Softtek Advocates Transparent AI for Justice, Public Sectors
Softtek has analyzed key factors for more transparent and understandable artificial intelligence in the justice and public sectors. The company highlights the need to move beyond “black box” algorithms, emphasizing the importance of explainable AI (XAI) and design that focuses on trust.
Addressing the “Black Box” Challenge
As AI adoption grows in these sectors, citizens and legal professionals demand greater transparency. Softtek’s White Paper stresses that XAI provides the technical foundation for auditing algorithmic behavior. However, it’s the trust-focused approach that translates this complexity into a clear, user-friendly interface, ensuring control, security, and legal integrity.
The combination of XAI and trust-focused design helps overcome the lack of clarity in traditional AI systems.
Regulatory Compliance and User Experience
With the AI Act entering into force, compliance is a significant concern. Softtek notes that integrating transparency, explainability, and disclosure principles from the outset substantially reduces friction with regulatory requirements and protects organizations from sanctions.
The trust-focused experience goes beyond technical auditing. It focuses on presenting algorithmic logic in a way that’s accessible to end-users and legal operators, enabling them to understand, intervene, and maintain control over AI-driven decisions.
Real-Time Governance and Sustainable AI Adoption
Softtek advocates for dynamic governance, replacing static policies with real-time mechanisms that adapt to each use case’s risk level. This includes feedback loops, uncertainty management, and data lineage tracking.
By embedding transparency and explainability into the design process, organizations can eliminate a major barrier to AI adoption in regulated public sector environments.
According to Doris Seedorf, Softtek’s CEO for Spain, “Today’s users demand immediacy but reject any result they can’t understand. While XAI provides the technical rigor for algorithm auditing, the trust-based experience is what transforms this complexity into a comprehensible interface, ensuring control and legal integrity.”
Seedorf warns that maintaining opaque “black box” processes not only increases operational and regulatory risks but also directly compromises the return on AI investments.
