Trust and Ethics in AI

- Overview

Today, artificial intelligence (AI) is integrated into nearly every area of ​​life, from retail to government to healthcare, and its adoption is only expected to grow. Organizations are leveraging this technology to better serve customers, help employees be more productive, create jobs, grow their businesses, and more. 

However, while AI brings unprecedented opportunities to businesses and society, many are also aware of the risks of this widespread technology. 

To ensure that AI can reach its full potential, ethical principles need to be embedded into AI applications and processes to create trust-based systems. This approach is helping organizations reduce abuse and prepare for government regulations.

- The Future of Trustworthy AI Systems

With the advent of generative AI, the search experience has undergone a revolutionary transformation. Instead of presenting a list of links to numerous articles, users now receive direct answers that are synthesized from a vast pool of data. Engaging with this technology is akin to having a conversation with an exceptionally intelligent machine.

When it comes to using AI solutions for business applications, it's important to keep in mind that these solutions rely on non-deterministic algorithms. This means that they can't be completely trusted without proper safeguards in place during both their development and implementation.

The future of AI is not only to develop smarter algorithms, but also to ensure that these algorithms live in harmony with human society. By embracing responsible, explainable, and constitutional AI, we are paving the way for a future where AI systems are not just smart, but also ethical, transparent, and legal.

- Principles Guiding AI Ethics

Trust and ethics in AI is a broad topic that touches many levels. Some principles guiding the ethics of AI include:

• Fairness: AI systems should treat all employees fairly and never affect similarly situated employees or employee groups in different ways
• Inclusiveness: AI systems should empower everyone
• Transparency
• Accountability
• Privacy
• Security
• Reliability
• Impartiality: Do not create or act according to bias, thus safeguarding fairness and human dignity
• Societal and environmental well-being
• Technical robustness

Other principles relevant to AI ethics include:

• Diversity 
• Non-discrimination
• Control over one's data
• The ability to guide machines as they learn

- Ethics for Trustworthy AI

Trustworthy AI is based on seven technical requirements across three pillars, which should be met throughout the system's lifecycle: it should be (1) legal, (2) ethical, and (3) robust. Both come from a technical and social perspective. 

However, achieving truly trustworthy AI requires a broader view that includes the trustworthiness of all processes and actors in the system lifecycle and considers previous aspects from different perspectives. 

The more comprehensive vision considers four fundamental axes: global principles for the ethical use and development of AI-based systems, a philosophical perspective on AI ethics, a risk-based approach to AI regulation, and the aforementioned pillars and requirements. 

The seven requirements are analyzed from three perspectives (human agency and oversight; robustness and security; privacy and data governance; transparency; diversity, non-discrimination and equity; social and environmental well-being; and accountability):

• What are each of the requirements for AI.
• Why it is needed.
• How to implement each requirement in practice.

- EU and AI

Artificial intelligence (AI) can help find solutions to many of society’s problems. This can only be achieved if the technology is of high quality, and developed and used in ways that earns peoples’ trust. Therefore, an EU strategic framework based on EU values will give citizens the confidence to accept AI-based solutions, while encouraging businesses to develop and deploy them.

This is why the European Commission (EU) has proposed a set of actions to boost excellence in AI, and rules to ensure that the technology is trustworthy. 

The Regulation on a European Approach for Artificial Intelligence and the update of the Coordinated Plan on AI will guarantee the safety and fundamental rights of people and businesses, while strengthening investment and innovation across EU countries.  

- Explainable AI

Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services. With it, you can debug and improve model performance, and help others understand your models' behavior.

Explainable artificial intelligence (XAI) is an emerging field of research that brings transparency to highly complex and opaque machine learning (ML) models. In recent years, various techniques have been proposed to explain and understand machine learning models, which were previously widely considered black boxes (e.g., deep neural networks), and to validate their predictions. 

Surprisingly, the prediction strategies of these models sometimes prove to be somehow flawed and inconsistent with human intuition, for example, due to bias or spurious correlations in the training material. 

Recent efforts in the XAI community aim to move beyond merely identifying these flawed behaviors to integrating explanations into the training process to improve model efficiency, robustness, and generalization.

[New York City, New York - Civil Engineering Discoveries]

- Responsible AI

Responsible AI is a set of principles and regulations that guide how artificial intelligence (AI) is developed, deployed, and governed. It is also known as ethical or trustworthy AI.

The goal of responsible AI is to use AI in a safe, trustworthy, and ethical way. It can help reduce issues such as AI bias and increase transparency.

Some principles of responsible AI include:

• Fairness: AI systems should be built to avoid bias and discrimination.
• Transparency: AI systems should be understandable and explainable to both the people who make them and the people who are affected by them.
• Accountability: This means being held responsible for the effects of an AI system. It involves transparency, or sharing information about system behavior and organizational process. It also involves the ability to monitor, audit, and correct the system if it deviates from its intended purpose or causes harm.
• Empathy: This is one of the four foundations of responsible AI.

Other principles of responsible AI include:

• Privacy and Security
• Inclusive Collaboration

- Constitutional AI

Constitutional AI (CAI) is a method of training AI systems to be helpful, honest, and harmless by using a set of principles, or "constitution", to guide the AI's behavior. 

The constitution aims to ensure that AI systems operate within the bounds of constitutional principles, such as human rights, privacy protections, due process, and equality before the law. 

CAI addresses the legal, ethical, and societal implications of AI deployment. For example, the constitution can help AI systems avoid toxic or discriminatory outputs, and avoid helping a human engage in illegal or unethical activities. CAI can also enhance the credibility of AI systems by holding them accountable to a predefined constitutional standard. 

Undesirable behavior and harmful output are critical issues in the development and deployment of AI systems. CAI combines language models with human values ​​to produce harmless AI assistants. 

The concept of CAI is a fascinating way to address the challenge of creating AI models that are both helpful and harmless. By establishing a charter for an AI model, the process aims to provide a transparent and principled framework for guiding model behavior.