Hard drives: the backbone of trustworthy AI.

As AI becomes increasingly integrated into more industries and use cases, its effectiveness hinges on producing reliable outputs. Reliable AI has become a sought-after business currency.

Building trustworthy AI relies on key elements that ensure data and outputs are dependable. This article will explore the roles of transparency, data lineage, explainability, accountability, and security in shaping AI systems we can trust. Each of these elements supports the data integrity and reliability essential to AI’s success, with hard drives providing the backbone of storage needed to deliver these benefits consistently.

Trustworthy AI refers to AI data workflows that use dependable inputs and generate reliable insights. Trustworthy AI is built on data that meets the following criteria:

• high quality and accuracy
• clear legality, ownership, and provenance
• secure storage and protection
• explainable and traceable transformations by the algorithm
• consistent and reliable outputs from the data processing.

Scalable storage infrastructure supports trustworthy AI, helping vast amounts of data used by AI systems to be properly managed, stored, and secured.

Trustworthy AI in large-scale data centers.

AI processes involve vast amounts of data that require robust infrastructure to manage efficiently. In order to manage these massive datasets, data centers that support AI workloads are equipped with scalable storage clusters that enable object stores and data lakes. This infrastructure underpins the entire AI data loop—from sourcing raw data to preserving model outputs for future use.

Without the scale and efficiency of data centers, AI’s potential would be limited, as the ability to store and retrieve massive datasets is central to AI’s success.

Modern AI-optimized architectures involve a balanced integration of compute, storage, and networking layers. Data lakes and object stores—often utilizing multiple storage tiers—form the foundation of AI environments that allow high-performance computing at scale. Storage infrastructure is critical to making sure AI systems have access both to data needing immediate access and to archival data. Architectures built for AI are designed with massive scalability in mind. The balance of storage capacity and performance drives the ability of AI systems to operate efficiently and scale according to demand.

Key elements of trustworthy AI.

But scalable architectures are not enough. Trustworthy AI also needs components that enable trust: transparency, data lineage, explainability, accountability, and security. Let’s take a look at how these elements support the integrity of AI workflows.

Transparency: making AI understandable.

Transparency at scale is key to trustworthy AI. It guarantees that decisions made by AI systems are understandable, accessible, replicable, and correctable. When an AI system recommends a movie, for example, transparency helps users understand the rationale behind the suggestion, noting that it’s grounded in clear data, like the user’s viewing history and preferences.

In data centers, scalable storage clusters support transparency by providing clear records of every decision point in the AI data lifecycle. With these infrastructures, organizations can trace data from its origin through processing to output, thereby fostering greater accountability.

Transparency plays a role across a number of industries. Three examples:

• In healthcare, AI algorithms analyze medical images, aiding in early disease detection. The more transparent the data, the more accurate the diagnoses.
• In finance, trading algorithms process market data rapidly and filter it more efficiently, allowing portfolio managers to better understand and optimize investment strategies. Trustworthy data can improve returns on investments.
• In natural language processing (NLP), chatbots respond to customer inquiries. Transparent data lineage keeps businesses accountable.

The lens of transparency fosters greater responsibility by clarifying the data, decisions, and model outputs.

Data lineage: tracing data sources and usage.

Data lineage is the ability to identify the origin (provenance) and usage of datasets throughout the AI process, which is crucial for understanding how models arrive at decisions.

For example, in healthcare AI applications, data lineage helps track which datasets were used to make diagnoses, indicating the information sources.

By providing a clear record of the journey data takes from input to output, data lineage allows organizations to verify the origin and usage of datasets, ensuring that AI models rely on accurate data. By tracking data through each stage of processing, data lineage enables AI systems to be fully auditable and supports both regulatory compliance and internal accountability. Hard drives facilitate data lineage by securely storing each transformation, allowing developers to review historical data records that reveal the full scope of AI decision-making processes.

Explainability: clarifying AI decision-making.

Explainability ensures that AI decisions are understandable and grounded in data that can be traced and evaluated. This is especially crucial in high-stakes industries like healthcare and finance, where understanding the reasoning behind AI decisions can affect lives and investments. By retaining checkpoints, hard drives enable developers to look back at various stages of model development, allowing them to assess how changes in data inputs or configurations affect outcomes. With this approach, AI systems become more transparent and comprehensible, fostering greater trust and usability.

Accountability: enabling auditable AI workflows.

Accountability in AI ensures that models can be examined and verified by stakeholders. Through checkpointing and data lineage, hard drives provide an audit trail that documents AI development from data input to output, allowing organizations to review the factors contributing to AI-generated decisions. This audit trail helps organizations adhere to regulatory standards and assures users that AI systems are built on reliable, repeatable processes. Accountability makes it possible to pinpoint specific checkpoints where decisions were made, holding AI systems responsible for their actions.

Security: safeguarding data integrity.

Security underpins trustworthy AI by protecting data from unauthorized access and tampering. Secure storage solutions, including encryption and integrity checks, ensure that AI models rely on authentic and unaltered data. Hard drives help support security by preserving data in a stable, controlled environment—helping organizations prevent tampering, and enabling compliance with stringent security regulations. By securing data at every stage of the AI process, companies can maintain trust in the integrity of AI workflows.

