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July 23.2025
3 Minutes Read

How to Build Trustworthy AI Agents With SAS Viya's Framework

Futuristic AI chip glowing with digital layers.

The Importance of Trust in AI Agents

As AI agents increasingly assume roles in sensitive sectors such as finance, healthcare, and public safety, the significance of trust in these systems cannot be overstated. Trust must be actively cultivated and upheld, and this challenge extends beyond mere design; it requires ongoing accountability and alignment with human values. A growing body of literature suggests that operationalizing trust—which encompasses fair and transparent practices—continues to be a key concern in AI development.

AI Agents: A Deeper Dive into Their Functionality

AI agents are more advanced than standard chatbots; they analyze data, make decisions, take action, and learn from their experiences. This transformative capability is reshaping numerous industries, offering organizations the potential for significant operational improvements. However, the path toward developing trustworthy AI agents must be navigated with a vigilance to ethical considerations and societal impact.

From Intent to Implementation: The SAS Viya Framework

SAS Viya positions itself at the forefront of responsible AI development, providing organizations with a robust set of tools to ensure that trust is embedded in every phase of an AI agent's lifecycle. This includes:

  • Foundation: Data Integrity and Governance - Trustworthy AI agents originate from high-quality data. SAS Viya offers comprehensive data governance practices such as lineage tracking, automated quality assessments, and proactive bias detection to foster transparency and reliability.
  • Decision Transparency - AI agents should be transparent about their decision-making processes. This transparency helps to eliminate biases and promotes fairness.
  • Human-AI Collaboration - Continual collaboration between human stakeholders and AI systems enhances trust. Human insights can guide AI development and refinement, ensuring alignment with ethical standards.
  • Continuous Trust Maintenance - Maintaining trust is an ongoing process, not a one-time effort. Organizations must regularly evaluate AI systems to adapt to new challenges and ensure ethical alignment.

Enhancing Organizational Impact with Trustworthy AI

Achieving operational trust in AI agents can have a profound impact on organizational performance. In sectors like healthcare, for instance, AI agents could prioritize emergency responses based on real-time data analysis. In finance, they might optimize credit limits dynamically by analyzing market fluctuations. Such applications underscore the necessity of operationalizing trust—not just claiming to uphold ethical standards but demonstrating it through consistent and accountable actions.

Challenges in Building Trust

Despite the growing consensus on the importance of trust, organizations frequently encounter challenges in its operationalization. Common misconceptions include the belief that merely invoking ethical principles suffices or that trust can be seamlessly integrated without ongoing effort. Instead, organizations must embrace a culture of accountability, continuously scrutinizing both their data practices and AI system deployments.

A Future-Ready AI Landscape

As AI technology advances, the potential for innovation is accompanied by the responsibility to cultivate trust. Failure to do so may lead to public skepticism and regulatory hurdles. Upholding trust will inevitably pave the way for broader acceptance and adoption of AI agents across diverse sectors. The path ahead supports an iterative process, where the lessons learned inform future AI developments and the evolving role of these systems in our lives.

Conclusion: Trust in AI is a Journey

In sum, the journey toward building trustworthy AI agents begins with recognizing that trust must be operationalized throughout their lifecycle. Organizations leveraging SAS Viya not only optimize their AI initiatives but also embody an ethical commitment to transparency, accountability, and human collaboration. As the landscape of AI continues to evolve, the fostering of trust will be fundamental to its success.

