Responsible AI with the Databricks Data Intelligence Platform
The transformative potential of artificial intelligence (AI) is undeniable. From productivity efficiency, to cost savings, and improved decision-making across all industries, AI is revolutionizing value chains. The advent of Generative AI since late 2022, particularly with the launch of ChatGPT, has further ignited market interest and enthusiasm for this technology. According to McKinsey and Co., the economic potential of Generative AI, including use cases and worker productivity enabled by AI, could add between $17 trillion and $26 trillion to the global economy.
As a result, more and more organizations are now focusing on implementing AI as a core tenet of their business strategy to build a competitive advantage. Goldman Sachs Economic Research estimates that AI investment could approach $100 billion in the U.S. and $200 billion globally by 2025.
However, as organizations embrace AI, it is crucial to prioritize responsible AI practices that cover quality, security, and governance to establish trust in their AI goals. According to Gartner, AI trust, risk, and security management is the #1 top strategy trend in 2024 that will factor into business and technology decisions. By 2026, AI models from organizations that operationalize AI transparency, trust, and security will achieve a 50% increase in terms of adoption, business goals, and user acceptance and realization of business goals.
Moreover, as AI regulations are picking up globally, organizations should start looking at meeting compliance with these regulations as part of their responsible AI strategy. In our previous blog on AI regulations, we discussed the recent surge in AI policymaking in the U.S. and other countries, emphasizing the common regulatory themes emerging worldwide. In this blog we will deep dive into how the Databricks Data Intelligence Platform can help customers meet emerging obligations on responsible AI.
Core challenges in responsible AI: Trust, Security, and Governance
Lack of visibility into model quality: Insufficient visibility into the consequences of AI models has become a prevailing challenge. Companies grapple with a lack of trust in the reliability of AI models to consistently deliver outcomes that are safe and fair for their users. Without clear insights into how these models function and the potential impacts of their decisions, organizations struggle to build and maintain confidence in AI-driven solutions.
Inadequate security safeguards: Interactions with AI models expand an organization's attack surface by providing a new way for bad actors to interact with data. Generative AI is particularly problematic, as a lack of security safeguards can allow applications like chatbots to reveal (and in some cases to potentially modify) sensitive data and proprietary intellectual property. This vulnerability exposes organizations to significant risks, including data breaches and intellectual property theft, necessitating robust security measures to protect against malicious activities.
Siloed governance: Organizations frequently deploy separate data and AI platforms, creating governance silos that result in limited visibility and explainability of AI models. This disjointed approach leads to inadequate cataloging, monitoring, and auditing of AI models, impeding the ability to guarantee their appropriate use. Furthermore, a lack of data lineage complicates understanding of which data is being utilized for AI models and obstructs effective oversight. Unified governance frameworks are essential to ensure that AI models are transparent, traceable, and accountable, facilitating better management and compliance.
Building AI responsibly with the Databricks Data Intelligence Platform
Responsible AI practices are essential to ensure that AI systems are high-quality, safe, and well-governed. Quality considerations should be at the forefront of AI development, ensuring that AI systems avoid bias, and are validated for applicability and appropriateness in their intended use cases. Security measures should be implemented to protect AI systems from cyber threats and data breaches. Governance frameworks should be established to promote accountability, transparency, and compliance with relevant laws and regulations.
Databricks believes that the advancement of AI relies on building trust in intelligent applications by following responsible practices in the development and use of AI. This requires that every organization has ownership and control over their data and AI models with comprehensive monitoring, privacy controls and governance throughout the AI development and deployment. To achieve this mission, the Databricks Data Intelligence Platform allows you to unify data, model training, management, monitoring, and governance of the entire AI lifecycle. This unified approach empowers organizations to meet responsible AI objectives that deliver model quality, provide more secure applications, and help maintain compliance with regulatory standards.
“Databricks empowers us to develop cutting-edge generative AI solutions efficiently - without sacrificing data security or governance.”