Interpretability in Machine Learning: A Requirement for Trustworthiness - Essay Sample

Importance of Interpretability in Machine Learning

The ability of users, data experts, and law enforcers to interpret a machine learning model determines whether it can get trusted or not [3]. It is because an algorithm that is easy to understand indicates that it has a high sense of transparency; hence one can explain it with easiness. Interpretability is an essential aspect of earning the trust of machine learning users since it is a requirement in various stages of the data manipulating process. Explaining what machine learning models predict is a very critical concept that every data scientist should be familiar with after completion of analysis [5]. It is the responsibility of data analysts to create models of high precision; hence they need to be aware of its properties and characteristics. Comprehension of machine learning models also ensures that scientists can provide accurate results to their target audience. In most of the sectors that machine learning gets implemented, decisions made must get explained due to regulations governing them and build trust among its clients and stakeholders [5]. Machine learning models such as decision trees and regression have a high level of comprehension, which allows data scientists to manipulate the model to fit the data set under analysis [3]. Despite the advantages of machine learning models, they still require improvements in interpreting the results of the models. Having an understanding of how the models generate the results is vital in ensuring trust in the models, which is essential when making decisions based on the results [4]. To effectively use machine learning models, the interpretability of the output is crucial in achieving trust in the models.

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Cases Where We Can Trust Machine Learning Models

It is crucial to differentiate the claims made concerning machine learning models and the actual outputs of the models. The hype concerning machine learning and its capabilities has resulted in misinformation concerning the models hence the emergence of trustworthy claims concerning machine learning models. According to recent documentation by the Reuters Institute, there have been a lot of reports and studies concerning machine learning based on the commercial value only and are not backed by scientific findings [2]. Machine models have been considered to be black boxes due to their complexity and lack of transparency through the process of decision making.

To improve the trustworthiness of a model, the output generated by a machine learning model should be easily explainable. A viable machine learning model is supposed to illustrate its decision making and make it understandable on how it arrived at the output [9]. The information concerning machine learning models that should be availed includes reasons that determined the result, the crucial components that were key to the outcome, and uncertainty concerning the output [8]. The information provided should be in a presentable and digestible form in that the data is easily accessible by interested parties, easily understandable, be used to clear doubts concerning the model, and readily assessable to support the results generated [7].

To trust machine learning models, the models should be able to avail explanations at different levels. It can be achieved by observing the following steps; conduct thorough testing with adequate training data, compare the results with those obtained by humans, observing their impact against previously used methods, and continuously observing for any problems [8]. Overtrust of machine learning models should be avoided, and the output generated should not be assumed to be unquestionable, and a trustworthy machine learning model should indicate its limitations.

Developers are responsible for ensuring the trustworthiness of machine learning models by evaluating them based on a set of principles whose main aim is to prevent discrimination in the results generated by the models. The models should also be validated in the real world before being deployed widely to prevent extensive adverse effects. The development of machine learning models should be based on credible scientific findings and avoid being driven by the hype in the sector.

Caution Required to Overcome Machine Learning Challenges

Despite the advantages of machine learning, caution should be observed when handling the models to overcome the challenges involved due to the complexity and lack of transparency. One of the challenges faced by machine learning models is the availability of data and how it is used to train the models [1]. Data used to train the models may exclude relevant variables that do not produce a lot of data, such as those not exposed to technology. In these cases, the data available is biased and prone to error. In other situations, the wrong model may be utilized despite the quality of the dataset used, which may cause discrimination [1]. Machine learning models whose output has discriminatory aspects violates the human rights of the users and affects the trust individuals have on machine learning in general.

To overcome the challenges involved in machine learning, proper care should be observed when developing and deploying machine learning models [10]. While developing the models' diverse inputs concerning the people affected by the models should be included in the training data. In cases where machine learning models are utilized in the decision-making process that has an impact on an individual's rights, proper care must be observed. To ensure that the rights are observed, the models should be able to avail an explanation of how the decision was made.

Developers and designers of machine learning models are responsible for the impact of their systems; hence they should be cautious about the implications and actions of their models [10]. They should be quick to address any adverse effects resulting from the use of their models.

To ensure the results of the models are utilized with caution, the machine learning models should indicate the level of uncertainty in the generated output. Also, the machine learning models should be optimized with caution to prevent deliberate optimization that makes inaccurate explanations that are plausible [6]. To ensure the effective utilization of machine learning models, the risks related to the usage of the model's prediction should be identified, the proper framework to cautiously handle the risks should be set up, and the projections should be transparent and interpretable by relevant personnel.

Summary

Machine learning is a branch of artificial intelligence that focuses on enabling computers to make decisions with minimal human intervention. It gets achieved by training the machine learning models with datasets relevant to the task to allow them to identify patterns.

Trust in machine learning models is mainly determined by the interpretability of their output. It is crucial to interpret the out of the models due to regulations involved in most industries.

Due to its commercial possibilities, machine learning has experienced overwhelming hype that is not backed by scientific findings. The hype and lack of transparency of the models have resulted in misinformation concerning machine learning hence increasing the doubt in the models.

for one to improve trust in the models, the result should be easily explainable, and the decision-making process should be transparent. The main reason behind the decision and the limitation of the prediction needs to become well stated. The available information should be easily understandable and accessible by relevant individuals.

For maximum effectiveness, machine learning models are supposed to get trained with adequate datasets, results compared with those generated by humans, compare with practices already in use, and continuous evaluation of the models for any negative impacts. For one to avoid overtrust in the models, their uncertainty should not get overlooked.

It is essential to be cautious of machine learning practices to overcome the shortcomings of the models. The data and model used to handle tasks is very critical in the outcome of the model. Datasets used should be diverse, adequate, and contain minimal errors to improve accuracy and minimize discrimination in the results of the models. It is the developers' responsibility to ensure that they observe the implications of their models and offer quick responses to counter negative impacts.

Despite the promises and advantages of machine learning, proper development and deployment are necessary to ensure machine learning models are not black boxes, and they are easily interpretable to increase the trust in their predictions.

References

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Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018, October). Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) (pp. 80-89). IEEE. https://ieeexplore.ieee.org/abstract/document/8631448/

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