In our previous article, we discussed the importance of transparency, accountability, and fairness in ensuring the trustworthiness of artificial intelligence (AI) systems. We also outlined some specific actions that can be taken to make AI systems more transparent, accountable, and fair. In this article, we will explore some major measures needed to help AI achieve mass adoption and find out about the potential applications of aiProof - the world’s first blockchain-based system designed to enable immutable reporting of applicational AI performance, safety, and compliance.
Practical Approaches to AI's Mass Adoption
The term 'mass adoption' has recently been widely discussed in relation to artificial intelligence (AI). What does it truly mean? Is it achievable in the near future?
AI mass adoption is no longer a futuristic concept. Businesses worldwide are actively testing and implementing AI tools to enhance efficiency, while consumers have access to a variety of AI-powered applications. However, amidst this rapid growth, the critical question arises: How do we ensure the safety and reliability of the AI models we use daily?
Currently, there is a lack of public transparency around algorithm performance and data sourcing, posing potential risks to individual and business consumers. To enable true mass adoption, it is imperative to establish measures that address these challenges.
To foster mass adoption of AI, one factor that we cannot ignore is to ensure its trustworthy application. This entails transparent and accountable AI systems that respect human rights in their development and utilization. The solutions discussed in the first part of this series, such as bias mitigation, security, transparency, and accountability, are all important steps towards ensuring the trustworthiness of AI. However, they are not enough on their own. There also comes a need to develop practical approaches to promote AI's trustworthiness:
- Educating the public about AI: It is important to educate the public about AI, so that they understand how AI systems work and how they can be used. This can help to build trust in AI systems and to mitigate the risks associated with AI.
- Developing ethical guidelines for AI: Ethical guidelines can be developed to help ensure that AI systems are used in a responsible and ethical manner. These guidelines can be used to guide the development and use of AI systems, and to help to mitigate the risks associated with AI.
- Regulating AI: Governments can regulate AI systems to help ensure that they are used in a responsible and ethical manner. Regulations can be used to set standards for the development and use of AI systems, and to ensure that AI systems are accountable for their actions.
The Artificial Intelligence Act: A Big Step towards Trustworthy AI
The development of 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.
The European Union (EU) is proposing a regulation for artificial intelligence (AI) to ensure that this innovative technology is developed and used in a trustworthy manner. The AIA would classify AI systems according to the risks they pose to users, with more stringent regulations for systems that pose higher risks. The AIA would also set out requirements for developers and users of AI systems, such as the need to conduct risk assessments and to provide users with information about how AI systems work.
The AIA takes a risk-based approach to regulation. This means that the level of regulation that is applied to an AI system will depend on the level of risk that the system poses. For example, AI systems that are used in critical infrastructure, education, and employment will be subject to more stringent regulations than AI systems that are used for less critical purposes.
The AIA also includes a number of transparency requirements. This means that developers and users of AI systems must be able to explain how the systems work and how they make decisions. This will help to ensure that users are aware of the risks associated with AI systems, and it will also help to hold developers and users accountable for the consequences of their actions.
Though the AIA is still in its early stages of development and it is possible that the final version of the act will differ from the current proposal, it is a significant step forward in the regulation of AI, and it is likely to have a major impact on the development and use of AI in the EU, which is expected to be a model for other jurisdictions around the world.
aiProof: A Blockchain-based Solution for Trustworthy AI
What is aiProof?
aiProof is an innovative blockchain-based solution designed to enhance the trustworthiness of AI systems. Its focus lies in analyzing AI output through decentralized test cases, thereby providing the desired transparency for AI model performance. This approach is crucial because auditing AI algorithms, especially self-learning systems, can be incredibly challenging. By examining the output and performance alone, aiProof can quickly identify potentially harmful AI results and safeguard end-users.
While this tool will be incredibly valuable for end users to quickly evaluate the safety of AI models before purchasing, the power of aiProof lies in its ability to serve as a starting point for AI developers to diagnose and correct model training issues. This system can make the complicated task of detecting bias and harmful AI output far easier, expediting the process and providing more thorough coverage for maximum transparency. The results of aiProof monitoring may highlight previously overlooked flaws, prompting developers to investigate and even reinvest in algorithm development, thus ensuring continuous improvement.
