ZortifyLabs

Zortify Labs drives innovation by seamlessly integrating human resources (HR) with the realms of data science and human-centered design, placing a strong emphasis on the human aspect within our dynamic solutions and services.

Our fundamental objective is to bridge the divide between theoretical advancements and practical implementations across the fields of psychology, computer science, and human-centered design.

Functioning as Zortify’s dedicated hub for research and development, Zortify Labs fosters cross-disciplinary collaboration and the ethical development of novel technologies, both internally and through partnerships with industry and academia. Our diverse team comprises researchers, data scientists, psychologists, designers, and experts from various scientific domains, many of whom have publication records in esteemed research journals.

While we find complexity exhilarating, at the core of Zortify Labs’ philosophy lies the commitment to making advanced technologies accessible to audiences without specialized expertise. Leveraging natural language processing (NLP) and data visualization techniques, we ensure that our innovations are not only user-friendly, ethically sound, and transparent but also visually intuitive. This dedication not only cultivates trust but also enhances decision-making capabilities within the increasingly intricate landscape of HR.

Our Whitepapers
11.09.2024 Forget NLI, Use a Dictionary- Zero-Shot Topic Classification for Low- Resource Languages with Application to Luxembourgish This paper introduces a method that uses dictionaries to adapt language models for identifying topics in documents from less commonly spoken languages. The approach was then successfully demonstrated on Luxembourgish using the Luxembourg Online Dictionary.
11.09.2024 Evaluating Parameter-Efficient Fine-tuning Approaches for Pre-trained Models on the Financial Domain This paper reveals, through experiments on tasks like sentiment and headline classification of financial news articles, that parameter-efficient approaches perform as well as traditional model training methods, with the added benefit of being computationally more efficient.
11.09.2024 DIE JUNGBULLEN KOMMEN Narzissten in Unternehmen – das sind doch Führungskräfte vom alten Schlag, oder? Falsch. Die größte Studie zum Thema weltweit zeigt, dass junge Menschen heute narzisstischer sind als frühere Generationen. Spätestens bei der Besetzung von Führungspositionen wird das zum Problem. Was können Unternehmen tun?
31.05.2023 Identifying the Correlation Between Language Distance and Cross-Lingual Transfer in a Multilingual Representation Space Prior research has investigated the impact of various linguistic features on cross-lingual transfer performance. In this study, we investigate the manner in which this effect can be mapped into the representation space. While past studies have focused on the impact on cross-lingual alignment in multilingual language models during fine-tuning, this study examines the absolute evolution of the respective language representation spaces produced by MLLMs. We place a specific emphasis on the role of linguistic characteristics and investigate their inter-correlation with the impact on representation spaces and cross-lingual transfer performance. Additionally, this paper provides preliminary evidence of how these findings can be leveraged.
11.09.2024 Towards a Common Understanding of Contributing Factors for Cross- Lingual Transfer in Multilingual Language Models- A Review This paper investigates the various factors that cause multilingual language models to perform better for some languages and worse for others. Some of these factors are related to the linguistic characteristics of the languages, while others stem from technical aspects.
11.09.2024 Soft Prompt Tuning for Cross-Lingual Transfer- When Less is More This paper investigates the effectiveness of Soft Prompt Tuning, a technique for automated prompt engineering, in the context of multilingual language models.
10.05.2023 Invest in people: Why AI can be an added value in the selection process of early-stage entrepreneurs for funding Investors face the challenge of finding the right investments, and while financial reports and growth markets are important, the personalities of the founding team and employees are also critical. Investors need to evaluate soft skills such as leadership, communication, optimism, resilience, self-efficacy, and agility as they play a critical role in a company’s success. In this context, AI can help investors identify the soft skills of founders and teams. This whitepaper presents the use case of Uncap, a fintech company that has used Zortify personality assessment to measure entrepreneurial capital and disruptive potential when selecting early-stage entre-preneurs for funding in Africa.
