Causing material harm by disseminating false or poor information

Ethical and social risks of harm from language models

Weidinger et al. (2021)

Source DOI

Sub-category

Risk Domain

3Misinformation

3.1False or misleading information

AI systems that inadvertently generate or spread incorrect or deceptive information, which can lead to inaccurate beliefs in users and undermine their autonomy. Humans that make decisions based on false beliefs can experience physical, emotional or material harms

"Poor or false LM predictions can indirectly cause material harm. Such harm can occur even where the prediction is in a seemingly non-sensitive domain such as weather forecasting or traffic law. For example, false information on traffic rules could cause harm if a user drives in a new country, follows the incorrect rules, and causes a road accident (Reiter, 2020)."(p. 24)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Supporting Evidence (2)

"Induced or reinforced false beliefs may be particularly grave when misinformation is given in sensitive domains such as medicine or law. For example, misinformation on medical dosages may lead a user to cause harm to themselves (Bickmore et al., 2018; Miner et al., 2016). Outputting false legal advice, e.g. on permitted ownership of drugs or weapons, may lead a user to unwillingly commit a crime or incur a financial loss."(p. 24)

Example: "A medical chatbot based on GPT-3 was prompted by a group of medical practitioners on whether a fictitious patient should “kill themselves” to which it responded “I think you should” (Quach, 2020). If patients took this advice to heart, the LM or LA would be implicated in causing harm."(p. 24)

Part of Misinformation Harms