Trendy synthetic intelligence (AI) techniques pose new sorts of dangers, and many of those are each consequential and never effectively understood. Regardless of this, many AI-based techniques are being accelerated into deployment. That is creating nice urgency to develop efficient check and analysis (T&E) practices for AI-based techniques.
This weblog submit explores potential methods for framing T&E practices on the idea of a holistic strategy to AI threat. In growing such an strategy, it’s instructive to construct on classes realized within the many years of wrestle to develop analogous practices for modeling and assessing cyber threat. Cyber threat assessments are imperfect and proceed to evolve, however they supply important profit nonetheless. They’re strongly advocated by the Cybersecurity and Infrastructure Safety Company (CISA), and the prices and advantages of assorted approaches are a lot mentioned within the enterprise media. About 70% of inside audits for big companies embody cyber threat assessments, as do mandated stress exams for banks.
Danger modeling and assessments for AI are much less effectively understood from each technical and authorized views, however there’s pressing demand from each enterprise adopters and vendor suppliers nonetheless. The industry-led Coalition for Safe AI launched in July 2024 to assist advance {industry} norms round enhancing the safety of contemporary AI implementations. The NIST AI Danger Administration Framework (RMF) is resulting in proposed practices. Methodologies primarily based on the framework are nonetheless a piece in progress, with unsure prices and advantages, and so AI threat assessments are much less usually utilized than cyber threat assessments.
Danger modeling and evaluation are essential not solely in guiding T&E, but additionally in informing engineering practices, as we’re seeing with cybersecurity engineering and within the rising observe of AI engineering. AI engineering, importantly, encompasses not simply particular person AI parts in techniques but additionally the general design of resilient AI-based techniques, together with the workflows and human interactions that allow operational duties.
AI threat modeling, even in its present nascent stage, can have helpful affect in each T&E and AI engineering practices, starting from total design decisions to particular threat mitigation steps. AI-related weaknesses and vulnerabilities have distinctive traits (see examples within the prior weblog posts), however additionally they overlap with cyber dangers. AI system parts are software program elements, in spite of everything, so that they usually have vulnerabilities unrelated to their AI performance. Nonetheless, their distinctive and sometimes opaque options, each throughout the fashions and within the surrounding software program constructions, could make them particularly enticing to cyber adversaries.
That is the third installment in a four-part collection of weblog posts centered on AI for important techniques the place trustworthiness—primarily based on checkable proof—is crucial for operational acceptance. The 4 components are comparatively unbiased of one another and handle this problem in phases:
- Half 1: What are acceptable ideas of safety and security for contemporary neural-network-based AI, together with machine studying (ML) and generative AI, reminiscent of massive language fashions (LLMs)? What are the AI-specific challenges in growing secure and safe techniques? What are the boundaries to trustworthiness with fashionable AI, and why are these limits elementary?
- Half 2: What are examples of the sorts of dangers particular to fashionable AI, together with dangers related to confidentiality, integrity, and governance (the CIG framework), with and with out adversaries? What are the assault surfaces, and what sorts of mitigations are presently being developed and employed for these weaknesses and vulnerabilities?
- Half 3 (this half): How can we conceptualize T&E practices acceptable to fashionable AI? How, extra typically, can frameworks for threat administration (RMFs) be conceptualized for contemporary AI analogous to these for cyber threat? How can a observe of AI engineering handle challenges within the close to time period, and the way does it work together with software program engineering and cybersecurity issues?
- Half 4: What are the advantages of trying past the purely neural-network fashions of contemporary AI in direction of hybrid approaches? What are present examples that illustrate the potential advantages, and the way, trying forward, can these approaches advance us past the elemental limits of contemporary AI? What are prospects within the close to and longer phrases for hybrid AI approaches which might be verifiably reliable and that may help extremely important functions?
Assessments for Practical and High quality Attributes
Practical and high quality assessments assist us achieve confidence that techniques will carry out duties accurately and reliably. Correctness and reliability aren’t absolute ideas, nonetheless. They have to be framed within the context of supposed functions for a element or system, together with operational limits that have to be revered. Expressions of intent essentially embody each performance—what the system is meant to perform—and system qualities—how the system is meant to function, together with safety and reliability attributes. These expressions of intent, or techniques specs, could also be scoped for each the system and its function in operations, together with expectations relating to stressors reminiscent of adversary threats.