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12.04.2025

Unlocking the Potential of Job Scheduling in SAS Viya for AI Learning

Update The Importance of Job Scheduling in SAS Viya In today’s fast-paced digital world, automation is key to efficiency, especially when it comes to data management and analytical workflows. For users of SAS Viya, job scheduling emerges as a vital feature that simplifies operations by allowing processes to run without manual intervention. Forgetting to trigger a job or mismanagement can result in lost time and productivity; thus, mastering job scheduling is paramount for anyone looking to leverage SAS Viya effectively. Understanding Jobs and Their Significance in Viya A job in SAS Viya refers to any unit of work that executes a specified task, such as running a program, refreshing a Visual Analytics report, or executing data plans. By saving these jobs within the SAS Viya platform, users can automate when and how often these tasks are performed. This scheduling capability is crucial for maintaining a smooth workflow that can adapt to the demands of data analytics and reporting. How to Create and Schedule Jobs in SAS Viya The process of creating a scheduled job within SAS Viya begins within the SAS Studio environment, where users can write and store their SAS code. After doing so, the scheduling process is initiated by selecting “Schedule as a Job” from the options menu. Users are then guided through a straightforward dialog that allows them to define the frequency of the job execution, the start time, and even the time zone. This personalized schedule is designed to fit the specific needs of the user or organization. Monitoring and Confirming Scheduled Jobs To ensure that jobs are executed as scheduled, SAS Viya provides the Environment Manager feature. By navigating to the Jobs and Flows page within the manager, users can monitor scheduled jobs and verify their successful completion by looking for the blue clock icon next to their job under the Scheduled column. This feature enhances user confidence in the reliability of their automated tasks, thereby fostering a more proactive approach to data management. Exploring Job Flows: Advanced Scheduling Techniques Job flows expand the functionality of standard job scheduling by enabling users to connect multiple jobs and establish execution dependencies. For instance, one job can be set to commence only after another has completed successfully, providing an intelligent chain of operations. This capability is particularly beneficial for more complex processes, such as ETL activities, where multiple interdependent tasks must be carefully orchestrated within time-sensitive workflows. Utilizing Command Line Scheduling for Power Users For those who prefer command line interfaces, SAS Viya allows for job scheduling through its CLI, enabling the creation of intricate time-based triggers. This feature is ideal for advanced users or IT administrators seeking to incorporate SAS jobs into broader automation scripts, streamlining overall data operations and ensuring timely execution without manual input. Conclusion Automating task scheduling in SAS Viya not only saves time but also enhances data processing efficiency significantly. Whether you’re a beginner or have advanced skills, mastering job scheduling and flows can lead to optimized workflows that support organizational goals. By leveraging these features, you position yourself and your organization at the forefront of data analytics innovation. Interested in harnessing the full potential of AI in your job scheduling processes? Explore various AI learning paths that can help you enhance your skills and transform your approach to data technology. Integrating AI science into your work could propel your efficiency and insights to new heights.

12.04.2025

Exploring Responsible AI Design: Sierra Shell's Approach to Trust and Ethics

Update The Rise of Responsible Innovation in AI As the capabilities of artificial intelligence (AI) continue to expand, so does the collective commitment to responsible innovation. Sierra Shell, a prominent UX designer at SAS, exemplifies this shift. Her work focuses on creating AI user experiences that embody trust, accountability, and human-centric design. The essence of responsible innovation lies in ensuring that AI systems operate transparently and ethically, values that are becoming increasingly vital in today’s technology landscape. Designing for Trust and Accountability In her role, Sierra Shell is dedicated to helping users navigate complex AI systems with ease. She emphasizes a dual approach by ensuring user interfaces are not only intuitive but also encourage thoughtful decision-making. "Building technology that earns trust involves creating instinctual design elements that prompt users to reflect on their actions," she explains. With features that offer impact analyses before edits are made, Sierra ensures users weigh the consequences of their actions, promoting a culture of accountability. Understanding the Realities of AI Governance AI governance is not just a regulatory checkbox; it's a fundamental aspect of how AI impacts our daily lives. Shell asserts that the very design of an interface can influence user behavior significantly. A poorly designed consent pop-up, for example, can lead to users bypassing crucial privacy settings simply for convenience. "Responsible design must make the empowered choice the default choice," she notes, highlighting the importance of ethical UI/UX in promoting user rights. Education and Empowerment through Clear UI/UX Effective UI/UX design in AI governance enhances user education and engagement. By making policies visually accessible and understandable, users can better grasp the implications of AI interactions. This transparency builds trust and empowers users to make informed decisions about their data. As emphasized in recent insights about AI ethics, designers need to aim for clarity, ensuring interfaces are straightforward and free from manipulative patterns. Architecting the Future of Ethical AI Interfaces The future trend in AI design will prioritize ethical considerations, transforming how technology interacts with daily life. Successful products will increasingly feature designs rooted in fairness, privacy, and inclusion. Designers must remain vigilant against biases and proactively create experiences that cater to diverse populations. Organizations that prioritize ethical design will not only enhance user trust but also fortify their market reputation amidst growing scrutiny in AI practices. Next Steps for AI Evolution For companies looking to adopt ethical AI practices, conducting a comprehensive audit of existing interfaces is crucial. This involves assessing compliance with the latest ethical standards, ensuring they prioritize user agency, and continuously evolving through user feedback. Those willing to embrace a proactive stance on ethical design are positioned to lead in innovation while maintaining public trust. Conclusion: A Call for Ethical Innovation The commitment to responsible innovation is not merely beneficial but necessary as technology reshapes our interactions. By prioritizing ethical AI designs that emphasize transparency, inclusion, and user empowerment, we can forge a more trustworthy digital environment. As readers and technology stakeholders, actively participating in this conversation and advocating for responsible practices can help us all create a future where innovation and integrity go hand in hand. Let's push for AI that serves humanity ethically and responsibly, paving the way for exploring AI learning paths, AI science, and more.

