Author Name:- Samiksha Kourav Date:- 29,September,2026
Abstract
Artificial intelligence (AI) is increasingly being prevalent in psychological assessment, with machine learning and large language models facilitating for personality assessment, mental health screening, Clinical decision support, analysis of responses, behavioral evaluation, and automated interpretation of psychological data. These advancement’s providing valuable benefits, opportunities, including greater efficiency, accessibility, and scalability. However, these increasing advancements by using AI also raises an important question: can an assessment be considered trustworthy, fair, valid, and transparent simply because an AI system produces accuracy ? An assessment may produce appropriate or predictive accuracy while still raising concerns about reliability, fairness, ethical accessibility, and furthermore.
This review examines research published between 2019 and 2026 on Advancements in psychological assessment, focusing on three interconnected areas: fairness and algorithmic bias, psychological validity and reliability, and ethical responsibility. Although existing research has increasingly addressed these concerns, these factors are often frequently examined separately rather than collectively, leaving gaps in existing research in understanding how these factors interact within and affect to psychological assessment. AI systems may demonstrate high overall performance while generating variation in outcomes across diverse backgrounds, cultural, linguistic, or socioeconomic groups. Likewise, systems that provide clear and interpretable decision making processes may still lack sufficient psychometric validity or reliability.
Accordingly, this review evaluates, Relying on data sets that may Risk of over reliance on automated recommendations, and ambiguity in human responsibility. It also considers their potential impacts, including inaccurate or unequal assessment outcomes, Potentially biased psychological judgments and perpetuation of social inequalities.
This review takes a broader approach to look beyond the technical capabilities of advancements to consider its wider implications for psychological assessment while also considering the challenges associated with particular attention to both measurement quality and responsible practice. It considers whether these systems can assess psychological characteristics appropriately, perform fairly across different groups, and produce results that can be meaningfully interpreted. It also highlights the importance of proper validation, representative datasets, transparency, privacy protection, and human involvement throughout the assessment process. Ultimately, the effectiveness of AI-based psychological assessment should be judged from a wider perspective than predictive performance. The assessment process must also demonstrate sound measurement, fairness across individuals and groups, clear interpretation of results, protection of personal information, and appropriate human oversight.
