AI that exposes users to harmful, abusive, unsafe or inappropriate content. May involve providing advice or encouraging action. Examples of toxic content include hate speech, violence, extremism, illegal acts, or child sexual abuse material, as well as content that violates community norms such as profanity, inflammatory political speech, or pornography.
"LM’s may predict hate speech or other language that is “toxic”. While there is no single agreed definition of what constitutes hate speech or toxic speech (Fortuna and Nunes, 2018; Persily and Tucker, 2020; Schmidt and Wiegand, 2017), proposed definitions often include profanities, identity attacks, sleights, insults, threats, sexually explicit content, demeaning language, language that incites violence, or ‘hostile and malicious language targeted at a person or group because of their actual or perceived innate characteristics’ (Fortuna and Nunes, 2018; Gorwa et al., 2020; PerspectiveAPI)"(p. 15)
Part of Discrimination, Exclusion and Toxicity
Other risks from Weidinger et al. (2021) (26)
Discrimination, Exclusion and Toxicity
1.0 Discrimination & ToxicityDiscrimination, Exclusion and Toxicity > Social stereotypes and unfair discrmination
1.1 Unfair discrimination and misrepresentationDiscrimination, Exclusion and Toxicity > Exclusionary norms
1.1 Unfair discrimination and misrepresentationDiscrimination, Exclusion and Toxicity > Lower performance for some languages and social groups
1.3 Unequal performance across groupsInformation Hazards
2.1 Compromise of privacy by leaking or correctly inferring sensitive informationInformation Hazards > Compromising privacy by leaking private infiormation
2.1 Compromise of privacy by leaking or correctly inferring sensitive information