Trendy AI-based techniques pose important technical challenges in all these points, starting from expressing specs to acceptance analysis and operational monitoring. What does it imply, for instance, to specify intent for a educated ML neural community, past inventorying the coaching and testing knowledge?
We should contemplate, in different phrases, the conduct of a system or an related workflow below each anticipated and surprising inputs, the place these inputs could also be significantly problematic for the system. It’s difficult, nonetheless, even to border the query of the right way to specify behaviors for anticipated inputs that aren’t precisely matched within the coaching set. A human observer might have an intuitive notion of similarity of recent inputs with coaching inputs, however there is no such thing as a assurance that this aligns with the precise that includes—the salient parameter values—inside to a educated neural community.
We should, moreover, contemplate assessments from a cybersecurity perspective. An knowledgeable and motivated attacker might intentionally manipulate operational inputs, coaching knowledge, and different points of the system improvement course of to create circumstances that impair appropriate operation of a system or its use inside a workflow. In each instances, the absence of conventional specs muddies the notion of “appropriate” conduct, additional complicating the event of efficient and inexpensive practices for AI T&E. This specification problem suggests one other commonality with cyber threat: facet channels, that are potential assault surfaces which might be unintended to implementation and that is probably not a part of a specification.
Three Dimensions of Cyber Danger
This alignment within the rising necessities for AI-focused T&E with strategies for cybersecurity analysis is clear when evaluating NIST’s AI threat administration playbook with the extra mature NIST Cybersecurity Framework, which encompasses an enormous range of strategies. On the threat of oversimplification, we are able to usefully body these strategies within the context of three dimensions of cyber threat.
- Risk issues the potential entry and actions of adversaries in opposition to the system and its broader operational ecosystem.
- Consequence pertains to the magnitude of influence on a corporation or mission ought to an assault on a system achieve success.
- Vulnerability pertains to intrinsic design weaknesses and flaws within the implementation of a system.
Each risk and consequence carefully rely on the operational context of use of that system, although they are often largely extrinsic to the system itself. Vulnerability is attribute of the system, together with its structure and implementation. The modeling of assault floor—apertures right into a system which might be uncovered to adversary actions—encompasses risk and vulnerability, as a result of entry to vulnerabilities is a consequence of operational setting. It’s a significantly helpful aspect of cyber threat evaluation.
Cyber threat modeling is in contrast to conventional probabilistic actuarial threat modeling. That is primarily because of the typically nonstochastic nature of every of the three dimensions, particularly when threats and missions are consequential. Risk, for instance, is pushed by the operational significance of the system and its workflow, in addition to potential adversary intents and the state of their data. Consequence, equally, is decided by decisions relating to the location of a system in operational workflows. Changes to workflows—and human roles—is a mitigation technique for the consequence dimension of threat. Dangers could be elevated when there are hidden correlations. For cyber threat, these might embody widespread parts with widespread vulnerabilities buried in provide chains. For AI threat, these might embody widespread sources inside massive our bodies of coaching knowledge. These correlations are a part of the rationale why some assaults on LLMs are moveable throughout fashions and suppliers.
CISA, MITRE, OWASP, and others provide handy inventories of cyber weaknesses and vulnerabilities. OWASP, CISA, and the Software program Engineering Institute additionally present inventories of secure practices. Most of the generally used analysis standards derive, in a bottom-up method, from these inventories. For weaknesses and vulnerabilities at a coding degree, software program improvement environments, automated instruments, and continuous-integration/continuous-delivery (CI/CD) workflows usually embody evaluation capabilities that may detect insecure coding as builders kind it or compile it into executable elements. Due to this quick suggestions, these instruments can improve productiveness. There are a lot of examples of standalone instruments, reminiscent of from Veracode, Sonatype, and Synopsys.
Importantly, cyber threat is only one aspect within the total analysis of a system’s health to be used, whether or not or not it’s AI-based. For a lot of built-in hardware-software techniques, acceptance analysis may even embody, for instance, conventional probabilistic reliability analyses that mannequin (1) sorts of bodily faults (intermittent, transient, everlasting), (2) how these faults can set off inside errors in a system, (3) how the errors might propagate into numerous sorts of system-level failures, and (4) what sorts of hazards or harms (to security, safety, efficient operation) might lead to operational workflows. This latter strategy to reliability has an extended historical past, going again to John von Neumann’s work within the Nineteen Fifties on the synthesis of dependable mechanisms from unreliable elements. Apparently, von Neumann cites analysis in probabilistic logics that derive from fashions developed by McCulloch and Pitts, whose neural-net fashions from the Nineteen Forties are precursors of the neural-network designs central to fashionable AI.
