Palantir Technologies

Lena Ulbricht, Simon Egbert

Organizations increasingly rely on digital technologies to perform tasks. To do so, they have to integrate data banks to make the data usable. We argue that there is a growing, academically underexplored market consisting of data integration and analysis platforms. We explain that, especially in the public sector, the regulatory implications of data integration and analysis must be studied because they affect vulnerable citizens and because it is not just a matter of state agencies overseeing technology companies but also of the state overseeing itself. We propose a platform-theory-based conceptual approach that directs our attention towards the specific characteristics of platforms—such as datafication, modularity, and multilaterality and the associated regulatory challenges. Due to a scarcity of empirical analyses about how public sector platforms are regulated, we undertake an in-depth case study of a data integration and analysis platform operated by Palantir Technologies in the German federal state of Hesse. Our analysis of the regulatory activities and conflicts uncovers many obstacles to effective platform regulation. Drawing on recent initiatives to improve intermediary liability, we ultimately point to additional paths for regulating public sector platforms. Our findings also highlight the importance of political factors in platform regulation-as-a-practice. We conclude that platform regulation in the public sector is not only about technology-specific regulation but also about general mechanisms of democratic control, such as the separation of power, public transparency, and civil rights.

Andrew Iliadis, Amelia Acker

ABSTRACT Platforms designed for data integration and analysis introduce unique challenges for understanding the social implications of data management in public services. Researchers who study such platforms in the public interest must negotiate limited access to proprietary systems and the firms that make them but can create accountability through openly available data. This paper introduces an approach to researching a data integration and analysis firm, Palantir Technologies, through what we call ‘public interest archives for platform accountability.’ The paper begins by introducing data integration and analysis platforms as an emerging sector of the platform economy, followed by a summary of academic researchers’ challenges. We then provide a literature review of Palantir research, public interest archives, and contemporary archival projects for platform accountability. Then, we present our Palantir case study and describe our methods for document tracking to build a corpus (n = 2807), including court documents, privacy impact assessments, press releases, contracts, Freedom of Information Act requests, non-governmental organization reports, Securities and Exchange Commission filings, and patents. We end with several vignettes that help researchers understand this material’s significance to platform accountability.

Kristin D Alfaro-Munoz, Gemma Hallatt, Jamie Sookprasong, J. D. Groot, B. Lari, Emily Yu, Jeff Jin, A. Futreal, V. Ravi

e18077 Background: MD Anderson (MDA) strives to maximize learning from the > 44,000 patients seen annually. This has historically been challenging due to the diverse, often unstructured, and siloed nature of patient data. To begin addressing these challenges, MDA constructed an institutional data warehouse and developed Natural Language Processing (NLP) capabilities. This allowed technical users to extract specific cohorts but proved challenging for many non-technical users. In 2018, MDA conducted a proof of concept with Palantir Technologies to integrate disparate data sources into a single cloud-based platform enabling both technical and non-technical users to directly access and analyze data. Methods: Through a highly iterative, collaborative process between clinical/research users, IT experts and Palantir engineers, 150 deidentified datasets were integrated from the existing data warehouse, NLP, molecular reports, public data and manually curated data, representing data from > 1.5 million patients. Data integration was validated and feedback from clinical users gathered. Results: In addition to centralizing data, the platform was found to: (1) Make data accessible to non-technical users by mapping data to real-world concepts, (2) Create reports backed by live data, with clear visibility into inputs, analysis and history, (3) Create templates for common analytical workflows (e.g. KM curve), and (4) Support increased collaboration through transparent, secure access controls. This resulted in dramatic acceleration of cohort identification, data extraction, and outcomes analyses. Conclusions: Integrating siloed data exposed gaps and errors arising from the unstructured nature of medical data and use of NLP. In response, we are piloting a combination of (1) Purposeful structured data capture at the time of care, (2) Use of logic to suggest commonly uncaptured but critical patient events such as progression, and (3) Auditing functionality to allow users to manually fill in gaps and correct data in a transparent manner. Expanding the data foundation unlocks the potential for more advanced research, fueling scientific discovery, while enhancing the quality and safety of patient care.

Vasilis Galis, Björn Karlsson

ABSTRACT When Palantir Technologies customized the Gotham platform into POL-INTEL, a data integration and analysis platform purchased and used by the Danish police, it also enforced a new ontology that simultaneously shaped the police organization and policing practices. In this context, the concept of ontology should be understood in a twofold, albeit interconnected, way: it stands for its usual philosophical burden, but also refers to a centralized concept repository. In Computer Science, ontology refers to the basic concepts for how data is structured. Based on a series of interviews with Palantir engineers and police-officer users of POL-INTEL, this paper investigates what kind of politics, concepts, and bias in the form of data are inserted, processed and materialized in and by POL-INTEL’s ontology. We argue that POL-INTEL’s ontology is inherently political, as it is articulated by an assemblage of data, ideological positions, and economic concerns that are translated into the Danish context. POL-INTEL as a vector of platformization of police work also implies ideological choices with important consequences for the organizational life and accountability of law enforcement, establishing new distributions of skills between the platform and police officers.

Tamer Anacardi, Adalmiro Pereira, Tânia Teixeira

This paper investigates the international financial activities and risk management strategies employed by Palantir Technologies. The study aims to analyze how the company navigates financial complexities associated with global operations and employs instruments to mitigate risks such as currency fluctuations, geopolitical instability, and regulatory compliance. In an era of increasing globalization, companies like Palantir operate in highly interconnected and volatile financial markets. The ability to manage international financial activities effectively is paramount to sustaining operations and achieving competitive advantages. Equally important is risk management, which helps organizations anticipate and neutralize financial threats. Palantir’s case offers valuable insights into how a leading technology firm addresses these challenges in the global market.