Introduction

AI systems already pose many significant existing risks including harmful malfunctions, discrimination, reducing social connection, invasions of privacy, and disinformation. Training and deploying AI systems can also involve copyright infringement and worker exploitation.

Individual malfunctions

AI systems can make mistakes if applied inappropriately. For example:

• A self-driving car in San Francisco collided with a pedestrian that was thrown into its path by a human driver. This was arguably not its fault - however, after initially stopping it then started moving again, dragging the injured pedestrian a further six meters along the road.
• A healthcare chatbot deployed in the UK was heavily criticized when it advised users potentially experiencing a heart attack not to get treatment.

Furthermore, the use of AI systems can make it harder to detect and address process issues. Outputs of computer systems are likely to be overly trusted. Additionally, because most AI models are effectively black boxes and AI systems are much more likely to be protected from court scrutiny than human processes, it can be hard to prove mistakes.

Discrimination

AI systems are trained with data that can reflect existing societal biases and problematic structures, resulting in systems that learn and amplify those biases.

AI systems that might not have inherent biases may still exacerbate discriminatory practices based on the societal context in which they are deployed. For example, unequal access to AI knowledge and skills could further entrench inequality.

Reducing social connection

Two effects can result in greater polarisation of opinions, reducing social connection:

• Recommender systems can result in filter bubbles, where users are only shown a particular type of content - for example, that which agrees with their views.
• Optimizing for engagement can result in amplifying divisive content, such as articles that promote moral outrage or misinformation, despite users not wanting this.

Invasions of privacy

AI systems are often used to process personal data in unexpected or undesirable ways. For example:

• Period-tracking apps are used by over a third of women in the UK and US and they sometimes share information about their users for targeted advertising. Even when the companies building AI recommender systems do not intend this, AIs could learn from this data that it's effective to target people when they're most vulnerable to specific types of advertising.