Humans rely more on algorithms than social influence as a task becomes more difficult

@article{Bogert2021HumansRM,
  title={Humans rely more on algorithms than social influence as a task becomes more difficult},
  author={Eric Bogert and Aaron Schecter and Richard Thomas Watson},
  journal={Scientific Reports},
  year={2021},
  volume={11}
}
Algorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three… 

Human preferences toward algorithmic advice in a word association task

Algorithms provide recommendations to human decision makers across a variety of task domains. For many problems, humans will rely on algorithmic advice to make their choices and at times will even

The Answer Bot Effect (ABE): A powerful new form of influence made possible by intelligent personal assistants and search engines

TLDR
ABE poses a serious threat to both democracy and human autonomy because it produces large shifts in opinions and voting preferences with little or no user awareness, it is an ephemeral form of influence that leaves no paper trail, and worldwide it is controlled almost exclusively by just four American tech companies.

Will We Trust What We Don't Understand? Impact of Model Interpretability and Outcome Feedback on Trust in AI

TLDR
Investigating whether improving AI interpretability and providing feedback on the outcome of AI decisions increases human trust in AI and human performance in AI-assisted prediction tasks finds that outcome feedback has a greater effect on human trust and performance.

Human-centered mechanism design with Democratic AI

Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement

Causal Framework of Artificial Autonomous Agent Responsibility

Recent empirical work on people's attributions of responsibility toward artificial autonomous agents (such as Artificial Intelligence agents or robots) has delivered mixed findings. The conflicting

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team

Connecting Multilayer Semantic Networks to Data Lakes: The Representation of Data Uncertainty and Quality

TLDR
The study explores the use of semantic networks to capture those multidisciplinary data relationships and demonstrates that use of a multilayered graph, employing other notions that do not expressly refer to the processes that generated the data, can capture the description of how uncertainty propagate between each of those concepts.

LBS as Vectors of Influence

TLDR
This essay outlines four main interaction characteristics that constitute LBS as a vector of influence and contextualizes them by mapping them onto a pragmatic framework of autonomy.

Application of Image Color Gamut Boundary Judgment Algorithm in Digital Media

  • Xuewei Li
  • Computer Science
    Wireless Communications and Mobile Computing
  • 2022
TLDR
The research results of the threshold color difference experiment show that the perceptible chromatic aberration threshold and acceptable color difference threshold of image color gamut observers for image color difference are higher than those of chroma and hue, which shows that the boundary judgment algorithm framework has certain advantages for the evaluation performance of color digital image threshold chromatic Aberration.

References

SHOWING 1-10 OF 62 REFERENCES

Task-Dependent Algorithm Aversion

TLDR
It is shown that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task, and increasing algorithms’ perceived affective human-likeness is effective at increasing the use of algorithm for subjective tasks.

Algorithm appreciation: People prefer algorithmic to human judgment

Abstract Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results

How social influence can undermine the wisdom of crowd effect

TLDR
This work demonstrates by experimental evidence that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks.

Making sense of recommendations

TLDR
It is found that recommender systems outperform humans, whether strangers, friends, or family, in a domain that affords humans many advantages: predicting which jokes people will find funny.

Network dynamics of social influence in the wisdom of crowds

TLDR
This work presents theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange and shows that the dynamics of group accuracy change with network structure.

Algorithm aversion: people erroneously avoid algorithms after seeing them err.

TLDR
It is shown that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster, and this phenomenon, which is called algorithm aversion, is costly, and it is important to understand its causes.

Quantifying machine influence over human forecasters

TLDR
This work presents a model that can be used to estimate the trust that humans assign to a machine, and uses forecasts made in the absence of machine models as prior beliefs to quantify the weights placed on the models.

Effects of Task Difficulty on Use of Advice

Although prior studies have found that people generally underweight advice from others, such discounting of advice is not universal. Two studies examined the impact of task difficulty on the use of

Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips

TLDR
The results of four studies suggest that when faced with difficult questions, people are primed to think about computers and that when people expect to have future access to information, they have lower rates of recall of the information itself and enhanced recall instead for where to access it.

Using Advice and Assessing Its Quality.

TLDR
It appears that using advice imposes a heavier processing load than assessing its quality and that this load can be lightened by including advisors who exhibit unusual behavior.
...