Introduction

The potential of artificial intelligence (AI) to cause harm as well as create a greater good has aroused great interest in the field of ethics and made it one of the most publicized areas, as well as an emerging topic of focus within a board’s oversight responsibilities.

Any information technology can be used for good or ill. But AI requires special attention because it increases the hazards of IT by enlarging both its scope and the scale of its impact. Moreover, with AI it is not just a matter of use once a product is deployed, as the use of an AI system is tightly linked to its design. Setting ethics standards for AI systems is necessary to avoid harm and increase well-being. Standards include the principles that AI systems and their developers, operators and managers should follow as they develop, operate and use AI systems and the data on which they depend. Standards also include the expectation of compliance with those principles by organizations and the law. AI ethics standards are necessary, but they are not enough: standards need to be taught, facilitated, absorbed, and possibly enforced in an organization’s culture and workflow.

This module provides tools and information to help boards oversee the setting of ethics standards, and the establishment of an ethics board. For content on who and how boards oversee ethics and other AI decisions, please review the Governance module.

AI’s ethics hazards

AI presents new hazards because it has capabilities lacked by previous IT systems. Counterfeiters can now create realistic fake videos, and companies can build chatbots that are nearly indistinguishable from human interactions. Autonomous systems could make life and death decisions without human oversight. In general, AI systems are able to make or recommend decisions to human operators, some with very high stakes.

Because AI systems learn, they are vulnerable to being trained by deliberately or inadvertently biased data, and to developing and following decision-making models with hidden biases. For example, AI systems have been accused of rejecting female candidates for jobs and recommending disproportionate criminal sentences and policing of minority groups due to such biases.[1]

Many AI techniques resist explainability, rendering it difficult to pinpoint the reasons for a specific decision, and also to assess if the decision path crossed an ethical line. Certain kinds of machine learning-based AI systems can make decisions without human oversight, based on complex patterns beyond human comprehension, and thus make decisions that cannot be predicted. Humans may not be capable of overriding such AI systems when they make instantaneous decisions in real time, as with autonomous vehicles and aircraft.[2]

How human–machine interactions take place affects the risk management process. Accuracy improves as AI systems learn. There is a risk, however, that decisions made solely by technology are accepted by users without proper human oversight.

The costs of ethical failure

Any failure to consider and address these issues and concerns could drive away clients, partners, and employees. Their trust in both the technology and the company can easily be diminished and lost if the company is not clear about its the policies around AI issues, and if the delivered technology acts as a “black box”.

Moreover, there may also be legal and regulatory consequences. Many AI application domains, such as financial systems, are heavily regulated in regard to fairness, diversity and inclusion. Enterprises need to ensure that compliance with these regulations is maintained when they inject AI systems into these domains. Companies should understand the origin of the data used to train AI models and what it is possible to do with it. Several regulations, such as the General Data Protection Regulation (GDPR) in Europe, put restrictions on how to treat data, especially personal data. Furthermore, accountability, redress and liability of AI systems are likely to feature in future litigation. Litigators and legal decision-makers will require clarity around such issues.