The release of ChatGPT in 2022 marked a true inflection point for artificial intelligence. The abilities of OpenAI’s chatbot — from writing legal briefs to debugging code — opened a new constellation of possibilities for what AI can do and how it can be applied across almost all industries. ChatGPT and similar tools are built on foundation models, AI models that can be adapted to a wide range of downstream tasks. Foundation models are typically large-scale generative models, comprised of billions of parameters, that are trained on unlabeled data using self-supervision. This allows foundation models to quickly apply what they’ve learned in one context to another, making them highly adaptable and able to perform a wide variety of different tasks. Yet there are many potential issues and ethical concerns around foundation models that are commonly recognized in the tech industry, such as bias, generation of false content, lack of explainability, misuse, and societal impact. Many of these issues are relevant to AI in general but take on new urgency in light of the power and availability of foundation models.

While this topic garners a lot of public attention, many researchers are not concerned with the idea of AI surpassing human intelligence in the near or immediate future. This is also referred to as superintelligence, which Nick Bostrum defines as “any intellect that vastly outperforms the best human brains in practically every field, including scientific creativity, general wisdom, and social skills.” Despite the fact that Strong AI and superintelligence is not imminent in society, the idea of it raises some interesting questions as we consider the use of autonomous systems, like self-driving cars. It’s unrealistic to think that a driverless car would never get into a car accident, but who is responsible and liable under those circumstances? Should we still pursue autonomous vehicles, or do we limit the integration of this technology to create only semi-autonomous vehicles which promote safety among drivers? The jury is still out on this, but these are the types of ethical debates that are occurring as new, innovative AI technology develops.

While a lot of public perception around artificial intelligence centers around job loss, this concern should be probably reframed. With every disruptive, new technology, we see that the market demand for specific job roles shift. For example, when we look at the automotive industry, many manufacturers, like GM, are shifting to focus on electric vehicle production to align with green initiatives. The energy industry isn’t going away, but the source of energy is shifting from a fuel economy to an electric one. Artificial intelligence should be viewed in a similar manner, where artificial intelligence will shift the demand of jobs to other areas. There will need to be individuals to help manage these systems as data grows and changes every day. There will still need to be resources to address more complex problems within the industries that are most likely to be affected by job demand shifts, like customer service. The important aspect of artificial intelligence and its effect on the job market will be helping individuals transition to these new areas of market demand.

Privacy tends to be discussed in the context of data privacy, data protection and data security, and these concerns have allowed policymakers to make more strides here in recent years. For example, in 2016, GDPR legislation was created to protect the personal data of people in the European Union and European Economic Area, giving individuals more control of their data. In the United States, individual states are developing policies, such as the California Consumer Privacy Act (CCPA), which require businesses to inform consumers about the collection of their data. This recent legislation has forced companies to rethink how they store and use personally identifiable data (PII). As a result, investments within security have become an increasing priority for businesses as they seek to eliminate any vulnerabilities and opportunities for surveillance, hacking, and cyberattacks.

Instances of bias and discrimination across a number of intelligent systems have raised many ethical questions regarding the use of artificial intelligence. How can we safeguard against bias and discrimination when the training datasets can lend itself to bias? While companies typically have well-meaning intentions around their automation efforts, there can be unforeseen consequences of incorporating AI into hiring practices. In their effort to automate and simplify a process, Amazon unintentionally biased potential job candidates by gender for open technical roles, and they ultimately had to scrap the project. As events like these surface, Harvard Business Review (link resides outside ibm.com) has raised other pointed questions around the use of AI within hiring practices, such as what data should you be able to use when evaluating a candidate for a role.

Bias and discrimination aren’t limited to the human resources function either; it can be found in a number of applications from facial recognition software to social media algorithms.

As businesses become more aware of the risks with AI, they’ve also become more active this discussion around AI ethics and values. For example, last year IBM’s CEO Arvind Krishna shared that IBM has sunset its general purpose IBM facial recognition and analysis products, emphasizing that “IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency.”

There is no universal, overarching legislation that regulates AI practices, but many countries and states are working to develop and implement them locally. Some pieces of AI regulation are in place today, with many more forthcoming. To fill the gap, ethical frameworks have emerged as part of a collaboration between ethicists and researchers to govern the construction and distribution of AI models within society. However, at the moment, these only serve to guide, and research (link resides outside ibm.com) shows that the combination of distributed responsibility and lack of foresight into potential consequences isn’t necessarily conducive to preventing harm to society.

Governance is an organization’s act of overseeing the AI lifecycle through internal policies and processes, staff, and systems. Governance helps to ensure that AI systems are operating as an organization’s principles and values intend, as stakeholders expect, and as required by relevant regulation. A successful governance program will:

• define the roles and responsibilities of people working with AI.

• educate all people involved in the AI lifecycle about building AI in a responsible way.

• establish processes for building, managing, monitoring, and communicating about AI and AI risks.

• leverage tools to improve AI’s performance and trustworthiness throughout the AI lifecycle.

An AI Ethics Board is a particularly effective governance mechanism. At IBM, the AI Ethics Board is comprised of diverse leaders from across the business. It provides a centralized governance, review, and decision-making process for IBM ethics policies and practices. Learn more about IBM’s AI Ethics Board.

An organization’s approach to AI ethics can be guided by principles that can be applied to products, policies, processes, and practices throughout the organization to help enable trustworthy AI. These principles should be structured around and supported by focus areas, such as explainability or fairness, around which standards can be developed and practices can be aligned.

When AI is built with ethics at the core, it is capable of tremendous potential to impact society for good. We’ve started to see this in its integration into areas of healthcare, such as radiology. The conversation around AI ethics is also important to appropriately assess and mitigate possible risks related to AI’s uses, beginning the design phase.