10.05.2023 Comparison of the Psychological Capital of Founders and Their Employed Top Management This study examines the difference of Psychological Capital (PsyCap) of founders in comparison to their employed top managers in young companies. We use the PCQ (Psychological Capital Questionnaire) developed and tested by Luthans and colleagues to do so. Results were concluded on the basis of 36 responses from founders from Germany and Chile from 27 different young companies and the same number of answers from their respective employed top managers. A t-test for independent samples shows a significantly higher level in three of the four states of PsyCap among founders: self-efficacy, resilience and optimism. Hope is the only state in which founders don’t exhibit a significantly higher level than their employed top managers. Overall, PsyCap of founders is higher than their employees’.
11.09.2024 Natural Language Processing als Stethoskop für das Erleben von Mitarbeitenden in der Industrie 5.0 In diesem Artikel erfährst du, wie Natural Language Processing verwendet werden kann, um offene Fragen, wie etwa in einer Mitarbeiterbefragung, automatisch auszuwerten.
11.09.2024 Mitarbeitendenbindung in der deutschen Industrie – eine Aus- wertung von 40.000 qualitativen Rückmeldungen mittels Natural LanguageProcessing (NLP) Dieser Beitrag bietet dir einen Einblick in die Ergebnisse der NLP-Analyse von 40.000 frei formulierten Textantworten aus Befragungen in der deutschen Industrie.
11.09.2024 Tausche Geld gegen Wertschätzung Wer möchte nicht einen großartigen Arbeitsplatz haben? Arbeit stiftet Identität, knüpft Netzwerke und macht einen wesentlichen Teil unserer Lebenszeit aus. Wie also kann es sein, dass der Arbeitsplatz zum neuen Kampfplatz der Kulturen ge- worden ist? Bedingt durch Generationenwechsel, andere Auffassungen von Lebens- und Arbeitswert und natürlich auch durch die Pandemie stehen viele Arbeitgeber fassungslos vor dem Rätsel eines wachsenden Mismatches: Die Angebote, die sie machen, und die Ansprüche der Arbeitnehmenden passen nicht mehr zusammen.
11.09.2024 Personalauswahl 4.0 Die Persönlichkeitsmerkmale eines Menschen bieten eine robuste und präzise Momentaufnahme der Art und Weise, wie eine Person ihr tägliches Leben führt, z. B., wie sie sich in alltäglichen sozialen Interaktionen, täglichen Gewohnheiten oder auch am Arbeitsplatz verhält. Jeder Mensch kann schematisch entlang von Dimensionen einzelner Persönlichkeitsmerkmaler eingeteilt werden.
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NLP

Natural Language Processing (NLP) is transforming the world of technology, enabling us to automate tasks, gain valuable insights from vast amounts of data, and communicate with machines in a natural, intuitive way. From chatbots to voice assistants, NLP has revolutionized how we interact with textual data, making it possible for machines to interpret human language and respond intelligently.

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NLP
Explainable AI

Explainable AI, or XAI, is an emerging field that seeks to create more transparent artificial intelligence systems or explain how non-transperant systems work. This is crucial in building trust in AI and ensuring that decisions made by these systems are ethical, fair, and responsible. At Zortify, the team is committed to the development of ethical and sustainable technology, with a focus on XAI. Their expertise in the field of explainability is leveraged to improve their products and push the field forward.

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Explainable AI
EU AI ACT

The EU AI ACT is a proposed legal framework for the development and use of artificial intelligence in the European Union. It aims to create a harmonized legal framework for AI development and use, using a risk-based approach. The regulation sets specific requirements and restrictions for each risk level, such as transparency and documentation requirements for high-risk systems. The EU AI ACT aims to protect fundamental rights and values while promoting innovation and economic growth. The regulation is currently in the legislative process and has a target to be approved and in force by 2024.

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EU AI ACT

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