At Palantir, we believe that responsible AI is not an afterthought. Rather, it is fundamental to how we build technology. Our approach centers on developing software that enables responsible AI use throughout the entire system lifecycle, recognizing that ethical considerations extend beyond model development alone to encompass the full technology system, from data foundations and processing pipelines to user interfaces and human decision-making workflows. Rather than treating ethical considerations as separate compliance requirements, our platform integrates responsible AI capabilities throughout the development lifecycle.
This document outlines how Palantir's AI platform (AIP) actively supports eight core themes of responsible AI development. Each theme represents not just a principle, but a set of concrete capabilities and workflows designed to help users build trustworthy AI systems that are responsible, ethically sound, and effective in practice. This comprehensive approach ensures that users have the tools and workflows needed to address responsible AI requirements systematically, whether they are working with traditional machine learning models or generative AI systems.
AI systems should be inclusive and accessible and should not result in unfair discrimination against individuals or groups.
AIP offers features for identifying sources of bias in data, evaluating models for bias, and monitoring fairness concerns during model use.
Teams can identify fairness risks early in their data preparation process using Sensitive Data Scanner and data health monitoring to detect protected attributes and imbalances in their data foundation before model development begins. This addresses potential bias at the data level. However, bias can also emerge from the model itself even with high-quality data, so both subset evaluation through Modeling Objectives and AIP Evals provides systematic testing to detect unequal model performance across demographic groups. When bias is detected at either level, teams can implement targeted mitigation strategies such as re-sampling, collecting additional representative data, or adjusting algorithmic approaches.
AI systems should not be black boxes. Instead, to build trust in AI systems, users should understand how they work as much as possible.
AIP provides tools to help users understand how AI systems work, from debugging generative AI reasoning to evaluating traditional model performance.
Version control systems automatically capture model development decisions and rationale throughout the development process. For generative AI systems, the debug view in AIP Logic provides real-time visibility into how LLMs orchestrate tasks and delegate to explainable tools, while AIP Observability delivers comprehensive execution traces that help teams understand system behavior. Teams can design more transparent systems by using AIP Logic tools to delegate specific tasks to interpretable components rather than relying solely on LLM processing. Testing and evaluation approaches through AIP Evals and Modeling Objectives complement these capabilities by presenting model performance metrics in formats accessible to both technical teams and business stakeholders.
AI systems should be built with capabilities for assessing safety, security, and effectiveness throughout their entire lifecycle.
AIP enables secure, controlled deployment and continuous monitoring of AI systems to ensure safety and reliability throughout their lifecycle.
Access controls and data markings establish security boundaries from the outset, with georestrictions ensuring that model requests and responses remain within compliant jurisdictions. Encryption at rest and in transit protects information throughout the AI development lifecycle. Model deployments and pre-release functions can enforce staged testing procedures before production release, while capacity limits prevent service disruptions from unexpected LLM usage spikes. Real-time monitoring systems provide continuous oversight across security, performance, and operational metrics, with rollback capabilities enabling immediate response when issues are detected.
AI systems should provide capabilities to document relevant development processes, data sources, and the provenance of all data used for building models.
AIP automatically captures comprehensive documentation and audit trails across the AI development lifecycle, from data provenance to deployment decisions.
Data Lineage automatically captures provenance information as data flows through processing pipelines, providing complete visibility into data sources and transformations. Workflow Builder provides visibility into how AI powers application logic and decision-making workflows. Audit logs document all system interactions and decisions, while LLM cost governance tracks resource consumption and expenses, adding transparency to AI system operations. Documentation templates in Notepad and Modeling Objectives documentation enable teams to create standardized records that centralize project information, evaluation results, and decision rationale in formats suitable for regulatory review and internal audits.
Building AI systems should be an interdisciplinary process where scientists, engineers, domain experts, and other stakeholders work together.
AIP supports multi-stakeholder collaboration through flexible access controls, shared development environments, and evaluation frameworks accessible to users across different skill levels.
Role-based permissions and structured approval workflows create clear collaboration frameworks from project initiation. Code Workspaces and Code Repositories provide environments where technical and non-technical contributors can work together on AI development. Workshop tools enable real-time collaborative analysis across different disciplines, while external data sharing controls facilitate secure partnerships. Flexible evaluation frameworks ensure that domain experts, compliance officers, and technical teams each contribute their specialized expertise at appropriate points in the development process, rather than working in isolation.
There should be clear definition of roles and workflows for people responsible for different parts of an AI system.
AIP establishes clear chains of responsibility through granular permissions, comprehensive audit trails, and structured approval workflows.
Granular permission management establishes clear accountability structures from the outset, defining who can take which actions throughout the AI lifecycle. Full audit trails automatically document decision-makers and their rationale, while structured approval workflows through checks and checkpoints create systematic review processes. Checkpoints specifically enable stakeholders to acknowledge and justify AI-suggested decisions in operational workflows. This creates transparent chains of responsibility that can be audited and verified without requiring additional manual tracking efforts.
AI systems should benefit individuals, society, and the environment overall. They should enhance rather than replace human decision-making.
AIP ensures that AI enhances rather than replaces human decision-making through structured decision support frameworks and mandatory human oversight mechanisms.
Ontology-based decision support frameworks present AI insights within structured workflows that preserve human agency and decision-making authority. Human oversight workflows through actions and approval processes ensure critical decisions remain under human control. Dashboard and visualization capabilities translate complex AI outputs into formats that enable informed human judgment, while workflow automation with human checkpoints ensures appropriate oversight at critical decision stages. Opt-out and fallback mechanisms can be designed into applications to ensure users retain control over AI-assisted processes. Feedback loop integration captures human decisions to continuously improve AI recommendations, creating a collaborative intelligence approach that enhances rather than replaces human expertise.
Palantir's AI platform makes responsible AI systematic rather than ad hoc. The platform guides users of all skill levels and domains of expertise through established workflows that incorporate responsible AI principles:
Responsible AI is not a constraint on innovation. Instead, it is what makes AI systems trustworthy enough to use for critical decisions. Palantir embeds responsible AI principles into every aspect of the development lifecycle, enabling organizations to build AI systems that are not just technically sophisticated, but ethically sound and operationally reliable.
By taking an integrated approach that considers the full context of AI deployment, we help our users solve their most challenging problems while enabling them to maintain the highest standards of responsibility and governance.