - We use security measures designed to protect against unauthorized access to customer data.
  - We enforce tailored permissions and user access controls around who can view and access data.
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Commitment to Diversity and Inclusion

• 5.1  Inclusive Data Sourcing: We actively seek and incorporate diverse datasets that represent a wide range of cultures, ethnicities, ages, genders, and geographical locations. This approach ensures our AI models are trained on a comprehensive representation of global diversity.
• 5.2 Bias Mitigation: Our development process includes rigorous testing and refinement to identify and mitigate potential biases in our AI models. We employ diverse teams of experts to continuously evaluate and improve our models' fairness and inclusivity.
• 5.3 Content Moderation: We implement content moderation filters to prevent the generation of harmful or biased content.
• 5.4 Stakeholder Engagement: We regularly consult with diverse stakeholder groups, including underrepresented communities, to understand their perspectives and incorporate their feedback into our AI development and deployment processes.
• 5.5 Diverse Workforce: We are committed to maintaining a diverse and inclusive workforce across all levels of our organization. This diversity in our team contributes to more holistic and unbiased AI development.
• 5.6 Cultural Sensitivity: Our AI models are designed with cultural sensitivity in mind, respecting and accurately representing various cultural nuances and contexts in their outputs.
• 5.7 Ongoing Education: We provide regular training and education to our team members on the importance of diversity and inclusion in AI, fostering a culture of awareness and continuous improvement.
• 5.8 Transparency in Diversity Efforts: We regularly publish reports on our diversity and inclusion initiatives, including the composition of our datasets and the results of our bias mitigation efforts, to maintain accountability and encourage industry-wide progress.

Safety, Misinformation, and Harmful Content

• 6.1  Content Marking: All AI-generated content is distinctly marked using cutting-edge technologies, ensuring it remains trackable and detectable even if modified, providing full transparency and traceability.
• 6.2 Content Guidelines: We enforce strict guidelines against generating harmful or offensive content. Users are strictly prohibited from using Bria's models for:
  - Intentional disinformation or deception.
  - Violation of privacy rights.
  - Unauthorized impersonation or non-consensual replica.
  - Harm or exploitation of minors.
  - Harassment or harm to individuals or groups.
  - Circumvention of safety features.
  - Generation of harmful or illegal content.
• 6.3 Misinformation Prevention: We implement measures to prevent the misuse of our platform. We do not accept any data that features public figures or harmful content, making it impractical to use our models to generate these concepts.

Revenue Sharing and Attribution

• 7.1  Fair Compensation: We compensate partners based on their measured contribution to generated content, providing a recurring revenue stream to data partners. Bria rejects one-time license deals for AI training data, instead implementing a fair and transparent system of ongoing compensation that ensures creators receive a share of the long-term value their work generates in existing and future AI applications.
• 7.2 Attribution Technology: Our patented attribution technology enables Bria to bridge the gap between demand for synthetic content and supply of authentic training data. Each synthetic output generated by our models is attributed to the original content creators that impacted the most on its generation.
• 7.3 Transparency: Bria’s attribution technology offers transparency to both data contributors and customers.
• 7.4 Protection of Data Contributors: Bria does not allow customers to use its models to pre-generate synthetic assets and distribute them to directly compete with our data contributors. Instead, customers are encouraged to build services and products that integrate Bria's models. In addition, using any Bria model to generate synthetic assets to inform the training of a model circumventing Bria’s attribution commitment to data contributors is forbidden.

Global Compliance and Ethical Monitoring

• 8.1  Regulatory Adherence: We meet global regulatory standards, including EU AI Act, and stay informed about evolving global AI policy requirements.
• 8.2 Ethical Usage Monitoring: We apply controls to monitor ethical usage of our AI,including content guidelines, moderation, and generative AI content labeling.

Accountability and Continuous Improvement

• 9.1  Ongoing Development: We are committed to continuous growth in responsible AI practices.
• 9.2 Audits and Assessments: We conduct regular audits of our AI systems to ensure continued adherence to our responsible AI standards.
• 9.3 Research and Development: Bria is at the forefront of GenAI research and development, ensuring your data is contributed to models that will continue to lead the market, both on the technical level and responsible AI level.
• 9.4 Stakeholder Engagement: We actively seek feedback from clients, industry partners, and regulatory bodies to enhance our responsible AI framework.

