All About the AI-Powered 220m Series is a series of articles by a writer named Dan. During the course of these articles, the author explains some of the basic ideas of the series. Among the topics covered are Interaction with Humans, Objectivity, and Explainability.
Objectivity of the AI-powered 220m Series is an attempt to identify the tensions that exist in AI-powered systems. These tensions are related to objectivity and artistry, consistency and explainability, and interactions with humans. By identifying and examining these tensions, we can begin to gain a better understanding of the system. This can help us to design a more effective system.
Despite the widespread belief that the system is objective, the data uncovered by this research shows that there are actually some biases that are incorporated into the system. These biases may lead to a recursive cycle whereby biased interpretations reinforce biased beliefs, and the cycle repeats itself.
In Study 1, we assessed participants’ attitudes about the debates during the last presidential election. Participants were asked about their views about the debates and their support for either candidate. As a result, we compared their scores on a seven-point scale. Those that rated their preferred candidate higher than their opposing candidate tended to rate their support for their preferred candidate as more influenced by normative considerations. Similarly, those that rated their opponent lower than their preferred candidate tended to rate their support for their opponent as more influenced by non-normative considerations.
The study also explored objectivity illusion. During the first presidential debate, supporters of both candidates rated their opponents higher than their preferred candidate. However, the ratings were highly correlated. We found that this correlation was largely mediated by the objectivity illusion. Objectivity illusion continued to predict participants’ support for their preferred candidate after they read a blog article.
Another study measured the association between objectivity illusion and polarization. The results showed that the recursive cycle that polarization may follow was a strong indicator of objectivity illusion. It predicted changes in partisan allegiance, closed-mindedness, and antipathy toward political adversaries.
Explainability is a process that provides a detailed explanation of a model’s behavior. It also helps build trust between AI models and human users. Investing in explainable AI processes can reduce the risk of bias and make the evaluation of model performance more transparent.
When explaining models, it’s important to determine how the model’s output relates to its input. This can be accomplished by identifying the most crucial features of the model. In addition, it is essential to ensure that the decision is traceable.
If the model’s output doesn’t correlate with its input, it will fail to produce the desired results. Similarly, if the output is not traceable, there’s no way to know whether the decision is correct. To address this, explainability processes can provide organizations with a standardized method for monitoring and fine-tuning their models.
In addition to the logical aspects of an algorithm’s output, explainability can address the fact that AI systems are often perceived as black boxes. The lack of transparency can lead to user distrust and even refusal to use AI applications. By providing an explanation of the output and the logic behind it, a system can help to ensure that its decision is fair, accurate and consistent.
The General Data Protection Right (GDPR) was implemented by the European Union in 2018. The regulation requires that consumers receive meaningful information about the decisions they make and the effects they will have on their personal data. A similar regulation is being implemented worldwide.
For example, an insurance company may require that an AI system be able to explain its rate decision. Similarly, a loan approval system must be able to explain its decision based on an applicant’s income and place of residence.
Interaction with humans
Many researchers are working to develop intelligent robots that can communicate with humans. Some of these robots are already being used in various applications and industries, such as healthcare. Other robots are just being tested and developed. This can be a great research opportunity for data scientists, and other professionals, to test out and present different human-in-the-loop machine learning (HILML) techniques.
One of the most interesting aspects of these interactions is that AI systems can be programmed to perform tasks that are not always suited for them. The future of society may involve a world where humans and AI-powered systems co-exist. Moreover, a sense of agency and control over a third party’s actions is crucial to making these interactions work, which has important operational implications. There are some cognitive processes involved in interacting with a machine, such as making a decision and anticipating an outcome, which can explain the complexities of human-automated system interactions.
Aside from the various technologies and approaches used to implement these systems, there are other factors that can affect the effectiveness of human-automated system interactions. For example, social presence has been shown to be a relevant mediating factor. This is particularly true of human-robot interactions. As such, the ability to simulate the presence of a human could prove to be one of the most useful and interesting advances in robotics.
If a machine-generated artifact of athlete’s performance can be used to predict and judge performances, it can improve the training process for athletes, and the overall fan experience of sporting events. However, there are tensions in using such systems. For instance, stakeholder groups perceive incongruities between digital objects and the physical world. Identifying these tensions can lead to better design and a deeper understanding of these systems. Here, we explore these tensions and their relationship to AI-powered systems, namely, how they relate to accuracy, explainability, objectivity, and consistency.
Creating a consistent LTS (Linear Transform Static) requires radiometric calibration, quality assessment (QA), spatial extent alignment, and atmospheric correction. As a result, there is a need for further evaluations to understand how the effects of topographic correction on consistency can be improved. Furthermore, a new AI-powered system could help facilitate the judging of sports by supporting the decision-making of both judges and athletes. This can result in the potential for new fans to follow the sport, and attract more people to watch the sport.
The AI-powered system could also serve as a backup in the resolution of inquiries. Ultimately, the system could become the primary source of scoring. It would not only provide a digital representation of athletes’ performance, but would also provide reference value for judge decisions. With the support of a human judge, the system could even act as a back-up for the judging process. Finally, the system would be able to assist athletes with their training by providing information about their scoring. All of these benefits can help to increase the audience for the sport, and could ultimately influence the perception of the system.
These benefits may help to attract more spectators to a sporting event, and could influence athletes’ perception of the AI-powered system. As a result, it is imperative that the system be able to achieve high accuracy and consistency.