This article aims to provide a comprehensive market view of AI ethics in the industry today. To learn more about IBM’s point of view, see our AI ethics page here.

Ethics is a set of moral principles which help us discern between right and wrong. AI ethics is a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes. Examples of AI ethics issues include data responsibility and privacy, fairness, explainability, robustness, transparency, environmental sustainability, inclusion, moral agency, value alignment, accountability, trust, and technology misuse.

With the emergence of big data, companies have increased their focus to drive automation and data-driven decision-making across their organizations. While the intention there is usually, if not always, to improve business outcomes, companies are experiencing unforeseen consequences in some of their AI applications, particularly due to poor upfront research design and biased datasets.

As instances of unfair outcomes have come to light, new guidelines have emerged, primarily from the research and data science communities, to address concerns around the ethics of AI. Leading companies in the field of AI have also taken a vested interest in shaping these guidelines, as they themselves have started to experience some of the consequences for failing to uphold ethical standards within their products. Lack of diligence in this area can result in reputational, regulatory and legal exposure, resulting in costly penalties. As with all technological advances, innovation tends to outpace government regulation in new, emerging fields. As the appropriate expertise develops within the government industry, we can expect more AI protocols for companies to follow, enabling them to avoid any infringements on human rights and civil liberties.