Dangers Of The Black Box

What Makes Deep Learning Models Dangerous?

When talking about machine learning, deep learning, and artificial intelligence, people focus on the progress and amazing feats we could possibly achieve.

We live in an imperfect world, and the learning algorithms we design are not immune to these shortcomings.

 When to Use Them



<p class = Because of the nature of deep learning models, we should only use them if there is a clear, important reason to use them.

When choosing a solution to a problem, we have to address a number of factors, including

. speed accuracy training time interpretability maintenance enhancement size of the trained model

Before starting the prototype

and much more. Misconceptions

There are often misconceptions about AI and deep learning models and their implications for our world. Key Issues

Here are some key points to explain about machine learning models that can have potentially disturbing implications.

The machine learning algorithm can only be as good as it is trained on

There is also a history of the health industry that does not include enough women and people of color in the medical sciences.

Machine learning models do not understand the effect of a false negative versus false positive diagnostic (at least not like humans) .

Models may not have this “precautionary” attitude, especially if we do not design them with this in mind.

For many physicians and patients, the model is a black box. Stakeholders can only make decisions based on results, and predictions are not open to scrutiny.

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<p class = For concerns about the use of the learning model in the healthcare industry, this is just the tip of the iceberg, and we have yet to make an impact in other areas as well.

Employing black box technology becomes more of a problem when used without transparency in context.

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Models like these can go beyond mirroring existing inequalities. Personal Responsibility

This is not to say that we should not use machine learning models in ways that affect our daily lives. Interpretability and Transparency



<p class = It is also important that the models we develop are used in ways that are transparent to potential stakeholders. why the model is being used how their personal data is being used what personal data is being used

The last thing to consider is to make the internal workings of your model understandable to your users, also known as interpretive machine learning . In Conclusion

There is no doubt that machine learning can help us create waves of growth.

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