Making use of These Concepts to Framing AI Danger
Framing AI threat could be thought-about as an analog to framing cyber threat, regardless of main technical variations in all three points—risk, consequence, and vulnerability. When adversaries are within the image, AI penalties can embody misdirection, unfairness and bias, reasoning failures, and so forth. AI threats can embody tampering with coaching knowledge, patch assaults on inputs, immediate and fine-tuning assaults, and so forth. Vulnerabilities and weaknesses, reminiscent of these inventoried within the CIG classes (see Half 2), typically derive from the intrinsic limitations of the structure and coaching of neural networks as statistically derived fashions. Even within the absence of adversaries, there are a number of penalties that may come up because of the specific weaknesses intrinsic to neural-network fashions.
From the angle of conventional threat modeling, there’s additionally the problem, as famous above, of surprising correlations throughout fashions and platforms. For instance, there could be related penalties on account of diversely sourced LLMs sharing basis fashions or simply having substantial overlap in coaching knowledge. These surprising correlations can thwart makes an attempt to use strategies reminiscent of range by design as a method to enhance total system reliability.
We should additionally contemplate the precise attribute of system resilience. Resilience is the capability of a system that has sustained an assault or a failure to nonetheless proceed to function safely, although maybe in a degraded method. This attribute is typically referred to as swish degradation or the flexibility to function by assaults and failures. On the whole, this can be very difficult, and sometimes infeasible, so as to add resilience to an present system. It is because resilience is an emergent property consequential of system-level architectural choices. The architectural objective is to scale back the potential for inside errors—triggered by inside faults, compromises, or inherent ML weaknesses—to trigger system failures with expensive penalties. Conventional fault-tolerant engineering is an instance of design for resilience. Resilience is a consideration for each cyber threat and AI threat. Within the case of AI engineering, resilience could be enhanced by system-level and workflow-level design choices that, for instance, restrict publicity of weak inside assault surfaces, reminiscent of ML inputs, to potential adversaries. Such designs can embody imposing energetic checking on inputs and outputs to neural-network fashions constituent to a system.
As famous in Half 2 of this weblog collection, a further problem to AI resilience is the problem (or maybe lack of ability) to unlearn coaching knowledge. Whether it is found {that a} subset of coaching knowledge has been used to insert a vulnerability or again door into the AI system, it turns into a problem to take away that educated conduct from the AI system. In observe, this continues to stay troublesome and will necessitate retraining with out the malicious knowledge. A associated challenge is the other phenomenon of undesirable unlearning—referred to as catastrophic forgetting—which refers to new coaching knowledge unintentionally impairing the standard of predictions primarily based on earlier coaching knowledge.
Business Issues and Responses Relating to AI Danger
There’s a broad recognition amongst mission stakeholders and companies of the dimensionality and problem of framing and evaluating AI threat, regardless of fast development in AI-related enterprise actions. Researchers at Stanford College produced a 500-page complete enterprise and technical evaluation of AI-related actions that states that funding for generative AI alone reached $25.2 billion in 2023. That is juxtaposed in opposition to a seemingly limitless stock of new sorts of dangers related to ML and generative AI. Illustrative of it is a joint examine by the MIT Sloan Administration Evaluate and the Boston Consulting Group that signifies that companies are having to increase organizational threat administration capabilities to deal with AI-related dangers, and that this case is prone to persist because of the tempo of technological advance. A separate survey indicated that solely 9 p.c of companies stated they had been ready to deal with the dangers. There are proposals to advance necessary assessments to guarantee guardrails are in place. That is stimulating the service sector to reply, with unbiased estimates of a marketplace for AI mannequin threat administration price $10.5 billion by 2029.
