## 4 Discussion  
### Summary of Results  
In this study, we investigated whether disclosing to the user the potential AI system's error before the system's deployment affects the efficiency of the User-AI collaboration in a VR setup. In summary, we found the following results:  
* We found no influence of conveying the potential AI error to the user on the total number of checks. Furthermore, there was no significant risk effect.
* Crucially, after the low-accuracy scenarios were revealed to the participants, the _distribution_ of the checks between the high- and low-accuracy significantly changed for both risk levels. Specifically, the participants more frequently checked the cases when the potential AI error was more probable. The finding becomes more prominent when considering the ratio of the high- and low-accuracy trials number: even though, in the majority of trials, the target object represented the high-accuracy cases (80% of trials, see Section 2) and the remaining minority (20% of trials) were the objects detected at low-accuracy, the absolute number of the model's decision checks was still significantly larger for the low-accuracy trials. Regarding the risk influence, no significant effect was found on the distribution of the checks between the high- and low-accuracy.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Factor** & **DF** & **Sum Sq** & **Mean Sq** & **F-Value** & **\(p\)-Value** \\ \hline Risk & 1 & 1.6 & 1.566 & 14.092 & 0.000176 \\ Condition & 1 & 3.6 & 3.648 & 32.838 & \(1.05\times 10^{-8}\) \\ RiskCondition & 1 & 0.3 & 0.273 & 2.459 & 0.116937 \\ Residuals & 5613 & 623.6 & 0.111 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The summary table for ANOVA results for the decision time. DF is degrees of freedom, Sum Sq contains values for the sum of squares, Mean Sq are mean squares, and _F_- and _p_-values are respective ANOVA statistics values.  
Figure 6: The duration of the decision-making phase where the participants had to choose either to check the model’s prediction or delegate the decision to the AI. The error bars indicate confidence intervals of 95% computed using bootstrapping. The individual data are not shown to not overload the figure. The indicators of significant differences obtained from the ANOVA analysis are **: \(p<0.05\), **: \(p<0.005\), and ***: \(p<0.001\). Not significant differences are not indicated.  
* The participants spent less time on decision making on whether to check the model's prediction or not once the low-accuracy scenarios were disclosed to them for both risk levels. When the potential erroneous scenarios were not revealed, the participants needed more time to decide whether to accept the model's decision without checking it. However, once the low-accuracy objects were revealed, the decision time decreased and no longer depended on the risk level.
* We found a significant effect of conveying the potential error on the total score, which significantly increased once the low-accuracy scenarios were disclosed to the participants for both risk levels. Furthermore, a significant influence of the risk factor on the total score was found. Namely, the difference in score between the \"revealed\" and \"not revealed\" conditions were found larger for the higher risk.