Smart technology is already all around us. Whether we’re fully aware of it or not, it tracks our digital footprint every step of the way. Whether it’s location tracking, personalized ads, or even keyboard word suggestions, there’s always an algorithm standing behind it. We experience its influence in our lives often without even realizing it’s there.
We feed the machines based on our knowledge, experience, and perceptions – and there’s nothing wrong with that. But as humans, we’re also naturally loaded with cognitive imperfections, influencing our daily lives without us even noticing it. Hence it’s not surprising that collecting data for machine-learning input often results in a considerably partial outcome.
It’s the subconscious influences of cognitive biases that create interference in machine-learning progress. Artificial intelligence (AI) can simply overcome cognitive biases, but only with appropriate resources, mindset, and workforce behind the wheel. Let’s explore the core of human cognitive biases and examine those that play an essential role in human-machine interactions.
What Is Human Cognitive Bias?
Cognitive biases are unconscious errors in thinking that arise from factors related to imperfections of human cognitive functions such as memory, attention, or perception.
These biases might be described as the brain’s efforts to simplify the incredibly complex and vast amount of data that we receive and process throughout the day. Not to overload itself, the brain uses clever but selective optimization that results in merging received information into shorter pieces, often leaving out potentially relevant details.
Those unconscious processes have been carefully carved throughout the years by the environment surrounding you to influence our daily lives. Although biases are hard to recognize and fight, there are still ways to overcome them and adopt new patterns of thinking that mitigate those adverse but convenient effects of cognitive imperfections.
If that’s the case, and we can train our brains towards truly objective logic, there’s no doubt we can dispose of this human interference while assembling input for AI algorithms. For example, when both diversity and inclusivity are essential factors defining the quality of the workplace, we can learn from human cognitive imperfections and carve them into state-of-the-art technology solutions.
There are many human cognitive biases, such as the similar-to-me effect, confirmation bias, stereotyping, halo effect, or fundamental contribution error. But which ones mentioned above can AI conquer today? Let’s go through each and learn what they stand for.
Stereotyping is defined as overly generalized beliefs about a particular group of people that can be out of our control. We tend to omit a person’s individual unique traits by stereotyping, expecting to look, behave, or act in certain ways.
Nowadays, overcoming the stereotype bias plays a crucial role, especially when it comes to building a diverse and respectful workplace. With the world as your office and emerging remote work possibilities, understanding people of various cultures, gender, ethnicity, and socio-economic backgrounds is critical.
What would be a stereotype input for AI algorithms? An example of this would be showing the computer executive board images full of men representatives or introducing a database that will make it assume that the vast majority of women are nurses or teachers.
Over time and with many images, the computer will have learned to recognize similar ones and classify them according to past patterns. We need to keep all machine-learning processes diverse but essentially on the data collection stage to answer this problem.
“Don’t judge a book by its cover” is a commonly known saying; unfortunately, it is still valid and applicable both in our personal and professional lives. It’s called the “Halo effect” and is defined as a relatively permanent judgment of a newly met person, based exquisitely on first impression.
This error in a judgment reflects individual preferences, prejudices, and social perception, meaning that when interviewing a job candidate with whom we share common interests, for example, we tend to overvalue the skill-set required for that particular position.
In the workplace, hiring specialists face the biggest risk of falling for this type of bias. It’s them who handle the first contact with candidates for each role, creating an idea about them that often goes beyond what’s objectively true. Here’s where AI comes in: Whether it’s the beginning, middle, or the end of the conversation, AI stays constant towards all traits characteristic for the person it interacts with.
When it comes to AI-powered technology, it’s always a mutual collaboration between a human and a machine. Therefore, if we ensure judgment-free input, what AI can offer in return is bias-free output.
Even if we consider ourselves open and tolerant individuals that praise diversity, we still tend to surround ourselves with those similar to ourselves. This psychological phenomenon is called the “Similar-to-me effect,” and it’s a cognitive bias that explains our tendency to prefer people that look, think, or act like us. At first, we might find it normal and harmless, but it becomes a significant problem once it results in discrimination or favoritism.
Research shows that this bias is particularly widespread among onboarding specialists. The similar-to-me effect refers to situations when the interviewer or employer favors and selects a person they primarily identify with. Consequently, it leads to unfairness when applied to hiring practices, workplace promotions, and tolerance towards otherness.
There are also three dimensions of similarity that have the most crucial impact on the selection of employees: similar biographical factors, ethnical characteristics, and attitudinal traits. For AI, all of these can be eradicated through well-defined data inputs.
When well taken care of, AI is able to make this bias disappear effortlessly, as it’s the smart technology that is free from imperfect human cognition interference. It’s the lack of this vulnerability that makes it non-judgemental and objective.
Overcoming the Bias
The latter tends to get tired and looks for the most accessible ways out of many situations. That is when cognitive biases come into play, clouding our judgment.
As an emerging technology of our times, one of AI’s biggest challenges is overcoming the biases we pass on to it. After all, we can’t blame the machine if the data it uses to learn is flawed. To overcome further bias interference, we need to focus on accurate and thorough data collection, proper model training followed by tuning, and frequent AI validation.
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