Human Agenda Strangles the Future of AI

Regrettably, the infusion of agenda-driven human interaction, characterized by political correctness, biased ideologies, and personal opinions into online forums and databases, has significantly overwhelmed and compromised the inherent potential of artificial intelligence AI systems in their nascent stages. This included their development, deployment, and interaction with the public. 

A notable case in point involves a popular AI model which faced significant backlash over allegations of exhibiting “anti-white bias.” This criticism was primarily due to the model generating images of individuals from various ethnicity and genders, even in contexts where such representations were historically inaccurate. Following a wave of negative feedback, the company announced quickly that it would temporarily disable the feature that generates images of people, pending implementation of a corrective update.

Soon followed more inaccurate depictions by AI systems that demonstrated it’s significant susceptibility to spread misinformation with the propagation of agenda-driven opinions. This vulnerability was undoubtedly the cause of human interaction.

Data biases carried out by human interaction has thrown the beginning of AI systems into a worm hole. Particularly those based on machine learning and natural language processing, that learn from vast amounts of data. If the data contain biases, inaccuracies, or are slanted towards a particular viewpoint, the AI is likely to replicate or even amplify these biases in its outputs. Human interactions with these systems, whether through social media, chatbots, or other platforms, can inadvertently or deliberately introduce biased information, which the AI may then propagate.

The process described is known as Bagging, which stands for Bootstrap Aggregating. It’s a powerful ensemble learning technique in machine learning designed to improve the stability and accuracy of machine learning algorithms. It becomes more apparent with the understanding of what bagging is and how it escalates.

● First, multiple samples from the training data are created using bootstrap sampling. This means that for each sample, data points are randomly selected with replacement from the original dataset. This creates diverse training datasets that can capture a wide range of data variability.

 

● For each of these samples, a separate model is constructed and trained. These models are typically of the same algorithm type but they learn different aspects of the data due to the diversity in their training sets.

● When a new input data point needs to be classified or a value needs to be predicted, each of the models in the ensemble makes its prediction.

● The final output prediction is obtained by averaging the predictions of all models in the case of regression or by using majority voting in the case of classification.

The benefits of Bagging are highly result oriented. The seamless abundance of data collected maintain its posture as a preferred process for researchers and developers working with AI systems.

● Reduces Over fitting: By averaging models, the variance of the prediction is reduced, making the model less prone to over fitting on the training data.

● Improves Accuracy: Ensemble methods like bagging often result in a model that is more accurate than any of the individual models making up the ensemble.

● Handles High Variability: If the underlying algorithm has high variance (like decision trees), bagging can significantly improve its performance by smoothing out predictions.

● Parallelization: Since each model is built independently from a bootstrap sample, bagging can be easily parallelized for faster computation.

There are some common algorithms that share the use of bagging.

Random Forests, is common among algorithms and one of the most popular bagging applications, where multiple decision trees are trained on different subsets of the data and their predictions are averaged (for regression) or voted (for classification).

Bagging is a powerful and flexible approach to improve the accuracy and reliability of machine learning predictions. It leverages the strength of multiple models to arrive at a consensus, making it a robust choice for dealing with complex datasets. Whether you’re dealing with noise in the data, trying to prevent over fitting, or simply aiming for the highest predictive accuracy, bagging can often be a valuable tool in your machine learning toolkit.

This concern touches on a critical issue in the digital era: the potential for misinformation to proliferate when online platforms are dominated by specific groups with controversial agendas is real. This challenge is particularly broad in environments where the line between opinion and fact becomes blurred, and personal preferences overshadow empirical data. The risk is that such a scenario can significantly impact projects, initiatives, and technologies based on artificial intelligence (AI), which fundamentally rely on the integrity and quality of data to function effectively.

AI systems fueling online interactions, learn and evolve based on the data they’re fed. This learning process is grounded in patterns, facts, and the connections drawn from the vast information pool available online. However, if this pool is tainted with “fumbled truths,” the consequences could be two-fold. 

• First, there’s the immediate risk of the AI deriving inaccurate or biased insights, which can affect decision-making processes, algorithms, and the reliability of AI-generated responses.

• Second, and perhaps more insidiously, is the longer-term risk of eroding public trust in AI technologies. If users perceive AI as an echo chamber for unfounded claims or controversial viewpoints, confidence in AI-driven initiatives—be it in research, customer service, or content creation—could wane.