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
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.
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’.
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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.

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.

Explainable AI

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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