Artificial intelligence (AI) is rapidly becoming more pervasive in our lives. AI systems are now being used to make decisions in a wide range of domains, from healthcare to finance to transportation. As AI systems become more powerful, it is becoming increasingly important to ensure that they are trustworthy. In this article, we'll explore the significance of trustworthiness in AI, the obstacles to guaranteeing AI system trustworthiness, and the solutions that developers have embraced to improve AI design, aiming for a positive impact on human life.

What is Trustworthiness in AI?

Trustworthiness in AI refers to the ability of AI systems to be reliable, fair, and transparent. Trustworthy AI systems are those that can be relied upon to make accurate and unbiased decisions, and that can be explained to users in a way that they can understand. The International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) provides a definition of “trustworthiness” as the “ability to meet stakeholder expectations in a verifiable way… Characteristics of trustworthiness include, for instance, reliability, availability, resilience, security, privacy, safety, accountability, transparency, integrity, authenticity, quality, usability.”

Trustworthiness is important for AI systems because it is essential for ensuring that these systems are used in a responsible and ethical manner, and are likely to be used to make decisions that are harmful or discriminatory. This fosters user acceptance and adoption of AI technologies, ultimately accelerating their integration.

There are a number of ways in which AI systems can be untrustworthy. For example, AI systems can be biased, which can lead to unfair or discriminatory outcomes. AI systems can also be opaque, which means that it can be difficult to understand how they make decisions, which would make it difficult to be trusted.

There are also a number of benefits to using trustworthy AI systems. For example, trustworthy AI systems can help to improve decision-making, reduce bias, and increase fairness. They can also help to build trust between users and AI systems, which can lead to increased adoption of these technologies. Additionally, the output of trustworthy AI will be credible and not harmful, as it will be based on sound data and algorithms. This can help to ensure that AI systems are used in a responsible and ethical manner and that they do not cause harm to individuals or society.

Key Challenges to Trustworthiness in AI and How They Could Be Dealt With

The development of artificial intelligence (AI) is rapidly accelerating, and AI systems are now being used in a wide range of applications. As AI systems become more sophisticated, it is becoming increasingly important to ensure that they are trustworthy. Trustworthiness in AI refers to the ability of AI systems to be reliable, fair, and transparent. Trustworthy AI systems are those that can be relied upon to make accurate and unbiased decisions, and that can be explained to users in a way that they can understand.

However, there are a number of challenges to ensuring the trustworthiness of AI systems, ranging from bias, opacity, and security to intellectual ownership.

Bias

One of the key challenges to ensuring trustworthiness in AI is bias. AI systems can be biased, which means that they can make decisions that are unfair or discriminatory. This can happen if the data that is used to train AI systems is biased, or if the algorithms that are used by AI systems are biased.

For example, an AI system that is trained on a dataset of resumes that is mostly from men is likely to be biased against women. This is because the AI system will learn to associate certain characteristics with men, such as being more assertive or having more experience. As a result, the AI system is more likely to recommend men for jobs, even if women are equally qualified.

One of the main ways to mitigate bias in AI systems is identifying and removing biased data, which involves identifying data that is known to be biased or using statistical techniques to identify biased data. Once biased data has been identified, it can be removed from the dataset. This helps to ensure that AI systems are not trained on biased data, which can help to reduce bias in their decisions. Furthermore, a comprehensive framework for documenting and reporting bias-associated risks is also necessary.

Another way to mitigate bias is to train AI systems on more diverse datasets. This helps to ensure that AI systems are exposed to a wide range of experiences and perspectives, which can help to reduce bias.

Opacity

Another challenge to ensuring trustworthiness in AI is opacity. AI systems can be opaque, which means that it can be difficult to understand how they make decisions. This can make it difficult to trust them, and it can also make it difficult to identify and address bias in AI systems.

For example, an AI system that is used to make loan decisions may not provide specific metrics and factors that are considered during the process, neither for accepted nor rejected loans. This means that it may be difficult to understand how the AI system decides who to approve for a loan. This can make it difficult to trust the AI system, and it can also make it difficult to identify and address bias in the AI system.

When it comes to ensuring transparency in AI systems, a viable approach is granting users access to the data and algorithms utilized by these systems. By doing so, users can observe the data used for training and the algorithms that influence decision-making.

The second approach is to use techniques such as explainability and auditing. Explainability is the ability of an AI system to explain its decisions in a way that is understandable to users. Auditing is the process of inspecting AI systems to ensure that they are working as intended.

Transparency is not just about making information available to users. It is also about creating a culture of openness and accountability around AI development and use. This means being transparent about the goals of AI systems, the data that is used to train them, and the decisions that they make. It also means being open to feedback from users and being willing to address any concerns that they may have.

Security

AI systems can also be hacked or manipulated, which can lead to malicious or harmful outcomes. This is a particular concern for AI systems that are used in critical applications, such as healthcare or financial services.

One significant concern is data poisoning, where malicious actors deliberately manipulate training data to mislead AI models, leading to inaccurate or harmful decisions. Additionally, user data hacks pose a serious threat, as AI systems often rely on vast amounts of sensitive information, making them potential targets for cyberattacks. Unauthorized access to user data can lead to privacy breaches and identity theft. Moreover, AI algorithms can be vulnerable to adversarial attacks, where input data is subtly altered to cause misclassification or system malfunction. As AI continues to be integrated into various domains, it is crucial to prioritize robust security measures to protect both users and the integrity of these powerful systems.

