As an emerging technology, AI faces challenges and will continue to face its fair share. On the one hand, consumers are cautious about adopting new technology.

Envisioning a world where humans are displaced by AI-powered machines may be troubling to some late adopters.

On the other hand, companies express disappointment that AI has not yet proven itself to be the magic bullet that will streamline every business process and lead to bountiful profits. Here the human side of AI is being exploited.

In many ways, AI has been its own worst enemy.
AI is operating under a mysterious set of rules only its authors, and IT, understand, and science has faced an identity crisis. Like a gangly teen, AI is still trying to find its more important purpose in the general market.

medical field
Everyone from medical researchers to web developers is trying to find ways to make the best use of AI. Bringing AI into your own hands, adapting it to commercial purposes, and leveraging its power may prove more difficult. However, some believe that the process of AI has not been really useful until now.

Gartner predicts that by 2022, 85 percent of AI projects will deliver “wrong” results “due to bias in the data, algorithms, or teams responsible for managing them.” While that number appears extreme, it points to the real struggle the enterprise has to navigate the uncharted waters of AI integration into a broader business strategy.

Some business owners think it’s too cool to say you’re into AI – but harnessing the power of AI to serve business-specific needs is another matter entirely.

AI and Consumers
Consumers themselves are not doing much better. In a recent survey by Blue Fountain Media, a digital marketing firm, nearly half of consumers claimed they didn’t know what AI is and how it was being deployed around them.

These findings echo others found in similar surveys that indicate there is an overall consumer mistrust of AI and a misunderstanding of how it is currently being deployed.

Siri and Alexa
It’s somewhat ironic that a consumer might distrust AI – yet, if you ask them whether they like their voice assistants like Alexa or Google Home, many will say it’s a huge help in their daily lives. Is. Are individuals not aware that voice assistance is enabled by AI? All smart home uses are also AI-connected.

So where does all this information leave us? Where are the next voice-assistant types of breakthroughs that will find the elusive mix where both consumers and businesses will see benefits?

AI and Human Elements
There may be a secret in bringing the human element to AI. By acting in a way that is contrary to consumer wants and needs, an AI platform can meet those needs.

The key to this new, mindful form of AI is to focus on being aware and objective about the intentions and feelings we develop through any artificial intelligence experience. The goal is to identify and clarify the main pain-points to be resolved and the positive value that reducing those pain points will drive.

For example, an organization struggling to create meaningful consumer engagement can identify the core issues that are driving the state of stagnation and then decide how to use AI as a means to unlock it. Can be used.

Mindful AI Approach.
Using AI to bring attention to mental health issues, researchers at MIT were able to develop an effective AI platform for accurate diagnosis of depression. Their neural network model analyzes raw text and audio data from natural interactions with a patient – ​​which can detect words and intonations of speech that may indicate depression.

The AI ​​platform is called context-free modelling, and is the first step in detecting mental illness using only data obtained during casual conversations.

Implementing careful AI practices can help identify and mitigate systemic biases inherent in raw data by ensuring that AI systems built on those data sources do not carry those biases.

garbage in garbage out.
Basically, it comes down to the basic garbage in/garbage out preaching, where the data sets for a given AI project are only as useful as the way they are acquired and interpreted.

In their recent book Data Feminism, authors D’Ignazio and Klein make the main point that data never “speaks for itself.” There are always humans and institutions interpreting and speaking for data, introducing biases and agendas that can affect AI outcomes.

give up prejudice.
Obtaining “clean” data – which has not been trained and tuned by biases – still remains a major challenge for building AI platforms that provide accurate results. At least at this stage, human activity plays a major role in the AI ​​production process.

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