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How to Overcome AI Distrust With Explainability

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How to Overcome AI Distrust With Explainability

This article explores how to overcome AI distrust with explainability.

· AI Zone ·
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AI has permeated our lives in nearly every aspect. We rely on it to get accurate search results, to enable conversational marketing, ad personalization, and even to suggest medical treatment.

In spite of headways in AI and it’s widespread use, there’s considerable distrust in it. Such distrust and unease arise from popular media representations of AI in movies where robots threaten to overthrow the human race. 

A lack of confidence also arises from the very real threat of losing one’s job to an intelligent machine. Understanding what makes AI problematic can be the first step to resolving its issues. Let’s look at some reasons why trust in AI has not completely developed. 

You might also be interested in: Are We Making AI Inefficient by Molding It to Human Thinking?

AI Is a Black Box

We can track the inputs that go into an algorithm and the outcomes it gives, but understanding the reasoning or process behind it is still a mystery. For now, AI can simply come up with correlations rather than causation. 

AI’s ability to process vast sets of data that are impossible by the human mind means that there’s a tradeoff between understanding how it works and getting accurate results. 

For example, IBM’s supercomputer Watson ended up being a failure in terms of its relevance in medical diagnostics. Doctors did not know the reasoning behind its recommendations and could not trust its judgment. 

The black box element of AI can be problematic especially when things go wrong. If an autonomous vehicle made a decision that leads to an accident, it’s not acceptable to say that we don’t know why such an event took place. Being unable to learn why an AI model acts as it does can be a strong reason to distrust it. 

AI Is Not Clued Into Context

In general, the ideal tool is one that does exactly as it’s told. But in reality, context plays an important role in making decisions. AI isn’t smart enough as yet to understand context or changes in information before delivering an output. 

In one example, Facebook’s memories feature forced a parent to relive the tragic loss of a child through its Memories feature. AI cannot comprehend the additional input needed in unique situations which makes it an unreliable substitute during crises.

It Can Reflect Human Biases

Many AI models are developed using input and data drawn from people’s behavior. This has resulted in AI tools carrying out functions that are discriminatory and biased. One of the ways in which AI has caused considerable controversy is through its discrimination against minorities and women. 

Another significant AI fail revolves around Microsoft’s chatterbot ‘Tay’ which was released via social media. AI models are developed from people’s real behaviors and insufficient data. It is not smart enough to distinguish between human prejudices and unethical behavior, resulting in significant controversy and damage. 

These issues with AI showcase why it isn’t completely reliable as yet and why the public distrusts it. The key to building trust is to explain why AI works as it does. To that end, Explainable AI has emerged as a technology to drive transparency. 

About Explainable AI

Explainable AI or XAI has risen as a class of systems that will help make AI less of a black box. It aims to explain an AI tool’s decision-making process and give an idea of how the system will behave in the future. Explainable AI focuses on creating accountability and interpretability. 

It’s important to note that XAI has to do with making design decisions so that offering clear explanations is part of the design itself. XAI is not an AI product that can automatically offer an explanation. 

XAI has to do with understanding the training data used to train AI, choosing the appropriate decision engine, and selecting algorithms that make it possible to explain decisions once they are made.

You can use tools and frameworks to help set up machine learning models that are interpretable and inclusive. The key to explainable AI is to start from the ground up. You need to be able to detect drift, bias, and other important gaps in your models. Using the right tool will also help you find these gaps in the input data. It’s also important to continuously test and evaluate your models to improve their performance and capture any indication of undesirable biases.

Conclusion

Building your model from an XAI approach creates transparency and from transparency, trust. However, it’s important to remember that the more ‘explainable’ an AI is, the less impressive is its functioning. It’s necessary to have a tradeoff between getting accurate data from vast sets of data and understanding an AI model’s decision.

In many ways, creating explainable AI is not always desirable. There are many things that we accept without having fully understood reasons for them. For a long time, no one understood how aspirin works. The placebo effect is also another well-known phenomenon that we accept without comprehending it. 

It’s important to strive for explainability without curbing AI’s potential to predict outcomes and make decisions. The direction that AI grows depends on the policies created by governments and institutions. The implementation of the GDPR indicates the growing importance placed on fairness and people’s rights. It also pushes for AI decisions to be transparent. It’s evident that XAI is growing, what’s left is to ensure that AI develops to benefit people and not harm their interests. 

Further Reading

Would You Trust an Automated Doctor?

Study: The AI Recommendations We Trust Most

Topics:
ai ,artificial intelligence ,ai distrust ,ai ethics ,human biases in ai ,ai bias ,bias in ai

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