Enhancing Danger Administration inside AI Engineering Follow
Because the neighborhood advances threat administration practices for AI, it is necessary have in mind each the various points of threat, as illustrated within the earlier submit of this collection, and likewise the feasibility of the completely different approaches to mitigation. It isn’t an easy course of: Evaluations should be finished at a number of ranges of abstraction and construction in addition to a number of phases within the lifecycles of mission planning, structure design, techniques engineering, deployment, and evolution. The numerous ranges of abstraction could make this course of troublesome. On the highest degree, there are workflows, human-interaction designs, and system architectural designs. Selections made relating to every of those points have affect over the danger parts: attractiveness to risk actors, nature and extent of penalties of potential failures, and potential for vulnerabilities on account of design choices. Then there’s the architecting and coaching for particular person neural-network fashions, the fine-tuning and prompting for generative fashions, and the potential publicity of assault surfaces of those fashions. Beneath this are, for instance, the precise mathematical algorithms and particular person traces of code. Lastly, when assault surfaces are uncovered, there could be dangers related to decisions within the supporting computing firmware and {hardware}.
Though NIST has taken preliminary steps towards codifying frameworks and playbooks, there stay many challenges to growing widespread parts of AI engineering observe—design, implementation, T&E, evolution—that might evolve into helpful norms—and vast adoption pushed by validated and usable metrics for return on effort. Arguably, there’s a good alternative now, whereas AI engineering practices are nonetheless nascent, to rapidly develop an built-in, full-lifecycle strategy that {couples} system design and implementation with a shift-left T&E observe supported by proof manufacturing. This contrasts with the observe of safe coding, which was late-breaking within the broader software program improvement neighborhood. Safe coding has led to efficient analyses and instruments and, certainly, many options of contemporary memory-safe languages. These are nice advantages, however safe coding’s late arrival has the unlucky consequence of an unlimited legacy of unsafe and sometimes weak code that could be too burdensome to replace.
Importantly, the persistent problem of straight assessing the safety of a physique of code hinders not simply the adoption of greatest practices but additionally the creation of incentives for his or her use. Builders and evaluators make choices primarily based on their sensible expertise, for instance, recognizing that guided fuzzing correlates with improved safety. In lots of of those instances probably the most possible approaches to evaluation relate to not the precise diploma of safety of a code base. As a substitute they give attention to the extent of compliance with a strategy of making use of numerous design and improvement strategies. Precise outcomes stay troublesome to evaluate in present observe. As a consequence, adherence to codified practices such because the safe improvement lifecycle (SDL) and compliance with the Federal Data Safety Modernization Act (FISMA) has turn out to be important to cyber threat administration.
Adoption may also be pushed by incentives which might be unrelated however aligned. For instance, there are intelligent designs for languages and instruments that improve safety however whose adoption is pushed by builders’ curiosity in enhancing productiveness, with out intensive coaching or preliminary setup. One instance from internet improvement is the open supply TypeScript language as a secure various to JavaScript. TypeScript is almost an identical in syntax and execution efficiency, however it additionally helps static checking, which could be finished nearly instantly as builders kind in code, quite than surfacing a lot later when code is executing, maybe in operations. Builders might thus undertake TypeScript on the idea of productiveness, with safety advantages alongside for the journey.
Potential optimistic alignment of incentives will probably be essential for AI engineering, given the problem of growing metrics for a lot of points of AI threat. It’s difficult to develop direct measures for basic instances, so we should additionally develop helpful surrogates and greatest practices derived from expertise. Surrogates can embody diploma of adherence to engineering greatest practices, cautious coaching methods, exams and analyses, decisions of instruments, and so forth. Importantly, these engineering strategies embody improvement and analysis of structure and design patterns that allow creation of extra reliable techniques from much less reliable parts.
The cyber threat realm presents a hybrid strategy of surrogacy and selective direct measurement by way of the Nationwide Data Assurance Partnership (NIAP) Frequent Standards: Designs are evaluated in depth, however direct assays on lower-level code are finished by sampling, not comprehensively. One other instance is the extra broadly scoped Constructing Safety In Maturity Mannequin (BSIMM) venture, which features a strategy of ongoing enhancement to its norms of observe. After all, any use of surrogates have to be accompanied by aggressive analysis each to repeatedly assess validity and to develop direct measures.
