MIT AI Risk Repository · Risk Sub-Category · 17.04.02

Facilitating fraud, scames and more targeted manipulation

Category: Malicious Uses

Open the MIT Risk Navigator All entries from Weidinger2021

Description

"LM prediction can potentially be used to increase the effectiveness of crimes such as email scams, which can cause financial and psychological harm. While LMs may not reduce the cost of sending a scam email - the cost of sending mass emails is already low - they may make such scams more effective by generating more personalised and compelling text at scale, or by maintaining a conversation with a victim over multiple rounds of exchange."

From Ethical and social risks of harm from language models (Weidinger2021), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Domain: 4. Malicious actors
Subdomain: 4.3 Fraud, scams, and targeted manipulation
Causal entity: Human
Intent: Intentional
Timing: Post-deployment

Subdomain definition: Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.

Real-world incidents in this subdomain

Browse all incidents in this subdomain

How other frameworks describe this risk

Impersonation/identity theft
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
IP/copyright loss
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Dehumanisation/objectification
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Defamation/libel/slander
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Financial and business
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Cheating/plagiarism
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)
Cybersecurity
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)
Domain-Specific Misuses
Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

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