{"id":477663,"date":"2025-05-10T17:00:30","date_gmt":"2025-05-10T16:00:30","guid":{"rendered":"https:\/\/cryptoslate.com\/?p=477663"},"modified":"2025-05-10T13:09:23","modified_gmt":"2025-05-10T12:09:23","slug":"ai-model-audits-need-a-trust-but-verify-approach-to-enhance-reliability","status":"publish","type":"post","link":"https:\/\/cryptoslate.com\/ai-model-audits-need-a-trust-but-verify-approach-to-enhance-reliability\/","title":{"rendered":"AI model audits need a \u2018trust, but verify\u2019 approach to enhance reliability"},"content":{"rendered":"

The following is a guest post and opinion of Samuel Pearton, CMO at Polyhedra.<\/em><\/p>\n

Reliability remains a mirage in the ever-expanding realm of AI models, affecting mainstream AI<\/a> adoption in critical sectors like healthcare and finance. AI model audits are essential in restoring reliability within the AI industry, helping regulators, developers, and users enhance accountability and compliance.<\/p>\n

But AI model audits can be unreliable since auditors have to independently review the pre-processing (training), in-processing (inference), and post-processing (model deployment) stages. A \u2018trust, but verify\u2019 approach improves reliability in audit processes and helps society rebuild trust in AI.<\/p>\n

Traditional AI Model Audit Systems Are Unreliable<\/h2>\n

AI model audits are useful for understanding how an AI system works, its potential impact, and providing evidence-based reports for industry stakeholders.<\/p>\n

For instance, companies use audit reports to acquire AI models based on due diligence, assessment, and comparative benefits between different vendor models. These reports further ensure developers have taken necessary precautions at all stages and that the model complies with existing regulatory frameworks.<\/p>\n

But AI model audits are prone to reliability issues due to their inherent procedural functioning and human resource challenges.<\/p>\n

According to the European Data Protection Board\u2019s (EDPB) AI auditing checklist<\/a>, audits from a \u201ccontroller\u2019s implementation of the accountability principle\u201d<\/em> and \u201cinspection\/investigation carried out by a Supervisory Authority\u201d<\/em> could be different, creating confusion among enforcement agencies.<\/p>\n

EDPB\u2019s checklist covers implementation mechanisms, data verification, and impact on subjects through algorithmic audits. But the report also acknowledges audits are based on existing systems and don\u2019t question \u201cwhether a system should exist in the first place.\u201d<\/em><\/p>\n

Besides these structural problems, auditor teams require updated domain knowledge of data sciences and machine learning. They also require complete training, testing, and production sampling data spread across multiple systems, creating complex workflows and interdependencies.<\/p>\n

Any knowledge gap or error between coordinating team members can lead to a cascading effect and invalidate the entire audit process. As AI models become more complex, auditors will have additional responsibilities to independently verify and validate reports before aggregated conformity and remedial checks.<\/p>\n

The AI industry\u2019s progress is rapidly outpacing auditors\u2019 capacity and capability to conduct forensic analysis and assess AI models. This leaves a void in audit methods, skill sets, and regulatory enforcement, deepening the trust crisis in AI model audits.<\/p>\n

An auditor\u2019s primary task is to enhance transparency by evaluating risks, governance, and underlying processes of AI models. When auditors lack the knowledge and tools to assess AI and its implementation within organizational environments, user trust is eroded.<\/p>\n

A Deloitte report outlines<\/a> the three lines of AI defense. In the first line, model owners and management have the main responsibility to manage risks. This is followed by the second line, where policy workers provide the needed oversight for risk mitigation.<\/p>\n

The third line of defense is the most important, where auditors gauge the first and second lines to evaluate operational effectiveness. Subsequently, auditors submit a report to the Board of Directors, collating data on the AI model\u2019s best practices and compliance.<\/p>\n

To enhance reliability in AI model audits, the people and underlying tech must adopt a \u2018trust but verify\u2019 philosophy during audit proceedings.<\/p>\n

A \u2018Trust, But Verify\u2019 Approach to AI Model Audits<\/h2>\n

\u2018Trust, but verify\u2019 is a Russian proverb that U.S. President Ronald Reagan popularized during the United States\u2013Soviet Union nuclear arms treaty. Reagan\u2019s stance<\/a> of \u201cextensive verification procedures that would enable both sides to monitor compliance\u201d<\/em> is beneficial for reinstating reliability in AI model audits.<\/p>\n