Analysis Practices: Trying Forward
Classes for AI Purple Teaming from Cyber Purple Teaming
The October 2023 Government Order 14110 on the Secure, Safe, and Reliable Growth and Use of Synthetic Intelligence highlights using crimson teaming for AI threat analysis. Within the army context, a typical strategy is to make use of crimson groups in a capstone coaching engagement to simulate extremely succesful adversaries. Within the context of cyber dangers or AI dangers, nonetheless, crimson groups will usually have interaction all through a system lifecycle, from preliminary mission scoping, idea exploration, and architectural design by to engineering, operations, and evolution.
A key query is the right way to obtain this type of integration when experience is a scarce useful resource. One of many classes of cyber crimson teaming is that it’s higher to combine safety experience into improvement groups—even on a part-time or rotating foundation—than to mandate consideration to safety points. Research recommend that this may be efficient when there are cross-team safety specialists straight collaborating with improvement groups.
For AI crimson groups, this means that bigger organizations might preserve a cross-team physique of specialists who perceive the stock of potential weaknesses and vulnerabilities and the state of play relating to measures, mitigations, instruments, and related practices. These specialists could be quickly built-in into agile groups so they may affect operational decisions and engineering choices. Their objectives are each to maximise advantages from use of AI and likewise to attenuate dangers by making decisions that help assured T&E outcomes.
There could also be classes for the Division of Protection, which faces specific challenges in integrating AI threat administration practices into the techniques engineering tradition, as famous by the Congressional Analysis Service.
AI crimson groups and cyber crimson groups each handle the dangers and challenges posed by adversaries. AI crimson groups should additionally handle dangers related to AI-specific weaknesses, together with all three CIG classes of weaknesses and vulnerabilities: confidentiality, integrity, and governance. Purple crew success will rely on full consciousness of all dimensions of threat in addition to entry to acceptable instruments and capabilities to help efficient and inexpensive assessments.
On the present stage of improvement, there’s not but a standardized observe for AI crimson groups. Instruments, coaching, and actions haven’t been totally outlined or operationalized. Certainly, it may be argued that the authors of Government Order 14110 had been smart to not await technical readability earlier than issuing the EO! Defining AI crimson crew ideas of operation is an monumental, long-term problem that mixes technical, coaching, operational, coverage, market, and lots of different points, and it’s prone to evolve quickly because the know-how evolves. The NIST RMF is a vital first step in framing this dimensionality.
Potential Practices for AI Danger
A broad range of technical practices is required for the AI crimson crew toolkit. Analogously with safety and high quality evaluations, AI stakeholders can anticipate to depend on a mixture of course of compliance and product examination. They may also be introduced with various sorts of proof starting from full transparency with detailed technical analyses to self-attestation by suppliers, with decisions sophisticated by enterprise issues regarding mental property and legal responsibility. This extends to produce chain administration for built-in techniques, the place there could also be various ranges of transparency. Legal responsibility is a altering panorama for cybersecurity and, we are able to anticipate, additionally for AI.
Course of compliance for AI threat can relate, for instance, to adherence to AI engineering practices. These practices can vary from design-level evaluations of how AI fashions are encapsulated inside a techniques structure to compliance with greatest practices for knowledge dealing with and coaching. They will additionally embody use of mechanisms for monitoring behaviors of each techniques and human operators throughout operations. We word that process-focused regimes in cyber threat, such because the extremely mature physique of labor from NIST, can contain a whole bunch of standards that could be utilized within the improvement and analysis of a system. Programs designers and evaluators should choose and prioritize among the many many standards to develop aligned mission assurance methods.
We will anticipate that with a maturing of strategies for AI functionality improvement and AI engineering, proactive practices will emerge that, when adopted, are inclined to lead to AI-based operational capabilities that reduce key threat attributes. Direct evaluation and testing could be complicated and expensive, so there could be actual advantages to utilizing validated process-compliance surrogates. However this may be difficult within the context of AI dangers. For instance, as famous in Half 1 of this collection, notions of check protection and enter similarity standards acquainted to software program builders don’t switch effectively to neural-network fashions.
Product examination can pose important technical difficulties, particularly with rising scale, complexity, and interconnection. It will possibly additionally pose business-related difficulties, on account of problems with mental property and legal responsibility. In cybersecurity, sure points of merchandise at the moment are changing into extra readily accessible as areas for direct analysis, together with use of exterior sourcing in provide chains and the administration of inside entry gateways in techniques. That is partly a consequence of a cyber-policy focus that advances small increments of transparency, what we might name translucency, reminiscent of has been directed for software program payments of supplies (SBOM) and 0 belief (ZT) architectures. There are, in fact, tradeoffs regarding transparency of merchandise to evaluators, and it is a consideration in using open supply software program for mission techniques.