12.03.2025

Why AI Governance Can No Longer Be Delayed: Key Insights

Update Understanding the Urgent Need for AI GovernanceIn the rapidly advancing landscape of artificial intelligence (AI), effective governance is no longer just a regulatory responsibility—it’s a crucial factor that determines the success and safety of AI applications across various sectors. As industries like finance harness AI to innovate and enhance operational efficiency, they must simultaneously confront emerging risks such as data bias, privacy infringements, and model inaccuracies. Recent discussions, particularly at the 'AI Governance and Future Innovation Strategy Seminar' held by SAS, highlight the pressing need for comprehensive governance frameworks to manage these risks.Key Steps for Financial Institutions in AI GovernanceAs the implementation of a basic AI law approaches, financial institutions in Korea must prioritize specific actions to align their governance frameworks effectively. Stephen Tonna, SAS’s Model and AI Governance Head, emphasized the importance of rigorous oversight, which includes:Expanding model inventories beyond credit risk to include generative AI and large language models (LLMs).Implementing real-time monitoring systems to detect model drift and responses to potential vulnerabilities like hallucination or jailbreaking attempts.Establishing robust documentation processes to demonstrate regulatory compliance effectively.Creating integrated governance teams that bring together compliance, risk, and data departments.These steps are instrumental in not only adhering to regulations but also in ensuring the ethical application of AI technologies.Real-Time Response: The Cornerstone of AI Risk ManagementOne of the central pillars for effective AI governance is the ability to respond to issues in real-time. In the context of finance, this means having systems in place that can promptly address customer inquiries and concerns without delay. Given that data breaches or unauthorized access can pose significant risks, financial institutions must adopt technological measures such as data tokenization, encryption, and the implementation of data loss prevention (DLP) solutions. Furthermore, establishing a proactive monitoring system can help detect anomalies before they escalate into more significant complications.Building a Comprehensive Governance FrameworkA robust AI governance framework transcends mere rule-setting—it encompasses the entire lifecycle of AI model development, from inception to deployment. This involves maintaining transparent records of every stage, the required approvals, and final validations of AI models. Collaboration among departments is key to achieving integrated management of AI applications.Amidst the complexities of AI governance, companies are reminded that they face significant challenges and responsibilities. However, by partnering with global experts, they can enhance their compliance frameworks to derive maximum value from AI technologies.Case Studies: Learning from Best PracticesExamining the successful governance frameworks of leading global tech firms can provide invaluable insights. Companies like Google and Microsoft have invested heavily in establishing ethical guidelines and compliance checks for their AI systems. Their practices include extensive user testing, thorough documentation of algorithmic decision-making processes, and stakeholder engagement to ensure a responsible approach to AI deployment.Conclusion: Embrace AI Governance NowWith the AI landscape evolving at breakneck speed, now is the time for businesses and organizations to invest in AI governance. Fostering transparency, ensuring compliance, and implementing effective monitoring systems are fundamental steps to harnessing AI’s potential responsibly. As the seminar highlights, neglecting these measures poses risks not just to organizations but to consumers and society at large.

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