算法,专家,还是两者兼而有之? 评估特征选择方法对用户偏好和依赖的作用
Jaroslaw Kornowicz1, Kirsten Thommes1
1Faculty of Business Administration and Economics, Paderborn University, Paderborn, Germany.
PloS one
|March 7, 2025
概括
用户更喜欢人工智能中综合专家算法特征选择,但实际依赖性在各方法中是平等的,揭示了态度-行为差距. 域特异性影响AI决策支持偏好和使用.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 人工智能开发中的用户和专家集成是一个关键的研究领域.
- 人工智能作为决策支持的接受受各种因素的影响.
- 机器学习模型的透明度增加了特征选择的重要性.
研究的目的:
- 调查用户在AI开发中专家集成的偏好.
- 分析功能选择方法如何影响用户对AI决策支持的依赖.
- 确定人工智能系统使用中的态度-行为差距.
主要方法:
- 实验性研究比较基于算法,基于专家和组合的特征选择.
- 处理方法1:分析用户对特征选择方法的偏好.
- 治疗方法2:通过将用户分配到不同的方法来评估顾问的依赖.
主要成果:
- 用户更喜欢组合方法,其次是基于专家的,然后是基于算法的.
- 尽管有偏好,但用户在实际使用中同样依赖所有方法.
- 当用户可以选择他们喜欢的方法时,没有发现依赖度的显著差异.
结论:
- 人工智能决策支持系统中存在着显著的态度-行为差距.
- 了解认知过程对于有效的人与人工智能交互至关重要.
- 行为实验对于评估人工智能系统设计和用户依赖度至关重要.
相关概念视频
Sensitivity, Specificity, and Predicted Value
157
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
157
Reliability and Validity
12.6K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.6K
Factorial Design
13.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.0K
Goodness-of-Fit Test
3.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.3K
Types of Selection
39.9K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
39.9K
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K


