5 Ways to Sustainably Strengthen Trust in AI with AI TRiSM
AI TRiSM helps address the risks associated with the use of AI that lead to a lack of trust. ChatGPT is currently generating discussions regarding the sustainable use of trustworthy AI, as many questions about the transparency and explainability of the AI model remain unanswered.
According to a Deloitte study, the four biggest concerns of customers when using AI are bias, insufficient accountability, unexpected behavior, and excessive complexity.
- 17. October. 2024
Patrick Ratheiser
CEO & Founder
As one of the top trends for 2023 defined by Gartner, AI TRiSM establishes methods and solutions based on five pillars, which are also implemented by Leftshift One with its AI platform AIOS: Explainability, ModelOps, Data Anomaly Detection, Protection Against Adversarial Attacks, and Data Privacy. Additionally, the virtual machines used can be swapped out based on changing requirements and needed resources (for example, to replace them with more cost-effective options).
What concerns hinder the use of AI in companies due to a lack of trust?
What does the Chatbot ChatGPT from OpenAI have to do with AI TRiSM? Since its public release in November 2022, the chatbot has been a hot topic. It seemingly has an answer to every question, providing a user experience that feels as human-like as possible. However, looking behind the facade reveals the weaknesses of the system. For instance, ChatGPT fabricates explanations for non-existent physical phenomena when researching for a doctoral thesis.
The increasing presence of AI, especially in the form of assisting chatbots, raises questions about trust in the technology. A Deloitte survey highlights the four biggest concerns customers still have when using AI today:
- Existing Biases: Some customers remain biased against the technology, believing it is not yet mature.
- Insufficient Accountability: Customers are concerned about a lack of human oversight over AI systems.
- Unexpected Behavior: Customers fear that AI systems may “act out” and produce unpredictable results.
- High Complexity: Many customers do not understand how AI works and fear the unknown behind the algorithms.
Thus, artificial intelligence necessitates new forms of trust as well as risk and security management that conventional regulations do not adequately address. With AI TRiSM, organizations can be protected while significantly improving business outcomes through the use of AI.
What is AI TRiSM, and how can it help increase trust in AI?
AI TRiSM (Trust, Risk, and Security Management) is recognized by Gartner as one of the top trends for 2023 and includes various methods and solutions aimed at enhancing the trustworthiness of AI applications. This encompasses governance, fairness, reliability, efficiency, security, and data privacy of AI models. The five main pillars of AI TRiSM are explainability, ModelOps, data anomaly detection, resistance to adversarial attacks, and data privacy.
How does Leftshift One implement AI TRiSM to create sustainable value for AI deployment?
Leftshift One provides value along the five pillars of AI TRiSM with its AI platform, AIOS (Artificial Intelligence Operating System), also focusing on sustainability:
1. Explainability
To address the growing complexity of AI models, AIOS incorporates functionalities that provide explanatory details. Strengths and weaknesses, as well as the probable behavior of the model, are articulated in a way tailored to the respective stakeholders. Through traceability mechanisms, the accuracy, fairness, accountability, stability, and transparency of AI algorithms are ensured. Data and processes involved in decision-making are documented according to the highest standards. Users interacting with the AI are aware of its potential limitations and are informed about possible constraints. Additionally, there is accountability for AI systems and their outcomes. It’s important to note that, in some cases, there may be a trade-off between improving explainability and enhancing accuracy.
2. ModelOps
AIOS guarantees rapid and cost-optimized deployment of AI models with seamless integration into new and existing infrastructures through industry-specific standards. End-to-end governance and complete lifecycle management of all AI applications are enabled alongside a high degree of availability and reliability. Especially in cloud operations, deployment is highly cost-efficient. Furthermore, there is monitoring regarding performance and utilization. At Leftshift One, a quality management system, incorporating Legal & Compliance, ensures continuous improvement of existing processes. Human oversight mechanisms are in place to respect human autonomy. Particularly for safety-critical applications, there is always auditability in the form of assessment reports.
3. Data Anomaly Detection
Monitoring data in production for possible bias as well as input and process errors is fundamental for achieving optimal performance and protecting organizations from attacks. Incorrect data—suffering from bias, incompleteness, or poor quality—can alter the behavior of AI systems. It’s essential to eliminate any inaccuracies or errors before training an AI model. Leftshift One has developed guidelines and frameworks for Machine Learners to validate the functionalities of AIOS’s AI services. Relevant tests are conducted during the analysis of AI deployment, and datasets are documented. This approach allows for the detection of anomalies before the models are executed, covering both custom-developed AI applications and those acquired from partners.
4. Resistance to Adversarial Attacks
Discovering and intercepting attacks on AI systems requires new techniques to prevent financial or data-related harm to organizations. Initially, Leftshift One considers unintended applications of the AI system and adversarial attackers. Appropriate measures are then implemented to mitigate vulnerabilities and prevent misuse, ensuring technical robustness. AIOS is safeguarded with a security concept against adversarial attacks, as no external inputs are allowed.
5. Data Protection
The potential risks of AI are directly linked to data protection. Both internal actors, such as data scientists, and external partner organizations have access to sensitive data when it is unprotected. At Leftshift One, end-to-end encryption of data is guaranteed, allowing AI models to decrypt and work with this data. For highly sensitive data, there is also the option to store it either on-premises directly at the customer’s site or alternatively in a data center in Austria. In addition to ensuring privacy, data management mechanisms are employed to legitimate access to the data and maintain data integrity.
How can you reconcile artificial intelligence and ethics with Leftshift One?
With AIOS, Leftshift One provides a lightweight operating system for various AI applications. Utilizing AI TRiSM methods and solutions, it is explainable, transparent, and adheres to ethical standards. These are documented in a dedicated Ethics Policy and monitored by a company-wide Ethical Review Board. Furthermore, sustainability is given significant attention, as resource-efficient models are deployed.
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