This article discusses the growing importance of analyzing uncertainty and bias when using AI and machine learning. The current application of these topics is uneven, with some papers discussing them carefully while others neglect to do so. This variation can be attributed to differences in vocabulary usage across observational data, models and domains as well as a lack of consistent multi-divisional policies regarding their use.

In order to ensure reliable results from AI/ML applications, we must establish consistent multi-divisional policies for analyzing uncertainties and biases in machine learning and the world is looking to NASA’s Science Mission Directorate (SMD) as a source of correctness in an uncertain and new subject.

Uncertainty and bias are important considerations when working with AI and Machine Learning. Uncertainty is the degree of variability or lack of certainty in the model’s predictions, while bias is a systematic error in the model that skews the model’s output in favor of certain outcomes. In order to improve the reliability of AI and Machine Learning, it is important to establish consistent multi-divisional policies for analyzing uncertainties and biases in scientific machine learning research across NASA’s Science Mission Directorate (SMD). This includes understanding the language associated with AI contexts, properly validating output against observational data, and considering bias when making decisions about instrument selection.

Uncertainty and bias considerations are both important for understanding results from a scientific point of view but also essential for ensuring credibility in applied science applications which rely on reliable measurements.

The ethics of AI is an increasingly important topic of discussion as AI technology continues to advance. The ethical implications of AI depend on the specific technology being used and the context in which it is used. Broadly speaking, ethical considerations include issues such as privacy, autonomy, and fairness.

What is Uncertainty in the context of AI and Machine Learning?

“Uncertainty is defined to be the quantification of the spread in observed and/or measured properties. This includes aleatoric uncertainty (statistical uncertainty, which can be reduced through more observations) and epistemic uncertainty (systematic uncertainty).”  – NASA Science Mission Directorate

In more common terms, uncertainty in the context of AI and Machine Learning is the degree of variability or lack of certainty in the model’s predictions. It occurs when a model is unable to accurately predict or classify data due to a lack of understanding of the underlying data or the model’s parameters.

Uncertainty is an important concept in AI and Machine Learning because it can be used to measure the accuracy of models and inform decisions about how to improve them.

What is Bias in the context of AI and machine learning?

“Bias is defined to be the systematic difference between the actual value and the value determined by some estimator. Specifically, strategies to deal with bias and uncertainty fall under the large umbrella of uncertainty quantification (UQ).” – NASA Science Mission Directorate (PDF available online)

Generally speaking, within the context of AI and machine learning, bias is a systematic error in the model that skews the model’s output in favor of certain outcomes. This can be caused by a variety of factors, such as data that has not been properly cleaned or labeled, an algorithm that is not accurately capturing the complexity of the data, or a model that is not programmed to accurately recognize certain patterns or classes.

Bias can lead to inaccurate predictions and have serious implications in areas such as healthcare, finance and law enforcement.

Key challenges of Uncertainty and Bias

Addressing the key challenges of uncertainty and bias, NASA SMD observations suggest establishing a consistent model and vocabulary to avoid negative connotations and to allow for multi-domain conversations and re-use.

They propose a consistent training experience to perform analysis of uncertainty and bias in observational data and model output, taking into consideration the size of data sets, non-linear or chaotic situations, unknown unknowns, understanding when bias matters, and the difference between addressing known and unknown bias.

Solutions to these challenges include creating a common vocabulary for multi-domain conversations and re-use that avoids negative connotations about uncertainty and bias, and collecting existing work on assessing uncertainty and bias in a way that can help the NASA SMD research and engineering communities.

Establishing Consistent Multi-Divisional Policies for Analyzing Uncertainties and Biases in Scientific Machine Learning Research Across NASA’s Science Mission Directorate (SMD)

The scientific machine learning community is increasingly recognizing the importance of analyzing uncertainties and biases in their work. Although these ideas have been derived from experimental physics or statistics, they need to be adapted for use across NASA’s Science Mission Directorate (SMD). 

Unfortunately, at present there are various inconsistencies with how it is applied – some papers discuss them thoroughly while others do not mention them at all. Furthermore, different observational/modeling data has different vocabularies associated with it which can lead to confusion over understanding uncertainty and bias considerations.

AI/ML Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD)

As a result of these discrepancies, establishing consistent multi-divisional policies about such matters could benefit research outcomes significantly as well as improve public confidence in the accuracy of science results. 

When designing an experiment to gain insight into a natural phenomenon or physical process, the errors arising from differences between the physical state and reported values must be controlled.

These errors can come in two forms – stochastic (random) and systematic (non-random). To accurately explain the phenomenon, these sources of error need to be identified and compensated for. Machine learning techniques also play an important role in producing accurate models as part of this experimental design. 

The practice of uncertainty quantification is unevenly applied across multiple disciplines due to widely varying definitions of terms used when characterizing data quality. There is often a language barrier preventing organizations from readily adapting new methods, making it necessary that mathematical jargon related to AI contexts are understood by scientists first before being implemented.

When validating output against observational data, consideration for bias plays a key role in understanding any discrepancies seen between model predictions and actual results as well as appropriately choosing instruments based on precision needed versus resources available.

Final word on Uncertainty and Bias

AI and Machine Learning are becoming increasingly important tools for scientific research, business, and development. Establishing consistent multi-divisional policies for analyzing uncertainties and biases within this context is essential for reliable measurements from applied science applications relying on correct AI/ML inferences moving forward.

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