인공신경망 편향을 대화형 반사실적 예제를 통해 비전문가에게 밝히다
📝 원문 정보
- Title: Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
- ArXiv ID: 2001.02271
- 발행일: 2020-01-13
- 저자: Chelsea M. Myers, Evan Freed, Luis Fernando Laris Pardo, Anushay Furqan, Sebastian Risi, Jichen Zhu
📝 초록 (Abstract)
AI 알고리즘은 편향으로부터 자유롭지 않습니다. 전통적으로 비전문가들은 자신의 삶에 영향을 미칠 수 있는 알고리즘 내에서 잠재적인 사회적 편향(예: 성별 편향)을 파악하는 데 거의 통제력을 갖추지 못했습니다. 본 논문에서는 일반적으로 사용되는 AI 방법인 신경망(NN)에 존재하는 편향을 드러내는 상호작용형 시각화 도구 CEB의 초안 설계를 제시합니다. CEB는 반사적 예제와 NN 의사결정 과정의 추상화를 결합하여 비전문가들이 편향을 감지할 수 있도록 합니다. 본 논문은 CEB의 설계와 AI, HCI, 사회과학 전문가들로 구성된 전문가 패널(n=6)의 초기 결과를 제시합니다.💡 논문 핵심 해설 (Deep Analysis)
This paper discusses the design of an interactive visualization tool called CEB (Counterfactual Examples for Bias) to help non-experts identify biases within neural network algorithms. The core issue addressed is that AI algorithms can introduce social biases, such as gender bias, which can significantly impact people's lives, but non-experts lack the tools and knowledge to uncover these biases.To solve this problem, CEB combines counterfactual examples with an abstraction of the decision-making process in neural networks. Counterfactuals are hypothetical scenarios where certain variables are altered to see how changes affect outcomes. By using these methods, users can understand how different inputs impact decisions made by AI algorithms and detect any biases that might be present.
The primary achievement is that initial findings from a panel of experts in AI, HCI (Human-Computer Interaction), and social sciences have positively evaluated the design of CEB as an effective tool for non-experts to uncover algorithmic biases. This suggests that such tools can empower users who are not specialists in machine learning or statistics to identify potential issues with AI systems.
The significance lies in enhancing fairness and transparency within AI algorithms, which helps build trust among end-users. By allowing non-experts to understand how AI makes decisions and identify any inherent biases, this tool contributes to more equitable outcomes in various applications of artificial intelligence.