To make AI systems more secure, these are some of the prevalent approaches:

Using security techniques such as encryption and authentication. Encryption is the process of scrambling data so that it cannot be read by unauthorized users. Authentication is the process of verifying the identity of a user or system.
Ensuring that AI systems are not connected to insecure networks. This means using secure networks that are protected from unauthorized access.
Keeping AI systems up to date with the latest security patches. This helps to protect AI systems from known security vulnerabilities.
Providing transparent security controls practices. This means users could easily understand the safety measures taken throughout the AI product’s lifecycle, ranging from risk assessment and risk elimination to recovery.

Data Privacy

The recent news of Samsung banning the use of generative AI tools like ChatGPT on its internal networks and company-owned devices highlights the critical issue of data privacy in the world of AI. Samsung's concerns stem from the fear that uploading sensitive information to these platforms represents a security risk. This incident underscores the importance of addressing data privacy challenges to ensure the trustworthiness of AI systems.

To tackle data privacy concerns and empower individuals, leveraging blockchain technology offers promising solutions. Non-custodial systems built on blockchain can grant users full control over their private information, allowing them to manage and share their data on their terms.

Advanced blockchain-based systems provide encrypted storage for sensitive data, safeguarding it from unauthorized access or misuse. Through the integration of blockchain technology, AI systems can create unchangeable evidence and timestamps for every data interaction. This guarantees transparency and responsibility, preserving a detailed record of data usage.

Accountability

It can be difficult to hold AI systems accountable for their actions. This is because AI systems are often complex and difficult to understand, and it can be difficult to trace their decisions back to the data and algorithms that were used to train them.

Ensuring accountability for AI systems is a daunting task due to their complex and opaque nature. Understanding the inner workings of these systems and tracing decisions back to the data and algorithms used for training can be challenging. Consequently, holding anyone responsible for negative outcomes becomes difficult. Is the builder of the AI model responsible for the inaccuracy or is the end user responsible for using the AI tool for decision making without a complete understanding of the risks?

For instance, in the medical field, an AI diagnostic system may misdiagnose a patient, but the intricate layers of neural networks make it hard to pinpoint the exact cause of the error. Similarly, in autonomous vehicles, determining accountability for accidents caused by AI-driven cars becomes complicated due to the vast training data and intricate algorithms. These challenges highlight the importance of transparency and explainability in AI to build trust and address accountability concerns.

There are various ways to make AI systems more accountable:

Developing clear rules and regulations about how AI systems can be used. These rules and regulations should specify the responsibilities of AI developers, users, and regulators.
Ensuring that there are mechanisms in place to hold AI systems accountable for their actions. This could include creating independent oversight bodies, or requiring AI systems to be audited on a regular basis.
Promoting transparency in AI development and use. This means making information about AI systems available to the public, so that people can understand how AI systems work and how they make decisions.

Accountability should not be all about punishing AI systems for their mistakes. It is also about learning from mistakes and preventing them from happening again. This means having a system in place to investigate AI failures and to identify ways to improve AI systems.

Intellectual Ownership

The advent of Generative AI has brought forth a concerning challenge for artists and their intellectual property. Data crawlers scour the internet, taking artworks and talent without proper recognition or compensation. These collected data are later utilized to train AI models, which can produce "unique" artworks that are put up for sale without compensating the original creators. Additionally, AI algorithms can mimic artists' distinctive styles, blurring the lines between genuine creativity and machine-generated replicas.

Blockchain technology emerges as a promising solution to address these issues. By registering intellectual property on the blockchain, artists can establish a public and immutable record of ownership. This blockchain-based registry ensures transparency in asset ownership and tracks the exchange and usage of AI training data. This way, artists can protect their contributions to AI development and receive fair recognition and rewards for their creative endeavors.

Conclusion

Although the points discussed above represent only a fraction of the pivotal hurdles in guaranteeing AI's trustworthiness, it may be enough to prove that transparency, accountability, and fairness are essential for ensuring that AI systems are trustworthy. While there have been many advances in AI, there is still more that can be done to make AI systems more transparent, accountable, and fair. For example, the renowned ChatGPT and its new competitor - Google's Bard - are both large language models that have been trained on massive datasets of text and code. However, these models are rather opaque and difficult to understand for newbies, which can raise concerns about their trustworthiness. By taking steps to make AI systems more transparent, accountable, and fair, we can build trust in AI and ensure that it is used in a rightful and ethical way.

With almost three years of experience as a pioneer in this domain of AI and Blockchain, Oraichain Labs is aiming for a bright future of Trustworthy AI ecosystem in coexistence with a thriving layer-1 blockchain infrastructure. In the upcoming article of this two-part series, join us in exploring the path towards AI mass adoption and the revolutionary aiProof, a blockchain-based solution for trust in AI systems.

About Oraichain

Oraichain provides multidimensional trustworthy proofs of AI and enables secure integration with Web3. With AI as the cornerstone, Oraichain has developed many essential and innovative products and services including AI Oracle, DINO Hub with DINO Center, AI Marketplace & Data Marketplace, fully on-chain VRF, AI-based NFT generation & copyright protection, Royalty Protocol, AI-powered Yield Aggregator Platform, Cosmwasm IDE, and many more. Oraichain is the world’s first Layer 1 of AI oracle and Trustworthy Proofs™️.

Building Trust in AI (Part 1): The Role of Transparency, Accountability, and Fairness