The aiProof Solution: Addressing Risks and Ensuring Transparency in AI
While it's impossible to provide an exhaustive list of risks associated with AI, there are essential themes that AI-centric businesses must consider before engaging with these technologies. Addressing these risks to the best of their ability is crucial for building trust and ensuring the widespread adoption of AI systems.
- Explainability of Output
AI and machine learning involve approximation, leading to varying degrees of accuracy and margin of error. To build trust, it is essential to understand why an AI model produces a specific result. Accurate reporting allows end-users to make informed decisions, promoting transparency and best practices when using AI products. As self-learning systems emerge, it is crucial to acknowledge the experimental nature of AI technologies and provide thorough debriefing when outputs are not entirely explainable. For high-risk applications, implementing failsafes in production environments can prevent potential damages.
- Technical and Institutional Opacity
While some AI-based systems remain opaque and proprietary due to intellectual property concerns, disclosure requirements should not exclude these black box instances. Transparent reporting on intention, operations, security, and quality assurance testing results is crucial, ensuring accountability and trustworthiness.
- The Potential for Bias
Systematic fairness is of utmost importance as reliance on AI technologies grows. Bias can arise from training data and human contributions, affecting underserved and underprivileged populations. Encouraging diversity in personnel and training data can mitigate this risk, but a comprehensive framework for documenting and reporting bias-related risks is necessary.
Furthermore, AI-focused companies must have clear guidance on their product's target audience and work towards providing broad accessibility without bias towards race, gender, nationality, or socio-economic status.
- Privacy and Data Protections
Users should have a clear understanding of how their data is collected, stored, and used when exposed to centralized parties. Fair trade opportunities can arise where data is vital to a company's development, and compensation should be defined in such cases. A fresh approach to data privacy disclosures can help mitigate potential damages and legal repercussions arising from exploitation.
- Security + Adversarial Attacks
Just as smart contract audits are crucial for security in the blockchain space, AI products should disclose comprehensive security controls. This includes risk assessments, access controls, monitoring regimes, maintenance, continuous monitoring, response plans for exploits, and recovery strategies. By adhering to these security measures, end-users can trust in the robustness of AI systems and expect prompt responses to security threats.
The "Disclosure" Framework for AI + Blockchain Products:
These five categories constitute the "disclosure" framework for AI and blockchain products, ensuring transparency and informed engagement. AI-centric businesses should present this information in a digestible format to educate users and technologists, promoting trust and confidence in AI technologies.
Within the context of Oraichain's AI Oracle, disclosures can strengthen trust for individual software products published on the network. The AI Oracle system itself contributes to transparency and reporting, enabling real-time monitoring, preventative security measures, and expedited response in case of an exploit.
In summary, aiProof serves as a powerful solution to address these risks and ensure transparency in AI systems. By embracing aiProof, AI-centric businesses can build trust, foster mass adoption, and empower users and technologists to engage confidently with AI technologies.
aiProof’s real-world use cases
aiProof is being developed to be a valuable tool for businesses and organizations that use AI systems in the following fields:
- Financial services: aiProof can be used to ensure the reliability and transparency of credit scoring systems, fraud detection systems, and chatbots
- EdTech: aiProof could be used to track the performance of an AI-based grading system and personalized curriculums.
- Insurance: aiProof can be used to ensure the fairness of AI-powered pricing and fraud detection systems. For example, aiProof could be used to track the performance of AI-powered pricing systems and to identify any potential biases. This information could then be used to improve the systems and to ensure that all customers are being treated fairly.
- Smart Health: aiProof can help to ensure precision of fraud detection, claims processing and product recommendations.
- Advertising: aiProof can assist in studying targeting algorithms and reduce capital burn with better predictions.
- IP (Intellectual Property): aiProof aims to reduce risk of infringement and prevent potential misuse of generative AI models.
Note: As the domain-specific value of aiProof is being explored, we are also seeking to engage with AI developers, companies, and AI-integrated businesses to gain insight into their use case and testing requirements. If you have an interest in joining our case study initiative, we extend an invitation to connect with us.
In conclusion, as AI is advancing swiftly, finding utility in various domains, pushing the boundaries of innovation, the development of trustworthy AI is essential to ensure that AI systems are used in a responsible and ethical manner. As there are numerous challenges to ensuring trustworthiness in AI, but there are also a number of promising solutions being developed. The development of aiProof is a significant step forward in the development of trustworthy AI. Don’t forget to sign up for our newsletter to stay up-to-date on the latest news about Oraichain’s ecosystem.
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™️.