Mechanisms that enable trustworthy AI.

Achieving these elements of trustworthy AI relies on robust mechanisms that support data integrity, security, and accountability. From checkpointing and governance policies to hashing and mass storage systems, these tools ensure that AI systems meet the high standards needed for reliable decision-making. Below, we explore how these mechanisms underpin trustworthy AI.

Checkpointing: supporting multiple elements.

Checkpointing is the process of saving the state of an AI model at specific, short intervals during its training. AI models are trained on large datasets through iterative processes that can take anywhere from minutes to days.

Checkpoints act like snapshots of the model’s current state—its data, parameters, and settings—at many points during training. Saved to storage devices every minute to few minutes, the snapshots allow developers to retain a record of the model's progression and to avoid losing valuable work due to unexpected interruptions.

Checkpoints enable trustworthy AI by serving several critical purposes:

Power protection. Checkpoints safeguard training jobs from system failures, power outages, or crashes, allowing models to resume from the last saved state without starting from scratch.

Model improvement and optimization. By saving checkpoints, developers can analyze past states, fine-tune model parameters, and enhance performance over time.

Legal compliance and intellectual property protection. Checkpoints provide a transparent record that helps organizations comply with legal frameworks and protect proprietary methodologies.

Building trust and ensuring transparency. Checkpointing records model states, supporting explainability by making AI decisions traceable and understandable.

Governance policies: guiding responsible data management.

Governance policies establish the framework within which data is managed, protected, and used throughout the AI lifecycle. These policies ensure that AI systems adhere to regulatory requirements and internal standards, creating an environment where data is handled ethically and securely. Governance policies define access controls, data retention schedules, and compliance procedures, thereby supporting security and accountability within AI workflows. By setting these standards, organizations can ensure that AI systems are transparent, reliable, and built on sound data management principles.

Hashing: securing data lineage

Hashing plays a crucial role in maintaining data lineage by creating unique digital fingerprints for data. These fingerprints enable organizations to verify that data has not been altered or tampered with at any stage in the AI process. By hashing datasets and checkpoints, AI systems can ensure that data inputs remain consistent and uncorrupted, bolstering security and contributing to transparency. Hard drives store these hashed data records, enabling organizations to verify data authenticity and maintain trust in AI workflows.

Mass-capacity storage systems: enabling scalable, secure data retention.

Mass storage systems, particularly those that leverage hard drives, provide the foundational capacity needed to store and manage the vast amounts of data essential for trustworthy AI.

Hard drives offer scalable, cost-effective storage that ensures AI systems have access to both current and archival data. These systems support transparency by maintaining accessible records, explainability by preserving data over time, and security by providing stable environments for data storage.

Seagate Exos® series hard drives, powered by Mozaic 3+™ technology, were built to provide this specific type of support. They store raw datasets that fuel AI models, detailed records of data creation processes, iterative checkpoints during model training, and the preservation of AI analysis outcomes.

Hard drives play a vital role in this transparency by storing vast datasets and other critical information that AI models rely on. This data is made easily accessible through a combination of networked hard drives for long-term retention and SSDs for immediate access, allowing organizations to keep track of every decision point in the AI lifecycle.

The meticulous hard drive-enabled documentation helps maintain regulatory compliance, enhances explainability, allows model improvement, and fosters greater accountability. Hard drives document the full lifecycle of data, allowing for clear, traceable records that can be reviewed to verify compliance with regulations and policies.

Data volumes are surging across various domains. In healthcare, areas like genomics research and medical imaging generate petabytes of data annually. IoT devices, including sensors and connected gadgets, as well as the explosion of user-generated content on social media significantly contribute to this data deluge.

Hard drives have emerged as cost-effective and scalable storage options. They offer high storage capacity at the lowest cost per terabyte (at a ratio of 6:1, when compared to flash storage), which makes them the optimal choice for long-term data retention. That’s why hard drives are the go-to storage for preserving the massive raw datasets used for AI processing and the storage of AI analysis outcomes. In addition to securing long-term storage of inputs and outputs, hard drives also support AI workflows during the compute-heavy training stage, keeping track of checkpoints and saving various content iterations.

Conclusion.

The path to reliable AI is paved with transparency, data lineage, explainability, accountability, and security. These elements allow organizations to transform data from simple figures into trustworthy innovations.

The path to reliable AI is paved with transparency, data lineage, explainability, accountability, and security. These elements allow organizations to transform data from simple figures into trustworthy innovations.

By supporting the entire AI data workflow—from capturing raw data to retaining checkpoints and preserving analyses’ outcomes—hard drives play a crucial role in validating, fine-tuning, and building trust into AI models over time. By leveraging hard drives for long-term data retention, AI developers can revisit past training runs, analyze results, and adjust models to improve efficiency and accuracy.

As AI expands across sectors, preserving data lineage, meeting regulatory standards, and establishing clear stakeholder communication becomes crucial. Seagate engineers built hard drives to be the scalable, cost-effective storage solutions needed to support these efforts. As a result, AI developers can build systems that are smart and trustworthy.