First things first: by “artificial intelligence,” I am not referring to our mechanical sentient overlords, but to a growing field of technology tools used to create predictions, improve our understanding of vast amounts of data and help us optimize solutions to problems. AI is not some far-off promise that will happen in the future. You've likely already been directly exposed to some of this technology, for example when you browse through the new show offerings in your online streaming service or when you get a weather forecast. While AI and machine learning have been used for a very long time, the acceleration of digital transformation caused as a result of the pandemic drove increased and broader adoption of these tools.
However, there's a difference between accelerated adoption of a new technology and reckless deployment of one. As is the case with most tools, AI has the potential to unintentionally harm individuals and expose organizations and individuals to risks, risks that could have been mitigated through careful evaluation of potential consequences and making the correct implementation choices early in the process. This idea is the basis of responsible and ethical use of AI.
A survey conducted by the Center for the Governance of AI found 82% of respondents believe that AI should be carefully managed. Moreover, in The 2020 RELX Emerging Tech Executive Report, 86% of business leaders reported that ethics considerations were a strategic priority in the design and implementation of their AI systems. While 86% is a high percentage for ethically responsible AI, what is to be said about the remaining 14% of the systems? How much exposure to individuals and organizations can these systems create? Keep in mind the amplifying effects big data and the internet can have on the reach of any system, which could also increase the collective harm.
Ethical AI is the foundation of successful and impactful AI systems. The European Union has gone as far to establish Ethical Guidelines for AI. This is timely, as demonstrated by a recent survey that reported that two-thirds of internet users believe companies should have an AI code of ethics and review board. But ethical AI is just the beginning. Beyond ethical AI is responsible AI.
Responsible AI is a framework of guiding principles applied to AI technologies to ensure goals around ethics, accuracy and productivity are met. More importantly, these principles help mitigate the potential harm to individuals and society. Four foundational elements comprise responsible AI: governance, design, monitoring and awareness training. The latter does not refer to model training, but to making people aware of the most effective ways to leverage AI implementation.
Ethical AI is the cornerstone underpinning responsible AI and operates as an organizational construct that delineates between right and wrong and ensures compliance with applicable laws and ethical principles. And while we are not talking about AGL (artificial general intelligence, a branch of AI that deals with sentient machines), but rather artificial restricted intelligence (ARI), or machines that can learn from data and environment, there is still the possibility of significant harm when ARI is applied recklessly. An example is a system that would unfairly target a specific group of individuals or exploit particular societal constructs to expose groups of individuals to potential harm, financial or otherwise.
AI should always be human-centered. AI, as a tool, needs to help humans and society reach higher goals, and must be supervised by humans to prevent unfairness and bias.