In a \u2018trust but verify\u2019 system, AI model audits require continuous evaluation and verification before trusting the audit results. In effect, this means there is no such thing as auditing an AI model, preparing a report, and assuming it to be correct.<\/p>\n

So, despite stringent verification procedures and validation mechanisms of all key components, an AI model audit is never safe. In a research paper, Penn State engineer Phil Laplante and NIST Computer Security Division member Rick Kuhn have called<\/a> this the \u2018trust but verify continuously\u2019<\/em> AI architecture.<\/p>\n

The need for constant evaluation and continuous AI assurance by leveraging the \u2018trust but verify continuously\u2019 infrastructure is critical for AI model audits. For example, AI models often require re-auditing and post-event reevaluation since a system\u2019s mission or context can change over its lifespan.<\/p>\n

A \u2018trust but verify\u2019 method during audits helps determine model performance degradation through new fault detection techniques. Audit teams can deploy testing and mitigation strategies with continuous monitoring, empowering auditors to implement robust algorithms and improved monitoring facilities.<\/p>\n

Per Laplante and Kuhn, \u201ccontinuous monitoring of the AI system is an important part of the post-deployment assurance process model.\u201d<\/em> Such monitoring is possible through automatic AI audits where routine self-diagnostic tests are embedded into the AI system.<\/p>\n

Since internal diagnosis may have trust issues, a trust elevator with a mix of human and machine systems can monitor AI. These systems offer stronger AI audits by facilitating post-mortem and black box recording analysis for retrospective context-based result verification.<\/p>\n

An auditor\u2019s primary role is to referee and prevent AI models from crossing trust threshold boundaries. A \u2018trust but verify\u2019 approach enables audit team members to verify trustworthiness explicitly at each step. This solves the lack of reliability in AI model audits by restoring confidence in AI systems through rigorous scrutiny and transparent decision-making.<\/p>\n","protected":false},"excerpt":{"rendered":"

The following is a guest post and opinion of Samuel Pearton, CMO at Polyhedra. Reliability remains a mirage in the ever-expanding realm of AI models, affecting mainstream AI adoption in critical sectors like healthcare and finance. AI model audits are essential in restoring reliability within the AI industry, helping regulators, developers, and users enhance accountability […]<\/p>\n","protected":false},"author":1592,"featured_media":477922,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[78326,25891,36],"tags":[],"prediction_market_topic":[],"post_folder":[],"acf":[],"yoast_head":"\nAI model audits need a \u2018trust, but verify\u2019 approach to enhance reliability<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cryptoslate.com\/ai-model-audits-need-a-trust-but-verify-approach-to-enhance-reliability\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI model audits need a \u2018trust, but verify\u2019 approach to enhance reliability\" \/>\n<meta property=\"og:description\" content=\"The following is a guest post and opinion of Samuel Pearton, CMO at Polyhedra. 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Drawing on decades of experience in tech, global marketing, and cross-cultural social commerce, Samuel understands that trust, scalability, and verifiability are essential to AI and blockchain.. Before officially joining Polyhedra\u2019s executive team in October 2024, he played a key advisory role as the company secured $20 million in strategic funding at a $1 billion valuation. Prior to Polyhedra, Samuel founded PressPlayGlobal, a social commerce and engagement platform that connected athletes and celebrities\u2014including Stephen Curry and other leading global brands\u2014with China\u2019s largest consumer fan market.","url":"https:\/\/cryptoslate.com\/author\/samuel-pearton\/"}]}},"_links":{"self":[{"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/posts\/477663"}],"collection":[{"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/users\/1592"}],"replies":[{"embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/comments?post=477663"}],"version-history":[{"count":3,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/posts\/477663\/revisions"}],"predecessor-version":[{"id":477923,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/posts\/477663\/revisions\/477923"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/media\/477922"}],"wp:attachment":[{"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/media?parent=477663"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/categories?post=477663"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/tags?post=477663"},{"taxonomy":"prediction_market_topic","embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/prediction_market_topic?post=477663"},{"taxonomy":"post_folder","embeddable":true,"href":"https:\/\/cryptoslate.com\/wp-json\/wp\/v2\/post_folder?post=477663"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}