Societal Impact and Workforce Transformation

• 10.1  Acknowledging AI's Impact: Bria recognizes that the rapid advancement of AI technologies, including our own, significantly impacts job markets and societal structures. While AI creates new opportunities, we acknowledge its potential to displace certain job types.
• 10.2Commitment to Responsible Transition:We are committed to developing and deploying our AI technologies in a manner that supports a responsible transition in the workforce:
  
  • 10.2.1 Scalability Use Cases: We focus on helping customers implement AI for generating on-brand, hyper-personalized content at scale, addressing needs that traditional workflows cannot fulfill.
  • 10.2.2 Productivity Use Cases: For workplace productivity use cases, we excel in augmenting human creative curation capabilities with on-brand AI creation capabilities, allowing teams to get fast time to market, rather than replacing workers.
  • 10.2.3 Skill Development: We offer specialized training programs to help clients' employees acquire AI-relevant skills.
• 10.3 Democratizing Creativity: Bria's technology aims to democratize the creative process, fostering a dynamic ecosystem where both emerging talents and established companies can thrive, driving innovation and healthy competition while making advanced creative capabilities accessible to a broader range of participants.
• 10.4 Ongoing Dialogue: We commit to maintaining an open dialogue with labor organizations, policymakers, and other stakeholders to address concerns and collaboratively work towards solutions that maximize the benefits of AI while minimizing negative impacts on employment.

Environmental Consideration

• 11.1  Efficient Data Usage: We utilize commercial-grade data, dramatically reducing data preparation and curation needs. This approach significantly decreases computational requirements and energy consumption compared to traditional AI development methods.
• 11.2 Optimized Model Deployment: Our use of hygiene datasets minimizes the need for content moderation, guardrails, and filtering. This optimization leads to substantial computational savings during customer model deployment.
• 11.3 Computational Efficiency: We develop highly optimized models with fast versions for almost all model types and versions. These efficient models ensure minimal energy consumption and reduced carbon footprint across all stages of AI operations, while meeting quality requirements. This approach not only benefits the environment but also translates to cost-effective solutions for our partners and customers.
• 11.4 Responsible Data Management: We license, use, and store only the data necessary for our AI development. By avoiding large-scale web scraping and excessive data storage, we significantly reduce our environmental impact and energy usage.
• 11.5 Continuous Improvement: We are committed to ongoing research and development in eco-friendly AI technologies. Our team constantly seeks new ways to enhance the environmental sustainability of our AI operations, ensuring we remain at the forefront of green AI practices.

Business Continuity and Partnerships

• 12.1  Market-Driven Innovation: We always work backward from real-world use cases,developing AI solutions where the current fidelity of generative AI technology can meet actual market quality requirements. This approach ensures we engage only in products with genuine demand, generating tangible value rather than pursuing purely academic endeavors.
• 12.2 Innovative Flexible Licensing Approach: We offer customers the flexibility to use our technology through APIs, SDKs, iFrames and source-available models, providing customers with source-code and weights access while ensuring a stable revenue stream for continued development, unlike free open-source models with uncertain sustainability.
• 12.3 Sustainable Business Model: Our responsible data procurement model ensures continuous access to high-quality data for training cutting-edge models, focusing on real-world applications that meet market demands.
• 12.4 Operational Efficiency: Our highly efficient computational needs lead to exceptional operational efficiency. This streamlined approach significantly contributes to our long-term business continuity by optimizing resource utilization and reducing operational costs.
• 12.5 Financial Stability: Bria is backed by global venture capital firms, including Intel Capital, GFT, and Entree Capital. This robust financial foundation, combined with our efficient operations, ensures our business continuity and ability to invest in long-term research and development.
• 12.6 Strategic Partnerships: We have established strategic partnerships with industry leaders in cloud computing and AI, including AWS, Microsoft, and Nvidia. These collaborations enhance our technological capabilities and market reach.
• 12.7 Partner Empowerment: Many of our data partners leverage our models to offer their creator communities and customers advanced GenAI tools, on preferred commercial terms. This symbiotic relationship strengthens our ecosystem and provides mutual benefits.
• 12.8 Long-term Partnership Commitment: We view our data partners as integral to our success and are committed to nurturing long-term, mutually beneficial relationships that evolve with the changing AI landscape.

Conclusion

Bria AI accountability framework is a living document, embodying our ongoing commitment to the highest standards of responsible AI practice. It is not a static set of rules, but a dynamic reflection of our evolving understanding and dedication to accountable AI development. We actively seek and welcome engagement from all stakeholders - from creators and customers to policymakers and the wider public - in this collective journey towards truly responsible AI innovation.

By transparently sharing our framework, Bria AI aims to lead by example in the responsible AI development conversation. We hope to inspire other organizations to prioritize genuine accountability over superficial compliance. Our goal is to demonstrate that innovation and responsible practices are not mutually exclusive, but rather mutually reinforcing.

As we continue to push the boundaries of AI technology, we remain steadfast in our commitment to driving innovation while prioritizing accountability, safety, and societal benefit. We invite you to join us in shaping a future where AI not only advances technologically but also upholds the highest ethical standards and contributes positively to society.

Together, we can create an AI landscape that is not just innovative, but also trustworthy, inclusive, and truly beneficial for all.