More and more companies are using various machine learning algorithms in their business. This technology is quite attractive, but it has certain risks because at the moment there is a lack of experience in using such algorithms.
The main risk is algorithmic bias, which can later lead the project in a different direction, which can be quite expensive for companies. Solving this problem at an early stage makes it possible to use machine learning more effectively.
The most popular and notable meta of machine learning is predictive modeling. It can analyze a large amount of various data and, based on this analysis, predict various outcomes fairly accurately. An excellent example is the determination of the credit rating of potential customers based on the history of previous loans. Check these developers, if need you project to be done properly.
Persistence of bias
The main factor in the introduction of machine learning is that such algorithms can analyze information, as well as make decisions much faster than a person, while significantly reducing financial costs. At the same time, these algorithms are not inclined to give additional weight to their experience (unlike people), which eliminates bias during decision-making.
However, machine learning algorithms, just like humans, are biased. This is due to several factors, namely:
– Algorithms are created by a person, which is why they will take into account the warnings of the creators,
– Lack of completeness of data, because sometimes it is simply impossible to specify all the necessary data, which can greatly distort the final results.
Eliminating bias in Machine Learning algorithms
To eliminate bias in algorithms and protect companies from their harmful effects, human control is necessary.
At the same time, 3 filters are of paramount importance at once, namely:
1) Correct use of machine learning algorithms
Managers should understand directly which areas and tasks these algorithms are suitable for and where they should be used, and where it is better to exclude the use of such models.
2) Proper use by users
It is necessary to know all the disadvantages of the algorithm, and also not to use such algorithms in questions where there may be invalid answers due to the presence of algorithmic bias.
3) Reducing the risk of systematic errors during data sampling
The most difficult and important part of the work needs to be paid additional attention to eliminate further system errors.
Proper use of machine learning algorithms
Machine learning algorithms are indispensable in case of work with big data, or in the area, like fraud detection and recognition of terrorists among the crowded demonstration., it is necessary to initially assess the profitability of using these algorithms.
There are various advantages of using computer vision, in many cases, which is described in sufficient detail here.
Removing bias from the sample
Certain tests can correct unwanted warnings that were made by people who had previously made decisions. It is necessary to adjust the solutions to exclude various kinds of warnings.
The main myth about machine learning is that such training does not require further human interaction, but this is far from the case with supervized learning. To ensure the efficient operation of the supervized algorithms, it is necessary to teach the model. During the training, ML specialists can eliminate or adjust the operation of the algorithm to improve its performance and validate the results with the special validation data set. This process cannot be performed automatically, but the optimization can be achieved, if we are speaking about semi-supervized learning, or self-superwized learning.
To protect against bias, it is also necessary to analyze the missing values. At the same time, initially, specialists can allow the algorithm to look beyond the limits of the existing limitations, which are determined by the initial data, and independently allow to find the correct answer.
Implementation: standards, verification, knowledge
The introduction of machine learning requires many decisions because the implementation of this project will require the coordinated work of the entire mechanism (organization). For successful implementation, it is extremely necessary to perform the following:
– Continuous development of knowledge
Invest in development, as well as track current trends and novelties in the field of machine learning to improve the performance of current models.
– Regular and professional inspections
To eliminate various errors and financial losses during the introduction of new data. At the same time, depending on the field of activity, it is necessary to establish adequate deadlines for checking the operation of existing algorithms.
– Availability of certain standards and compliance with them
At the same time, one should not believe in the myth of the perfection of artificial intelligence. For this model to work and bring real benefits, it needs constant human control. Only in this case it will be really useful and will be able to bring a lot of benefits and financial profit to almost any business.
