Short answer

Before launching new AI technologies, systematically identify and plan for potential ethical and social risks using a comprehensive framework.

Field
Innovation & Design
Source
2022 ACM Conference on Fairness, Accountability, and Transparency (2022)
Method
Literature review, expert consultation, and risk analysis.
Evidence
Strong effect

A structured taxonomy of potential harms can guide the responsible development and deployment of complex AI technologies. This innovation & design research insight is drawn from a 2022 study published in 2022 ACM Conference on Fairness, Accountability, and Transparency. Using Literature review, expert consultation, and risk analysis., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Before launching new AI technologies, systematically identify and plan for potential ethical and social risks using a comprehensive framework.

Study
Innovation & DesignHigh ImpactStrong effect

Proactive Risk Assessment Framework for Advanced AI Systems

A structured taxonomy of potential harms can guide the responsible development and deployment of complex AI technologies.

2022 ACM Conference on Fairness, Accountability, and Transparency · 2022

01

Key Findings

  • 01A structured taxonomy of six risk areas was developed for LMs.
  • 02Twenty-one specific ethical and social risks were identified and categorized.
  • 03Both observed and anticipated risks, along with mitigation strategies, were analyzed.
02

Application

Design takeaway

Before launching new AI technologies, systematically identify and plan for potential ethical and social risks using a comprehensive framework.

How to apply

Use the identified risk categories and specific risks as a checklist during the ideation and development phases of AI projects to ensure potential negative impacts are considered and addressed.

Project actions

  • 01When developing a new product, brainstorm potential negative consequences for users and society.
  • 02Categorize these potential problems to ensure a comprehensive review.
03

Method & Evidence

AimTo develop a comprehensive taxonomy of ethical and social risks associated with large-scale Language Models (LMs) and to analyze observed and anticipated risks, their causal mechanisms, evidence, and mitigation strategies.
MethodLiterature review, expert consultation, and risk analysis.
ProcedureResearchers identified and categorized twenty-one ethical and social risks posed by LMs into six distinct areas: Discrimination/Hate Speech/Exclusion, Information Hazards, Misinformation Harms, Malicious Uses, Human-Computer Interaction Harms, and Environmental/Socioeconomic Harms. For each risk, they discussed causal mechanisms, evidence, and mitigation approaches.
ContextDevelopment and deployment of advanced AI, specifically large-scale Language Models.

Variables

IVType of AI technology (e.g., Language Model).
DVObserved and anticipated ethical and social risks, and their mitigation strategies.
CVExpert knowledge, literature from relevant fields (computer science, linguistics, social sciences).
04

Strengths & Limitations

Strengths

  • +Comprehensive and structured approach to risk identification.
  • +Inclusion of both observed and anticipated risks.

Limitations

It can be challenging to foresee all possible risks, especially for novel technologies. Mitigation strategies may not always be effective or feasible.

Reliability & validity

The reliability of the taxonomy is supported by its derivation from expert knowledge and extensive literature review. Validity is enhanced by its comprehensive nature and the inclusion of both observed and anticipated risks, though the latter are inherently speculative.

Think critically

How might the 'Information Hazards' category apply to a non-AI technology, and what would be the differences in mitigation strategies?

05

Design Principles

"Anticipate and mitigate potential harms by developing a structured taxonomy of risks throughout the design and development lifecycle."

As AI systems become more sophisticated, anticipating and mitigating potential negative consequences is crucial for ethical design and societal acceptance. A comprehensive risk framework allows design teams to proactively address issues before they manifest, fostering trust and ensuring beneficial outcomes.

06

What This Means for Your Design

Think about all the ways a new technology, especially AI, could go wrong for people or society, and make a plan to prevent those bad things from happening.

How to use in your project

  • 1.Reference this taxonomy to justify the inclusion of a risk assessment section in your design project.
  • 2.Use the categories to structure your own analysis of potential negative impacts of your proposed solution.
07

Add to My Project

08

Quick Cite

Paragraph starter

A critical aspect of responsible design involves proactive risk assessment. Drawing upon frameworks such as the taxonomy of risks posed by language models (Weidinger et al., 2022), designers can systematically identify potential ethical and social harms, including discrimination, misinformation, and malicious use. This foresight enables the integration of mitigation strategies early in the design process, leading to more robust and ethically sound innovations.

09

Source

2022 ACM Conference on Fairness, Accountability, and Transparency

Taxonomy of Risks posed by Language Models

journal · 2022

View source

Questions About This Research

What does the research say about proactive risk assessment framework for advanced ai systems?
Before launching new AI technologies, systematically identify and plan for potential ethical and social risks using a comprehensive framework. Evidence: 2022 ACM Conference on Fairness, Accountability, and Transparency (2022).
Why does "Proactive Risk Assessment Framework for Advanced AI Systems" matter for design?
As AI systems become more sophisticated, anticipating and mitigating potential negative consequences is crucial for ethical design and societal acceptance. A comprehensive risk framework allows design teams to proactively address issues before they manifest, fostering trust and ensuring beneficial outcomes.
How can designers apply this research?
Before launching new AI technologies, systematically identify and plan for potential ethical and social risks using a comprehensive framework.
What were the main findings?
A structured taxonomy of six risk areas was developed for LMs.. Twenty-one specific ethical and social risks were identified and categorized.. Both observed and anticipated risks, along with mitigation strategies, were analyzed.
What research method was used?
Literature review, expert consultation, and risk analysis..
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2022 journal from 2022 ACM Conference on Fairness, Accountability, and Transparency.
What should I do differently in my next project?
Use the identified risk categories and specific risks as a checklist during the ideation and development phases of AI projects to ensure potential negative impacts are considered and addressed.
What are the limitations?
The taxonomy focuses on risks associated with Language Models and may not be directly transferable to all AI systems without adaptation. The analysis of anticipated risks is based on current understanding and may evolve.