How to Establish AI Ethics for Corporations

The risks that artificial intelligence (AI) may present, such as biases and possible loss of jobs due to automation, are becoming more and more clear to companies around the world. AI is simultaneously offering a wide range of observable advantages to businesses and society as a whole.

This is making it difficult for businesses to weigh the expenses of not implementing AI against the possible harm it could inflict. Many of the hazards involved with AI have ethical ramifications, but with clear guidelines, people and organizations can be given recommendations for ethical behaviors.

Corporations can negotiate the complicated world of ethical quandaries brought on by intelligent and autonomous technologies by using three developing strategies.

Ethical AI Organizations and the Principles of Ethical AI

While the risks associated with AI continue to rise, more public and commercial organizations are developing ethical guidelines to direct the creation and application of AI. In fact, a lot of people think that this practice is the most effective risk mitigation procedure. By establishing ethical standards, businesses may protect people's rights and liberties while also enhancing happiness and society as a whole. These concepts can be used by organizations to create norms and behaviors that can be managed.

More and more governmental and private groups, from tech firms to religious institutions, have published ethical standards to direct the creation and application of AI. Some have even called for the expansion of laws based on science fiction. The very first set of the Organization for Economic Co-operation and Development’s (OECD) ethical AI guidelines had been ratified by 42 nations as of May 2019, and more are anticipated to do the same.

Although there are many different ethical AI principles, there are certain commonalities. We have distilled over 200 ethical concepts from over 90 sets of ethical guidelines into nine fundamental ethical AI guidelines. We can visualize and identify the worries about AI that are reflected by tracking the principles by company, kind of organization, sector, and area, and how they differ among these groupings. These can be converted into norms and behaviors that can then be regulated by translating and contextualizing them.

These fundamental human rights, global pronouncements, agreements, or treaties, as well as an examination of current codes of ethics and conduct from several institutions, businesses, and initiatives, serve as the foundation for these basic ethical AI principles.

The nine fundamental principles can be reduced to general and epistemic principles, and they can serve as a starting point for evaluating and gauging an AI system’s ethical validity. These principles can be used to support the development of morally congruent AI solutions and culture. The landscape of these principles is intended to be used to compare and contrast the AI practices being deployed by enterprises.

The circumstances of knowledge that allow ethical AI organizations to assess if an AI system complies with an ethical standard are known as epistemic principles. They are indispensable in the study of AI ethics. They include guidelines for reliability and interpretability.

The general ethical AI principles, which is the second set of guidelines, outline how AI solutions ought to act when presented with moral choices or conundrums in a particular area of application. They constitute behavioral concepts that are applicable in numerous geographical and cultural contexts. Their guidelines touch on responsibility, data security, and human agency.

Different sectors of the economy and types of organizations, such as governments (like the US Department of Defense), businesses in the private sector, think tanks, associations, and consortiums, have a tendency to gravitate toward different guiding principles. While fairness is a top priority for all organizations, industries that deal with physical assets more frequently prioritize safety than those that deal with digital assets. While all corporations are required to abide by the law, few of them do so because they see it as just an ethical principle. Lawfulness and conformity, on the other hand, are most repeatedly detected in the principles published by governmental organizations, consortiums, and organizations.

Contextualizing the Principles of Ethical AI

The application of those ideas must take cultural differences into account. As a result, before applying the principles, they should be contextualized to represent the values, social mores, and behavioral norms of the society where AI solutions are used.

These “local behavioral drivers” can be divided into two groups: beyond compliance ethics, which is concerned with cultural and social norms, and compliance ethics, which is concerned with the regulations and laws in effect in a particular jurisdiction. Investigators should identify stakeholders, their interests, and any conflicts or tensions that may develop for them as a result of using that technology throughout the contextualization phase.

Why is contextualization crucial? Let’s think about justice. The various ways that fairness might be assessed regarding a specific person, decision, and situation have been the subject of much discussion. Simply declaring that systems must be “fair” does not give directions on how that fairness should be executed, and various authorities have varied ideas about what constitutes fairness. Key stakeholders would need to explain what fairness implies to them in order to contextualize.

Equal opportunity, for instance, can have several definitions in the recruiting context. The Equal Employment Opportunity Commission (EEOC) in the US mandates equality of opportunities regarding a selection rate (a specific measure that determines the proportion of applicants chosen for a job) with a threshold determined by the “Four-Fifth’s rule". The UK Equality Act 2010, an anti-discrimination policy, provides rules against discrimination, whether it is direct or indirect and whether it is produced by a human or automated decision-making mechanism.

Connecting Organizational Values and Human Rights to Ethical AI principles

It is crucial to link ethical principles to particular human rights in order to reduce regulatory ambiguity. Incorporating human rights principles into AI activities aids in establishing moral and legal responsibility as well as the advancement of AI that is human-centric and serves the greater good. This strategy complies with the ethical standards for AI established by the European Commission.

It is also important to integrate them with corporate values, current business ethics standards, and business objectives in order to clearly transform relevant ideas into particular norms that impact concrete design and governance to shape the creation and usage of AI systems. Organizations should develop effective AI ethics guidelines that include explicit accountability and practical monitoring techniques.