Reimagining AI: Ensuring Trust, Security, and Ethical Use

The birth of AI dates back to the 1950s when Alan Turing asked, "Can machines think?" Since then, 73 years have passed, and technological advancements have led to the development of unfathomably intelligent systems that can recreate everything from images and voices to emotions (deep fake).

These innovations have greatly benefited professionals in countless fields, be they data engineers, healthcare professionals, or finance personnel. However, this increased convergence of AI within our daily operations has also posed certain challenges and risks, and the assurance of reliable AI systems has become a growing concern nowadays.

What Is AI Safety?

AI safety is an interdisciplinary field of paramount importance for individuals concerning the design, development, and deployment of AI systems. It consists of mechanisms, philosophies, and technical solutions that guarantee the development of AI systems that don’t pose existential risks.

Like the GPT models, transformative AI can sometimes act unpredictably and even surprise users. For instance, every AI-related person is aware of the famous Bing Chat threatening incident. Similarly, in May 2010, several automatic trading algorithms triggered a stock market crash by ordering a large sell order of E-Mini S&P 500 futures contracts.

These examples indicate that if we allow human-independent systems into our fragile infrastructures, they may begin uncontrollable self-development and develop malicious goal-seeking behavior.

Different AI Safety Approaches To Build Trustworthy AI Systems

To use the technology for the benefit of society, it must be trusted, and the urgency of the fact further increases if the technology poses a human-like intelligence. Trust in AI is important to ensure investments, governmental support, and infrastructure migration.

That is why (NorwAI) the Norwegian Center for AI Innovation along with other renowned institutions, pays undivided attention to the topic and focuses on identifying trust needs of different industries. Here are the common areas where experts work to increase trust in AI.

Intensive Assessment And Validation

Frequent testing during different phases of the development process allows developers to rectify flaws and vulnerabilities. Several popular testing techniques, such as cross-validation, scenario testing, unit testing, domain knowledge, etc., help ensure that systems generalize accurately on unseen/new data.

Using statistical measures like the F1 score, AUC-ROC offers quantitative insights into the system’s efficacy. Furthermore, NIST is also working on guidelines to ensure safe AI models by creating test environments where risks and impacts of both individual user and collective behavior can be examined.

Transparency

Several powerful machine learning algorithms have complex working mechanisms and are often called black box algorithms (neural networks, ensemble methods, SVMs, etc). These models drive outputs or reach decisions without showing or explaining the behind-the-scenes mechanism, and describing such systems to users is difficult.

To build trust, the AI system should make transparent decisions. Transparency allows retractability if the system makes errors or develops a bias, preventing uncontrollable system learning. Another actionable approach is to use tools that explain the model in detail, including its performance benchmarks, ideal use cases, and limitations.

Fairness

Minding ethical considerations when designing and developing AI systems is vital to mitigate biases. A study by McKinsey, done in 2021, shows that about 45% of AI-related organizations prioritize ethical AI systems.

Fortunately, there are credible tools like IBM’s AIF 360, Fairlearn (a Python library), and Google’s Fairness Library to help ensure ethical procedures during AI system development. All these tools have distinctive characteristics, like AIF crafts comprehensive documentation to help with fairness assessment. Likewise, Fairlearn is helpful with its visualization capacity for interpreting fair results.

Accountability

The accountability of AI systems means examining the developed models using accountability frameworks and defining oversight entities. Companies deploying AI must increase their AI maturity score to make more accountable systems.

Studies reveal that about 63% of the organizations implementing AI systems are called "AI Experimenters" and have an AI maturity score of only 29%. These figures have to go up to indicate that organizations are genuinely prepared to employ AI and can implement ethics boards to remedy any issues caused by the system.

Privacy

Data is an organization’s or an individual’s most important asset. Data privacy is critical to building trust-gaining AI systems. An organization that openly explains how it uses user data will attract more customer confidence, while the opposite will erode customer trust.

AI organizations must try to align their practices with data protection laws like GDPR, CCPA, and HIPAA and follow approaches such as data encryption, privacy-preserving data mining, data anonymization, and federated learning. Moreover, organizations must follow a privacy-by-design framework when developing AI systems.

How Can AI Safety Ensure Security and Warrant Responsible Use?

AI safety guarantees that the developed AI systems perform the tasks the developers originally envisioned without inviting any unnecessary harm. Initially, the concept of AI safety and its principles regarding security assurance were largely theoretical. However, with the emergence of GenAI, the concepts of AI safety have taken on a dynamic and collaborative turn as AI risks can categorized extensively.

The most common risks include model poisoning, which happens due to corrupted training data, hallucination, and bias, which are inevitable if the model is poisoned. Similarly, prompt risks are also gaining prominence. The most evident risks include prompt injection, where a false prompt triggers wrong results from the model. Likewise, prompt DoS, exfiltration risks, data leakages, and other such threats that lead to non-regulatory compliance are common.

It is critical to note that all the damage happens during the training process, and if the developer can monitor the steps there, the resulting AI models will be beneficial. Hence, to ensure this, several organizations have portrayed their accountability models. Some of the most prominent ones include the NIST Risk Management Framework, the Open Standard for Responsible AI, Google’s Principles for Responsible AI, and the Hugging Face Principle for creating responsible AI systems.

However, a relatively new model named AI TRiSM (AI Trust, Risk, and Security Management) has been quite popular due to its transparency and security-guaranteeing features. According to Gartner, by 2026, businesses employing AI safety and security will see a 50% rise in user acceptance and improvement in business goals.

The Future of AI Safety

The creation of responsible AI is becoming a challenge as the means of AI corruption progress. Hence, to cope with the rising threats, a study field, "AI Safety," has been designated. The main goal of this discipline is to ensure the development of beneficial and correct vision-oriented AI models.

By deploying the methods and frameworks mentioned above, organizations can create responsible and accountable AI systems that can win user interest. However, technological advancement isn’t the only ingredient to creating safe AI. Factors like stakeholder engagement, behavioral and organizational change, government appreciation, and education initiatives are also detrimental.