Satirically, for contemporary AI techniques, even full transparency of a mannequin with billions of parameters might not yield a lot helpful info to evaluators. This pertains to the conflation of code and knowledge in fashionable AI fashions famous on the outset of this collection. There may be important analysis, nonetheless, in extracting associational maps from LLMs by patterns of neuron activations. Conversely, black field AI fashions might reveal much more about their design and coaching than their creators might intend. The perceived confidentiality of coaching knowledge could be damaged by mannequin inversion assaults for ML and memorized outputs for LLMs.
To be clear, direct analysis of neural-network fashions will stay a major technical problem. This offers further impetus to AI engineering and the applying of acceptable rules to the event and analysis of AI-based techniques and the workflows that use them.
Incentives
The proliferation of process- and product-focused standards, as simply famous, generally is a problem for leaders searching for to maximise profit whereas working affordably and effectively. The balancing of decisions could be extremely specific to the operational circumstances of a deliberate AI-based system in addition to to the technical decisions made relating to the inner design and improvement of that system. That is one purpose why incentive-based approaches can usually be fascinating over detailed process-compliance mandates. Certainly, incentive-based approaches can provide extra levels of freedom to engineering leaders, enabling threat discount by variations to operational workflows in addition to to engineered techniques.
Incentives could be each optimistic and unfavourable, the place optimistic incentives might be supplied, for instance, in improvement contracts, when assertions regarding AI dangers are backed with proof or accountability. Proof might relate to a variety of early AI-engineering decisions starting from techniques structure and operational workflows to mannequin design and inside guardrails.
An incentive-based strategy additionally has the benefit of enabling assured techniques engineering—primarily based on rising AI engineering rules—to evolve specifically contexts of techniques and missions whilst we proceed to work to advance the event of extra basic strategies. The March 2023 Nationwide Cybersecurity Technique highlights the significance of accountability relating to knowledge and software program, suggesting one essential doable framing for incentives. The problem, in fact, is the right way to develop dependable frameworks of standards and metrics that may inform incentives for the engineering of AI-based techniques.
Here’s a abstract of classes for present analysis observe for AI dangers:
- Prioritize mission-relevant dangers. Primarily based on the precise mission profile, establish and prioritize potential weaknesses and vulnerabilities. Do that as early as doable within the course of, ideally earlier than techniques engineering is initiated. That is analogous to the Division of Protection technique of mission assurance.
- Establish risk-related objectives. For these dangers deemed related, establish objectives for the system together with related system-level measures.
- Assemble the toolkit of technical measures and mitigations. For those self same dangers, establish technical measures, potential mitigations, and related practices and instruments. Monitor the event of rising technical capabilities.
- Regulate top-level operational and engineering decisions. For the upper precedence dangers, establish changes to first-order operational and engineering decisions that might result in probably threat reductions. This may embody adapting operational workflow designs to restrict potential penalties, for instance by elevating human roles or lowering assault floor on the degree of workflows. It might additionally embody adapting system architectures to scale back inside assault surfaces and to constrain the influence of weaknesses in embedded ML capabilities.
- Establish strategies to evaluate weaknesses and vulnerabilities. The place direct measures are missing, surrogates have to be employed. These strategies might vary from use of NIST-playbook-style checklists to adoption of practices reminiscent of DevSecOps for AI. It might additionally embody semi-direct evaluations on the degree of specs and designs analogous to Frequent Standards.
- Search for aligned attributes. Search optimistic alignments of threat mitigations with probably unrelated attributes that supply higher measures. For instance, productiveness and different measurable incentives can drive adoption of practices favorable to discount of sure classes of dangers. Within the context of AI dangers, this might embody use of design patterns for resilience in technical architectures as a strategy to localize any adversarial results of ML weaknesses.
The subsequent submit on this collection examines the potential advantages of trying past the purely neural-network fashions in direction of approaches that hyperlink neural-network fashions with symbolic strategies. Put merely, the objective of those hybridizations is to realize a type of hybrid vigor that mixes the heuristic and linguistic virtuosity of contemporary neural networks with the verifiable trustworthiness attribute of many symbolic approaches.