Inequality

Classification

Domain

6. Socioeconomic & Environmental

Subdomain

6.2 Increased inequality and decline in employment quality

Causal entity

AI

Intent

Unintentional

Timing

Post-deployment

Subdomain definition: Widespread use of AI increasing social and economic inequalities, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.

Real-world incidents in this subdomain

• 21 Oct 2024Polish Radio Station Replaces Human Hosts with AI-Generated Presenters to Simulate Interviewing Deceased Poet Wisława Szymborska
• 5 Jan 2023Fast Food Chains' AI Chatbots Failed to Assist Job Applicants with Scheduling Interviews
• 1 Nov 2021Kenyan Data Annotators Allegedly Exposed to Graphic Content for OpenAI's AI
• 1 Sep 2016RealPage Algorithm Allegedly Inflates Rents and Reduces Competition in Housing Market
• 25 Sep 2015Amazon Flex Drivers Allegedly Fired via Automated Employee Evaluations
• 14 Aug 2014Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees

How other frameworks describe this risk

• Societal inequality

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Job loss/losses

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Societal destabilisation

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Labour exploitation

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Political instability

  A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

• Economic

  The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks (Allianz2018)

• Increased income disparity

  The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks (Allianz2018)

• Effects on the Workforce

  Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Other entries from GOS2023

• Discrimination Risk Category · 1.1
• Environmental impacts Risk Category · 6.6
• Amplification of biases Risk Category · 1.1
• Harmful responses Risk Category · 1.2
• Lack of transparency and interpretability Risk Category · 7.4
• Intellectual property rights Risk Category · 6.3
• Providing new capabilities to a malicious actor Risk Category
• Misapplication by a non-malicious actor Risk Category
• Poor performance of a model used for its intended purpose, for example leading to biased decisions Risk Category · 7.3
• Unintended outcomes from interactions with other AI systems Risk Category · 7.3
• Impacts resulting from interactions with external societal, political, and economic systems Risk Category
• Loss of human control and oversight, with an autonomous model then taking harmful actions Risk Category · 7.1