Ethical AI and How We Can Attain It

What is Ethical AI and How Can We Attain It?

Ethical Artificial Intelligence (AI) refers to the moral principles and techniques that inform the development and responsible use of AI technology. As AI becomes an integral part of products and services in organizations, there is a growing need to develop codes of ethics or AI value platforms that define the role of AI and provide guidance for ethical decision-making.

The Need for AI Ethics

The concept of AI ethics can be traced back to science fiction writer Isaac Asimov, who foresaw the potential dangers of autonomous AI agents. Asimov's "Three Laws of Robotics" served as a code of ethics for limiting the risks associated with AI. Since then, rapid advancements in AI have spurred groups of experts to develop safeguards for protecting against the risks posed by AI to humans. One such group is a nonprofit institute founded by MIT cosmologist Max Tegmark, Skype co-founder Jaan Tallinn, and DeepMind research scientist Victoria Krakovna. This institute has worked with AI researchers, developers, and scholars from various disciplines to create 23 guidelines referred to as the "Asilomar AI Principles."

Types of Ethical Considerations in AI

Explainability

One ethical challenge in using AI technology is explainability. When AI systems go awry, teams need to be able to trace the complex chain of algorithmic systems and data processes to find out what went wrong. Organizations using AI must be able to explain the source of data and how resulting data algorithms were trained.

Chicago AI has engineered and implemented a world class observability platform, that allows us to monitor and audit our AI platform in real-time. It is using this insight into our artificial intelligence services, that we are able to spot, correct, and often times prevent, any ethical issues that might arrise during the course of normal business for our products, like our customer service automation plugin Newton AI.

Responsibility

Society still struggles with determining responsibility for decisions made by AI systems that can have catastrophic consequences, including loss of capital, health, or even life. Determining responsibility for the consequences of AI-based decisions requires a process that includes lawyers, regulators, and citizens.

Fairness

Ensuring fairness in data sets involving personally identifiable information is extremely important to ensure that there are no biases in terms of race, gender, or ethnicity. Misuse of AI algorithms may be used for purposes they were not created for, and scenarios like these need to be analyzed and addressed during the design stage to minimize risks and introduce safety measures.

The Importance of an Ethical AI Framework

An ethical AI framework is important because it shines a light on the risks and benefits of AI tools and establishes guidelines for their responsible use. A system of moral tenets and techniques for using AI responsibly requires the industry and interested parties to examine major social issues and ultimately question what makes us human.

Ethical challenges in AI are faced by enterprises across various industries. These challenges include explainability, responsibility, fairness, transparency, accountability, sustainability, awareness and literacy, privacy protection, multi-stakeholder governance, and non-discrimination.

Addressing Ethical Challenges

To address these ethical challenges in the use of AI technology, enterprises must take a proactive approach that addresses three key areas: policy development, education, and technology.

Policy Development

Policy development includes developing an appropriate framework that drives standardization and establishing regulations. Efforts like the Asilomar AI Principles are essential starting points for this conversation. Several efforts are already spinning around policy development in Europe, the U.S., and elsewhere.

Education

Education is crucial at all levels within an organization. Executives, data scientists, front-line employees, and even consumers need to understand the policies and key considerations regarding potential negative impacts of unethical AI use. This education should also cover topics such as fake data and the potential negative repercussions of oversharing or adverse automations.

Educating the Public

Chicago AI believes that when used appropriately, Artificial Intelligence technologies and LLMs(like OpenAI's ChatGPT) are game changers and help level the playing field for those that are disadvantaged, by providing tools for human-beings to augment and level up their creative and technical skills. It is Chicago AI's goal to help people realize the power of Generative AI, through education initiatives, like our regular Instagram campaigns, that teach people that artificial intelligence isn't something they should fear, but rather something that they should embrace to augment and upgrade their existing skills. Chicago AI also enables users to leverage the potential of artificial intelligence technology like ChatGPT, by providing the most cost effective, accessible and affordable artificial intelligence products on the market, ranging in capabilities from customer service automation(Newton AI) and product support, to content creation(Hemingway AI) and analytics.

Technology

Technology executives need to architect AI systems that automatically detect fake data or unethical behavior. This requires not only looking at a company's own AI but also vetting suppliers and partners for potential malicious use of AI. Openness, transparency, and trust are necessary in the development of AI infrastructure to ensure its responsible use.

Leading by Example

Chicago AI has implemented multiple initiatives aimed at holding the organization to a high standard of ethics, including the certification of all training data, used to train and operate services like Chicago AI's customer service automation solution, Newton AI, as ethically and accurately sourced. Chicago AI hopes that by raising the bar and holding their organization and themselves accountable, that other artificial intelligence technology startup firms(and their executives) will follow Chicago AI's lead.

Conclusion

Ethical AI is crucial in ensuring that AI technology is developed and utilized responsibly. It requires the establishment of guidelines, policies, and frameworks that address the ethical challenges posed by AI. This includes promoting fairness, transparency, accountability, inclusiveness, and sustainability in the development and deployment of AI systems.

The adoption of ethical AI practices across businesses has become imperative as AI continues to play a larger role in our lives. Responsible use of AI not only ensures positive impacts on consumers and employees but also helps businesses retain talent and operate smoothly.

Our Efforts

AI Ethics(and Ethics in general) was(and still are) an immediate concern from the inception of Chicago AI's AI platform. Established in one of Chicago AI's early initiatives, Chicago AI verifies and certifies that all training data used for and by the Chicago AI platform, and it's tools like Newton AI and Hemingway AI, is ethically sourced and screened for accuracy as well as any potentially harmful, hateful, biased, sensitive, fake or innacurate content or data. Throughout our AI platform, we have built out and implemented many guardrails, filters and alerts to ensure that our platform, and it's components, such as Newton AI, are only capable of being used for causes of good nature, like customer service automation, that have a positive impact on both small businesses and their customers.

As we move forward in this rapidly evolving and monumentally important field, it is imperative to continue exploring ways to safeguard against the misuse of AI for unethical and unsavory purposes. By developing rules, procedures and technologies that promote responsible AI, as well as setting a positive and ethical example for other artificial intelligence technology firm leaders to follow, we can create an environment where the benefits and equity provided by AI are maximized while minimizing any potential risks, biases or inequalities.